PRIVACY POLICY

Privacy Policy of
www.upstreamml.com

www.upstreamml.com collects some Personal Data from its Users.
Users may be subject to different protection standards and broader standards may therefore apply to some. In order to learn more about the protection criteria, Users can refer to the applicability section.

Owner and Data Controller

Energy Machine Learning Symposium, 8584 Katy Freeway, Suite 400, Houston, Texas 77024

Owner contact email:admin@upstreamml.com

Types of Data collected

Among the types of Personal Data that www.upstreamml.com collects, by itself or through third parties, there are: Cookies; Usage Data; first name; last name; phone number; company name; country; state; email address; city; field of activity; data communicated while using the service.
Complete details on each type of Personal Data collected are provided in the dedicated sections of this privacy policy or by specific explanation texts displayed prior to the Data collection.
Personal Data may be freely provided by the User, or, in case of Usage Data, collected automatically when using www.upstreamml.com.
Unless specified otherwise, all Data requested by www.upstreamml.com is mandatory and failure to provide this Data may make it impossible for www.upstreamml.com to provide its services. In cases where www.upstreamml.com specifically states that some Data is not mandatory, Users are free not to communicate this Data without consequences to the availability or the functioning of the Service.
Users who are uncertain about which Personal Data is mandatory are welcome to contact the Owner.
Any use of Cookies – or of other tracking tools – by www.upstreamml.com or by the owners of third-party services used by www.upstreamml.com serves the purpose of providing the Service required by the User, in addition to any other purposes described in the present document and in the Cookie Policy, if available.
Users are responsible for any third-party Personal Data obtained, published or shared through www.upstreamml.com and confirm that they have the third party’s consent to provide the Data to the Owner.

Mode and place of processing the Data

Methods of processing

The Owner takes appropriate security measures to prevent unauthorized access, disclosure, modification, or unauthorized destruction of the Data.
The Data processing is carried out using computers and/or IT enabled tools, following organizational procedures and modes strictly related to the purposes indicated. In addition to the Owner, in some cases, the Data may be accessible to certain types of persons in charge, involved with the operation of www.upstreamml.com (administration, sales, marketing, legal, system administration) or external parties (such as third-party technical service providers, mail carriers, hosting providers, IT companies, communications agencies) appointed, if necessary, as Data Processors by the Owner. The updated list of these parties may be requested from the Owner at any time.

Legal basis of processing

The Owner may process Personal Data relating to Users if one of the following applies:

  • Users have given their consent for one or more specific purposes. Note: Under some legislations the Owner may be allowed to process Personal Data until the User objects to such processing (“opt-out”), without having to rely on consent or any other of the following legal bases. This, however, does not apply, whenever the processing of Personal Data is subject to European data protection law;
  • provision of Data is necessary for the performance of an agreement with the User and/or for any pre-contractual obligations thereof;
  • processing is necessary for compliance with a legal obligation to which the Owner is subject;
  • processing is related to a task that is carried out in the public interest or in the exercise of official authority vested in the Owner;
  • processing is necessary for the purposes of the legitimate interests pursued by the Owner or by a third party.

In any case, the Owner will gladly help to clarify the specific legal basis that applies to the processing, and in particular whether the provision of Personal Data is a statutory or contractual requirement, or a requirement necessary to enter into a contract.

Place

The Data is processed at the Owner’s operating offices and in any other places where the parties involved in the processing are located.

Depending on the User’s location, data transfers may involve transferring the User’s Data to a country other than their own. To find out more about the place of processing of such transferred Data, Users can check the section containing details about the processing of Personal Data.
If broader protection standards are applicable, Users are also entitled to learn about the legal basis of Data transfers to a country outside the European Union or to any international organization governed by public international law or set up by two or more countries, such as the UN, and about the security measures taken by the Owner to safeguard their Data.

If any such transfer takes place, Users can find out more by checking the relevant sections of this document or inquire with the Owner using the information provided in the contact section.

Retention time

Personal Data shall be processed and stored for as long as required by the purpose they have been collected for.
Therefore:

  • Personal Data collected for purposes related to the performance of a contract between the Owner and the User shall be retained until such contract has been fully performed.
  • Personal Data collected for the purposes of the Owner’s legitimate interests shall be retained as long as needed to fulfill such purposes. Users may find specific information regarding the legitimate interests pursued by the Owner within the relevant sections of this document or by contacting the Owner.

The Owner may be allowed to retain Personal Data for a longer period whenever the User has given consent to such processing, as long as such consent is not withdrawn. Furthermore, the Owner may be obliged to retain Personal Data for a longer period whenever required to do so for the performance of a legal obligation or upon order of an authority.

Once the retention period expires, Personal Data shall be deleted. Therefore, the right to access, the right to erasure, the right to rectification and the right to data portability cannot be enforced after expiration of the retention period.

The purposes of processing

The Data concerning the User is collected to allow the Owner to provide its Services, as well as for the following purposes: Managing support and contact requests, User database management, Analytics, Contacting the User, Displaying content from external platforms, Handling payments, Hosting and backend infrastructure, Interaction with live chat platforms, Platform services and hosting and Traffic optimization and distribution.

Users can find further detailed information about such purposes of processing and about the specific Personal Data used for each purpose in the respective sections of this document.

Detailed information on the processing of Personal Data

Personal Data is collected for the following purposes and using the following services:

Analytics

The services contained in this section enable the Owner to monitor and analyze web traffic and can be used to keep track of User behavior.

Google Analytics (Google Inc.)

Google Analytics is a web analysis service provided by Google Inc. (“Google”). Google utilizes the Data collected to track and examine the use of www.upstreamml.com, to prepare reports on its activities and share them with other Google services.
Google may use the Data collected to contextualize and personalize the ads of its own advertising network.
Personal Data collected: Cookies; Usage Data.
Place of processing: United States –Privacy Policy
–Opt Out. Privacy Shield participant.

Contacting the User
Contact form (www.upstreamml.com)

By filling in the contact form with their Data, the User authorizes www.upstreamml.com to use these details to reply to requests for information, quotes or any other kind of request as indicated by the form’s header.
Personal Data collected: city; company name; country; email address; field of activity; first name; last name; phone number; state.

Displaying content from external platforms

This type of service allows you to view content hosted on external platforms directly from the pages of www.upstreamml.com and interact with them.
This type of service might still collect web traffic data for the pages where the service is installed, even when Users do not use it.

Wistia widget (Wistia, Inc.)

Wistia is a video content visualization service provided by Wistia, Inc. that allows www.upstreamml.com to incorporate content of this kind on its pages.
Personal Data collected: Cookies; Usage Data.
Place of processing: United States –Privacy Policy. Privacy Shield participant.

Eventrbrite (Eventbrite, Inc.)

Eventbrite is a ticketing and registration platform provided by Eventbrite, Inc. that allows www.upstreamml.com to incorporate content of this kind on its pages.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: United States –Privacy Policy. Privacy Shield participant.

Handling payments

Payment processing services enable www.upstreamml.com to process payments by credit card, bank transfer or other means. To ensure greater security, www.upstreamml.com shares only the information necessary to execute the transaction with the financial intermediaries handling the transaction.
Some of these services may also enable the sending of timed messages to the User, such as emails containing invoices or notifications concerning the payment.

Stripe (Stripe Inc)

Stripe is a payment service provided by Stripe Inc.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: United States –
Privacy Policy. Privacy Shield participant.

PayPal (PayPal Inc.)

PayPal is a payment service provided by PayPal Inc., which allows Users to make online payments.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: See the PayPal privacy policy –
Privacy Policy.

Hosting and backend infrastructure

This type of service has the purpose of hosting Data and files that enable www.upstreamml.com to run and be distributed as well as to provide a ready-made infrastructure to run specific features or parts of www.upstreamml.com. Some of these services work through geographically distributed servers, making it difficult to determine the actual location where the Personal Data are stored.

Amazon Web Services (AWS) (Amazon Web Services, Inc.)

Amazon Web Services (AWS) is a hosting and backend service provided by Amazon Web Services, Inc.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: United States –
Privacy Policy. Privacy Shield participant.

Interaction with live chat platforms

This type of service allows Users to interact with third-party live chat platforms directly from the pages of www.upstreamml.com, for contacting and being contacted by www.upstreamml.com support service.
If one of these services is installed, it may collect browsing and Usage Data in the pages where it is installed, even if the Users do not actively use the service. Moreover, live chat conversations may be logged.

Freshchat Widget (Freshworks, Inc.)

The Freshchat Widget is a service for interacting with the Freshchat live chat platform provided by Freshworks, Inc.
Personal Data collected: Cookies; Data communicated while using the service; email address; Usage Data.
Place of processing: United States –
Privacy Policy. Privacy Shield participant.

Platform services and hosting

These services have the purpose of hosting and running key components of www.upstreamml.com, therefore allowing the provision of www.upstreamml.com from within a unified platform. Such platforms provide a wide range of tools to the Owner – e.g. analytics, user registration, commenting, database management, e-commerce, payment processing – that imply the collection and handling of Personal Data. Some of these services work through geographically distributed servers, making it difficult to determine the actual location where the Personal Data are stored.

WordPress.com (Automattic Inc.)

WordPress.com is a platform provided by Automattic Inc. that allows the Owner to build, run and host www.upstreamml.com.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: United States –
Privacy Policy.

Traffic optimization and distribution

This type of service allows www.upstreamml.com to distribute their content using servers located across different countries and to optimize their performance.
Which Personal Data are processed depends on the characteristics and the way these services are implemented. Their function is to filter communications between www.upstreamml.com and the User’s browser.
Considering the widespread distribution of this system, it is difficult to determine the locations to which the contents that may contain Personal Information User are transferred.

Cloudflare (Cloudflare)

Cloudflare is a traffic optimization and distribution service provided by Cloudflare Inc.
The way Cloudflare is integrated means that it filters all the traffic through www.upstreamml.com, i.e., communication between www.upstreamml.com and the User’s browser, while also allowing analytical data from www.upstreamml.com to be collected.
Personal Data collected: Cookies; various types of Data as specified in the privacy policy of the service.
Place of processing: United States –
Privacy Policy.

User database management

This type of service allows the Owner to build user profiles by starting from an email address, a personal name, or other information that the User provides to www.upstreamml.com, as well as to track User activities through analytics features. This Personal Data may also be matched with publicly available information about the User (such as social networks’ profiles) and used to build private profiles that the Owner can display and use for improving www.upstreamml.com.
Some of these services may also enable the sending of timed messages to the User, such as emails based on specific actions performed on www.upstreamml.com.

Freshsales (Freshworks, Inc.)

Freshsales is a User database management service provided by Freshworks, Inc.
Personal Data collected: various types of Data as specified in the privacy policy of the service.
Place of processing: United States –
Privacy Policy. Privacy Shield participant.

The rights of Users

Users may exercise certain rights regarding their Data processed by the Owner.
Users entitled to broader protection standards may exercise any of the rights described below. In all other cases, Users may inquire with the Owner to find out which rights apply to them.
In particular, Users have the right to do the following:

  • Withdraw their consent at any time. Users have the right to withdraw consent where they have previously given their consent to the processing of their Personal Data.
  • Object to processing of their Data.Users have the right to object to the processing of their Data if the processing is carried out on a legal basis other than consent. Further details are provided in the dedicated section below.
  • Access their Data.Users have the right to learn if Data is being processed by the Owner, obtain disclosure regarding certain aspects of the processing and obtain a copy of the Data undergoing processing.
  • Verify and seek rectification.Users have the right to verify the accuracy of their Data and ask for it to be updated or corrected.
  • Restrict the processing of their Data.Users have the right, under certain circumstances, to restrict the processing of their Data. In this case, the Owner will not process their Data for any purpose other than storing it.
  • Have their Personal Data deleted or otherwise removed.Users have the right, under certain circumstances, to obtain the erasure of their Data from the Owner.
  • Receive their Data and have it transferred to another controller.Users have the right to receive their Data in a structured, commonly used and machine readable format and, if technically feasible, to have it transmitted to another controller without any hindrance. This provision is applicable provided that the Data is processed by automated means and that the processing is based on the User’s consent, on a contract which the User is part of or on pre-contractual obligations thereof.
  • Lodge a complaint. Users have the right to bring a claim before their competent data protection authority.
Details about the right to object to processing

Where Personal Data is processed for a public interest, in the exercise of an official authority vested in the Owner or for the purposes of the legitimate interests pursued by the Owner, Users may object to such processing by providing a ground related to their particular situation to justify the objection.
Users must know that, however, should their Personal Data be processed for direct marketing purposes, they can object to that processing at any time without providing any justification. To learn, whether the Owner is processing Personal Data for direct marketing purposes, Users may refer to the relevant sections of this document.

How to exercise these rights

Any requests to exercise User rights can be directed to the Owner through the contact details provided in this document. These requests can be exercised free of charge and will be addressed by the Owner as early as possible and always within one month.

Applicability of broader protection standards

While most provisions of this document concern all Users, some provisions expressly only apply if the processing of Personal Data is subject to broader protection standards.
Such broader protection standards apply when the processing:

  • is performed by an Owner based within the EU;
  • concerns the Personal Data of Users who are in the EU and is related to the offering of paid or unpaid goods or services, to such Users;
  • concerns the Personal Data of Users who are in the EU and allows the Owner to monitor such Users’ behavior taking place in the EU.

Additional information about Data collection and processing Legal action

The User’s Personal Data may be used for legal purposes by the Owner in Court or in the stages leading to possible legal action arising from improper use of www.upstreamml.com or the related Services.
The User declares to be aware that the Owner may be required to reveal personal data upon request of public authorities.

Additional information about User’s Personal Data

In addition to the information contained in this privacy policy, www.upstreamml.com may provide the User with additional and contextual information concerning particular Services or the collection and processing of Personal Data upon request.

System logs and maintenance

For operation and maintenance purposes, www.upstreamml.com and any third-party services may collect files that record interaction with www.upstreamml.com (System logs) use other Personal Data (such as the IP Address) for this purpose.

Information not contained in this policy

More details concerning the collection or processing of Personal Data may be requested from the Owner at any time. Please see the contact information at the beginning of this document.

How “Do Not Track” requests are handled

www.upstreamml.com does not support “Do Not Track” requests.
To determine whether any of the third-party services it uses honor the “Do Not Track” requests, please read their privacy policies.

Changes to this privacy policy

The Owner reserves the right to make changes to this privacy policy at any time by giving notice to its Users on this page and possibly within www.upstreamml.com and/or – as far as technically and legally feasible – sending a notice to Users via any contact information available to the Owner. It is strongly recommended to check this page often, referring to the date of the last modification listed at the bottom.

Should the changes affect processing activities performed on the basis of the User’s consent, the Owner shall collect new consent from the User, where required.

Definitions and legal references Personal Data (or Data)

Any information that directly, indirectly, or in connection with other information — including a personal identification number — allows for the identification or identifiability of a natural person.

Usage Data

Information collected automatically through www.upstreamml.com (or third-party services employed in www.upstreamml.com), which can include: the IP addresses or domain names of the computers utilized by the Users who use www.upstreamml.com, the URI addresses (Uniform Resource Identifier), the time of the request, the method utilized to submit the request to the server, the size of the file received in response, the numerical code indicating the status of the server’s answer (successful outcome, error, etc.), the country of origin, the features of the browser and the operating system utilized by the User, the various time details per visit (e.g., the time spent on each page within the Application) and the details about the path followed within the Application with special reference to the sequence of pages visited, and other parameters about the device operating system and/or the User’s IT environment.

User

The individual using www.upstreamml.com who, unless otherwise specified, coincides with the Data Subject.

Data Subject

The natural person to whom the Personal Data refers.

Data Processor (or Data Supervisor)

The natural or legal person, public authority, agency or other body which processes Personal Data on behalf of the Controller, as described in this privacy policy.

Data Controller (or Owner)

The natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of Personal Data, including the security measures concerning the operation and use of www.upstreamml.com. The Data Controller, unless otherwise specified, is the Owner of www.upstreamml.com.

www.upstreamml.com (or this Application)

The means by which the Personal Data of the User is collected and processed.

Service

The service provided by www.upstreamml.com as described in the relative terms (if available) and on this site/application.

European Union (or EU)

Unless otherwise specified, all references made within this document to the European Union include all current member states to the European Union and the European Economic Area.

Cookies

Small sets of data stored in the User’s device.

Legal information

This privacy statement has been prepared based on provisions of multiple legislations, including Art. 13/14 of Regulation (EU) 2016/679 (General Data Protection Regulation).
This privacy policy relates solely to www.upstreamml.com, if not stated otherwise within this document.

Latest update: July 10, 2019

Machine Learning Fault Detection: A Case StudyAldrin Rondon and Lamia Rouis - Dragon Oil

An innovative Fault Pattern Detection Methodology has been carried out using a combination of Machine Learning Techniques to produce a seismic volume suitable for fault interpretation in a structurally and stratigraphic complex field.

Through theory and results, the main objective was to demonstrate that a combination of ML tools can generate superior results in comparison with traditional attribute extraction and data manipulation through conventional algorithms.

The ML technologies applied are a supervised , deep learning, fault classification followed by an unsupervised, multi-attribute classification combining fault probability and instantaneous attributes.

The results are encouraging showing a higher level of structural details when compared with other interpretation techniques. Furthermore, visualization enhancement to better define stratigraphic relationships has also been partially achieved by the combination of fault probability volumes obtained from ML CNN fault detection procedure and multi-attribute classification of seismic features or SOM (Self Organizing Maps).

Machine Learning Technology was applied to a reprocessed seismic dataset in depth domain to generate a detailed, robust and reliable seismic fault attribute volume. The results are being used to constructing a more confident structural framework in the area and better understand stratigraphic trends and relationships to serve as input for static modeling.

Aldrin RondonSenior Geophysicist Engineer and Seismic Interpreter, Dragon Oil

Bachelor’s Degree in Geophysical Engineering from Central University in Venezuela with a specialization in Reservoir Characterization from Simon Bolivar University.

Over 20 years exploration and development geophysical experience with extensive 2D and 3D seismic interpretation including acquisition and processing.

Aldrin spent his formative years working on exploration activity in PDVSA Venezuela followed by a period working for a major international consultant company in the Gulf of Mexico (Landmark, Halliburton) as a G&G consultant. Latterly he was working at Helix in Scotland, UK on producing assets in the Central and South North Sea.  From 2007 to 2021, he has been working as a Senior Seismic Interpreter in Dubai involved in different dedicated development projects in the Caspian Sea.

Dr. Elita De AbreuGeophysics Coordinator, Petrobras

Dr. Elita De Abreu has Master's and Bachelor's degrees in Physics from the State University of Campinas in Brazil and a Ph.D. in Geophysics from the University of Houston. For the past 15 years, she has worked with the PETROBRAS geoscience exploration team, focused on applying and developing tools for Quantitative Interpretation, such as Rock Physics, AVO, Seismic Inversion, and Multi-Attribute analysis. Most recently, Dr. De Abreu has been leading digital transformation projects across different disciplines, integrating multi-scale data in a multi-disciplinary environment.  She is motivated by challenging projects, where she can share her knowledge and learn through other people's experiences. She also has a great interest in basic sciences and supports educational causes, particularly STEM and women in science. In her free time, Dr. De Abreu enjoys contact with nature and reading about diverse topics.

Alireza (Ali) ShahkaramiSr. Analytics Engineer, Baker Hughes

Completion Analytics, Utilizing Descriptive and Predictive Analytics for Optimizing Shut in Strategies of Adjacent Wells During Multi-Stage Hydraulic Fracturing Operations

Offset wells proximal to new hydraulically fractured wells often see intense pressure spikes that can damage the structure of the well, promote sand production and/or impact post-completion production. To mitigate these impacts, operators may shut in all offset wells within a radius of the new completion. Determination of the shut-in radius is often based on analogous operations and experience alone, and tends to be conservatively derived, potentially leading to the unnecessary shut-in of wells that may otherwise not experience any pressure event. Shutting-in too many wells can be the largest expense incurred by a new completion as operators not only work-over the offset wells, but also lose production for the entirety of the completion job. On the other hand, an underestimated shut-in radius might enhance fracture-driven interference during a completion job. An analytics and machine learning approach is presented to better understand the area of concern for offset wells and provide a data-driven recommendation for shut-in radius. The approach has been applied to a large field data set and indicates that historical data can be used to quantify the zone of communication and provide recommendations for future operations.

Dr. Amin KayaliPrincipal Data Science Lead, BHP

TRANSFER LEARNING FOR SUBSURFACE FAULT AND SALT INTERPRETATION

Advances in both high performance computing hardware and the birth of deep learning have created numerous opportunities to automate workflows in Petroleum exploration and development. Subsurface fault imaging and salt body extraction are vital components of subsurface characterization, seismic processing and prospect de-risking. These tasks require significant amounts of time and expertise from interpreters, making them good candidates for automation using machine learning. Developing technically rigorous workflows for the automation of fault and salt mapping will reduce interpretation time, as well as reduce subjective bias in seismic interpretation and ensure reproducibility. In this presentation, I will show how we successfully applied transfer learning to tackle fault network mapping and salt body extraction. First, I will summarize our approach to fault detection and explain how we automated the creation of labeled data sets to train our machine learning fault detection algorithm. I will show the result of our algorithm for a test data set from the F3 block in the North Sea. In the second part of this talk, I will discuss our transfer learning approach to salt body extraction and show how we successfully transferred the learning we achieved using one data set to delineate salt domes in another.
Dr. Amin Kayali Principal Data Science Lead, BHP
Dr. Kayali holds an M.S. in mathematics from the University of Cambridge, an M.S. and Ph.D. in physics from Texas A&M University. He was a postdoctoral fellow at Baylor College of Medicine and later served as a faculty member at Baylor College of Medicine, University of Houston, and Virginia Tech. During these years, his work focused on combining brain imaging data and mathematical modeling to study the neural underpinnings of social and economic decision-making. In 2014, he left academia to join a startup company, Lumina Technologies, where he served as chief scientist leading R&D efforts to create new algorithms for seismic data analysis. In 2018, he joined Baker Hughes as a technical lead for data science initiative within digital technology. At Baker Hughes, he delivered innovative solutions for well integrity prediction, automation of well log interpretation, enhancing the resolution of conventional well logging and many use cases in inventory optimization, cash-flow forecasting, and natural language processing. Currently, he is a principal data science lead at BHP Petroleum where he is leading a team of data scientists to find innovative solutions for E&P challenges using artificial intelligence and high performance computing.
A Combined Deep Learning and Unsupervised Machine Learning Fault Detection WorkflowDr. Carrie Laudon and Dr. Jie Qi - Geophysical Insights
Seismic fault detection is one of the critical steps in seismic interpretation. Identifying faults is crucial for characterizing and finding the potential oil and gas reservoirs. Machine learning holds promise for eliminating some of the tedious and repetitive steps in fault interpretation. Seismic amplitude data serves as input for automatic fault detection and deep learning Convolutional Neural Networks (CNN) perform well on fault detection without any human interactive work. This presentation shows an integrated CNN-based fault detection workflow which enhances the final fault detection volume by applying pre and post processing and an unsupervised seismic classification to ultimately isolate faults within a 3D volume. The pre and post processing objectives were to suppress noise or stratigraphic anomalies subparallel to reflector dip and to sharpen fault and other discontinuities that cut reflectors.  To suppress cross-cutting noise as well as sharpen fault edges, a principal component edge-preserving structure-oriented filter is first applied. The conditioned amplitude volume is then fed to a pre-trained 3D synthetic CNN model to compute fault probability. Finally, a 3D Laplacian of Gaussian filter is applied to the CNN fault probability to enhance fault images. The resulting fault detection volumes (fault probability, fault dip magnitude and fault dip azimuth) compare favorably with traditional human interpretation and in complex structural settings, provide a more complete and unbiased image of faults. Finally, the fault volume is input into an unsupervised machine learning seismic classification (SOM) to generate a 3D volume in which the faults volumes are classified into discrete neurons with known values. This provides superior final results which can subsequently be used to generate geobodies of individual faults or used directly as input to other fault surface extraction tools.
Dr. Carrie LaudonSenior Geophysical Consultant, Geophysical Insights

Carolan (Carrie) Laudon holds a PhD in geophysics from the University of Minnesota and a BS in geology from the University of Wisconsin Eau Claire. She has been Senior Geophysical Consultant with Geophysical Insights since 2017 working with Paradise®, their machine learning platform. Prior roles include Vice President of Consulting Services and Microseismic Technology for Global Geophysical Services and 17 years with Schlumberger in technical, management and sales, starting in Alaska and including Aberdeen, Scotland, Houston, TX, Denver, CO and Reading, England. She spent five years early in her career with ARCO Alaska as a seismic interpreter for the Central North Slope exploration team.

Crystal Lui Analytics & AI Manager, IBM Canada

OVERCOME SILOED APPROACH FOR OPTIMIZATION TO IMPROVE END-TO-END PRODUCTION WITH A PREDICTION-OPTIMIZATION FRAMEWORK

Oil and Gas Upstream Operations is a complex network of assets, processing plants, and inventory tank farms with continuous flows of material and products. The industry has decades of optimization experience and teams of professionals working to maximize production, reduce costs, and improve safety and reliability on these different business areas. The overarching challenge is optimizing the entire end-to-end operation involving multiple business areas in real time. The challenges arise due to the volume and variety of data, as well as the silos created by localized operating objectives.

By leveraging the power of cloud computing, machine learning, operations research, and AI, the Oil & Gas industry can make the most out of their real time IoT data to uncover optimization opportunities across their end-to-end operations. This presentation focuses on how a prediction-optimization framework is enabling one of Canada’s leading integrated energy companies to realize production improvements in extraction, upgrading, and tank farms operations. This solution augments their current workflow with new capabilities to dynamically identify opportunities, better manage operations with early process upset predictions, and near-real time operating plan generation capacity.

Crystal LuiAnalytics & AI Manager, IBM Canada

Crystal is an Analytics & AI Manager at IBM’s Advanced Analytics & AI practice in Canada. She focuses on leading multi-disciplinary teams to help companies build digital transformative capabilities and delivering AI solutions to empower businesses in making data-driven decisions. Prior to joining IBM, Crystal spent several years as an engineer in the Oil & Gas sector where she honed her technical skills and passion for analytics application in industrial settings. She specializes in use case and program design, predictive maintenance, and production optimization. She holds a BASc. in Mineral Engineering and a Masters degree in Business Analytics.

DARIUSZ PIOTROWSKIGlobal Cognitive Solutions Development Leader, Natural Resources Industries, IBM

TRENDS AND APPLICATIONS OF COGNITIVE COMPUTING IN GEOSCIENCE AND PRODUCTION OPTIMIZATION

Overview of examples where IBM applied advanced cognitive computing to exploration and production processes in the oil & gas industry. Starting from Geoscience AI platform that rapidly digests and interprets geological information spread across geological papers, seismic images, well logs and supports knowledge capture from a broad range of studies; and concluding with AI Production Optimization solution applied to multi-facilities continuous production process. The Geoscience solutions have delivered material reduction of exploration and production risks and avoided hundreds of millions in exploration costs. The Production Optimization solution demonstrated the efficacy of AI-based systems in predicting complex interdependent production process failures, reducing recovery time, and alerting operators to production improvement opportunities in a steady-state operations mode materially reducing production costs per barrel.

DARIUSZ PIOTROWSKIGlobal Cognitive Solutions Development Leader, Natural Resources Industries, IBM

Dariusz Piotrowski, Director, Global AI Solutions, IBM Natural Resources Industry Platform, leads the strategy and development of AI (Artificial Intelligence) solutions in natural resources (oil, gas, and mining). Dariusz specializes in large transformational projects focused on optimization, machine learning, and cognitive analytics. He has more than 20 years of technology and consulting experience working with senior leaders within some of the world’s largest natural resources companies. Dariusz helps these companies realize business value through fusing advanced technologies, data science, applied R&D, and agile methodologies and practices to transform business processes and performance. Currently, Dariusz leads the global natural resources AI development team within IBM Industry Platforms. Dariusz holds architecture and civil engineering degrees from Warsaw University of Technology in Poland and an M.B.A. from the Richard Ivey School of Business in Western Ontario.

DAVID HOLMESChief Technology Officer – Energy, Dell EMC

DELIVERING IMPACTFUL AI INNOVATION IN UPSTREAM

In 2019, innovation and in particular digital innovation have taken center stage in enabling companies to re-orient their businesses to the new economic reality. Artificial Intelligence is seen by many as a key enabling technology, but companies often struggle to move their innovation pipeline into impactful production systems. This talk will discuss  :

The role of Generation Z and Generation See-the-Beach in innovation
Vendors, Communities and Secret Sauce – how to balance investments in innovation
Building enterprise-grade digital innovation platforms

DAVID HOLMESChief Technology Officer – Energy, Dell EMC

As Chief Technology Officer – Energy, David is responsible for Dell Technologies’ strategy for the Energy industry. He works with partners and clients to identify business needs and leads teams to develop strategies and architectures to support these requirements. David oversees the roll out of Dell Technologies’ Energy Industry strategy and solutions within the client base globally.

Prior to working for Dell Technologies, David established a highly respected position in the Petro-technical Computing and Information Management community where he was described as “a leader in the global Information Management and Infrastructure community” where he was “highly respected for his ability to engage with clients, develop new solutions and master complex technical/business problems.”

As Information Management Practice Manager for Halliburton-Landmark, David was responsible for architecting, building and operating some of the world’s largest outsourced geotechnical information management and application hosting solutions including for Shell and PGS as well as the National Hydrocarbon Databanks for the UK, Norway and Oman.
Prior to joining Dell Technologies, David was Director of Operations for FUSE IM where he built a team to bring to market FUSE IM’s cloud-based workflow, collaboration and data management tools for petro-technical data. FUSE IM was acquired by Target Energy Solutions in 2014.

David is Dell Technologies’ representative on the OpenEarth Community Executive Committee and was recently elected to the Open Group’s Open Subsurface Data Universe Management Committee. He has previously served on a number of industry committees including the European ECIM Management Committee and SPE’s “Petabytes in Asset Management.” He has delivered numerous technical papers at conferences around the world and holds a patent for his work on the remote visualization of geotechnical applications.

DAVID MOOREPresident & CEO, Deep Imaging Technologies

REAL-TIME FLUID TRACKING: THE MISSING LINK FOR FRAC MODELING MACHINE LEARNING FRAMEWORKS

Upstream processes for the petroleum industry are now data-driven. Throughout the history of exploration and production in unconventional environments, multidisciplinary engineering and geoscience data have been continuously collected. Now, using machine learning and AI, this data is being used in new ways to address fracture stimulation and frac-driven interference by enhancing frac modeling. Through the use of data-driven machine learning techniques, models and frameworks can be tailored to individual assets of interest to predict and analyze frac stimulation within a reservoir. However, it seems the data available is still not enough to accurately predict frac behavior.

This presentation will focus on fluid tracking using an electromagnetic technique that provides the missing link to improve machine learning frameworks for frac modeling without disrupting existing workflows. This data directly correlates to completions and production data by providing direct measurements taken during frac stages, which in turn generates relevant features for machine learning inputs. An outlook is also given on recent developments in real time fluid tracking and how it can be integrated with AI to prevent frac-driven interference and forecast performance on a per-stage and per-well basis.

DAVID MOOREPresident & CEO, Deep Imaging Technologies

David Moore is currently the President and CEO of Deep Imaging, a leading provider of onshore magnetic imaging.  A seasoned energy executive, David spent over a decade at GE Power and GE Oil & Gas and then went on to lead a PE-backed upstream manufacturing company as President and CEO.  David began his career as a Captain in the United States Air Force. 

Deborah SacreyOwner, Auburn Energy

A TALE OF TWO RESERVOIRS: HOW MACHINE LEARNING CAN HELP DEFINE “SWEET SPOTS” IN CONVENTIONAL AND UNCONVENTIONAL RESERVOIRS

The process of statistically analyzing multiple seismic attributes using a SOM (Self-Organized Map) algorithm has been around for several decades. However, advances in computing power, coupled with the many new attributes developed in the last 30 years, has made this type of analysis extremely powerful.

In the past, SOM has been used on only one attribute at a time using the seismic wavelet as the basis for the neural analysis. The approach in this presentation is using SOM on multiple seismic attributes at one time, and in a sample-based, not wavelet, format.

Studies done in the Meramec Formation in Central Oklahoma and the Woodbine Formation of East Texas will be highlighted for the SOM process’s ability to find the best reservoir through the statistical analysis of seismic attributes. Then, converting the neural clusters into Geobodies, calculations can be made to determine reservoir size and reserve estimates. A statistical tool is also embodied to show how the neural patterns can be compared to distinct petrophysical rock properties to confirm the presence of the reservoir.

Deborah SacreyOwner, Auburn Energy

Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US.  She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.

She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past nine years she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience public, guided by Dr. Tom Smith, founder of SMT.  She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.

Deborah has been very active in the geological community.  She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is also Past President of the Gulf Coast Association of Geological Societies and just ended a term as one of the  GCAGS representatives on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2.  She belongs to AAPG, SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

Dr. Ali BakrCEO, Rockserv

APPLICATION OF UNSUPERVISED MACHINE LEARNING FOR 3D SEISMIC, PLIOCENE TURBIDITIES, OFFSHORE NILE DELTA.

Sr Field is a gas field located 100 km offshore west Nile Delta, within the West Delta Deep Marine (WDDM) concession of Shell (Rashid/ El-Burullus joint venture). The water depth ranging from 500-1000 meters.

The Field was penetrated by 4 wells, two of them are exploration wells (So-1 & Sr-1) and the other two are development wells (Sr-a & Sir-c). It is a stratigraphic Pliocene channel of 35-40 km length and 500- 100-meter width, with a clastic sand reservoir of 100-200 meter thickness. The filed is covered by 3D seismic of good quality. One segment of this channel has about 0.65 TCF of gases and three wells from the four wells are included at this segment.

The channel was conventionally mapped using Hampson Russel Spectral decomposition. Unsupervised machine learning is recently used for clustering the data and isolate the channel using the Paradise® software from Geophysical Insights.

Spectral magnitude, spectral voice, spectral phase, sweetness, energy ratio similarity, and Envelop are were identified by Principal Component Analysis (PCA) as having the highest energy in the region of interest. These attributes were then classified simultaneously over the reservoir zone using the Self Organizing Map (SOM) application, a form of machine learning.

Application of the unsupervised Machine learning using SOM clearly demonstrate the strike and geomorphology of the Pliocene marine turbidities. The southern segment of the channel penetrated with the three wells are very well defined after posting the wells. No significant difference on the neurons (hexagons) at the locations of the three wells which reflects the similarity of reservoir nature, thickness, sand content and pay thickness. A significant other channel is resolved to the east of the main channel that is not detected using the conventional spectral decomposition.

Dr. Ali BakrCEO, Rockserv

Ali is the CEO of RockServ for petroleum services based in Cairo. He has a PhD in Exploration seismology from Cairo-Leister University and Geology B.Sc., from Alexandria University. He worked at many different scale companies included Shell, Phillips, Apache, Phoenix, IPR and many Joint Ventures. Has a 40 years oil and gas exploration and development industry and leads many true oil finder groups in Egypt, Sudan, Syria, and Yemen. He is a teaching professor at the American University in Cairo, Ain Shams, Alexandria and Cairo Universities.

DR. ALI SHAHKARAMISenior Reservoir Engineer/Reservoir Analytics Lead, Baker Hughes

SUBSURFACE DIAGNOSTIC ANALYTICS: WHAT COULD MACHINE LEARNING TELL YOU ABOUT FRAC HITS?

Frac hits are a form of fracture-driven interference that occurs when newly drilled wells communicate with existing wells during a completion job. In most cases, frac hits leave a negative production impact. Understanding the main causes of frac hits is complicated and at the same time crucial for optimizing the net profit value of a well pad. Frac hits happen due to a combination of different parameters such as depletion and stress history, inter-well spacing, completion design, and rock characteristics. The available physics-based diagnostics workflows produce outputs with a high degree of uncertainty. These approaches also are unable to handle a database beyond a single well or a few stages. We developed a data-driven approach based on the pattern recognition capabilities of machine learning techniques to characterize and aid understanding of the root causes of frac hits in a well pad during a completion job. The approach was applied to a field data set and indicated that frac hits can be quantitatively attributed to operational or subsurface parameters such as spacing or depletion. A better understanding of frac hits will help to optimize well spacing and completion design parameters and consequently improve hydrocarbon recovery and maximize the return on capital investment. 

DR. ALIREZA (ALI) SHAHKARAMISenior Analytics Engineer, Baker Hughes

Dr. Ali Shahkarami is a Senior Engineer at Baker Hughes GE (BHGE) where he leads the Reservoir Analytics team leading a team of subsurface domain experts and data scientists for developing the next generation of data-driven solutions and workflows for oil and gas and energy industry. He is based at the Energy Innovation Center in Oklahoma City. Prior to joining BHGE, he was an Assistant Professor of Petroleum and Natural Gas Engineering (PNGE) at Saint Francis University in Loretto, Pennsylvania. Dr. Shahkarami started the undergraduate PNGE program at Saint Francis University in 2014 and was the program head and led the program accreditation process before joining BHGE in 2018. He holds Ph.D. and MSc degrees in Petroleum and Natural Gas Engineering from West Virginia University. 

Dr. Amit JunejaPrincipal Data Scientist, Agile Data Decisions

TABIO: AN OPEN-SOURCE TOOLKIT FOR DETECTING AND SEGMENTING TABLES FROM UNSTRUCTURED DOCUMENTS WITH MACHINE LEARNING

Most O&G technicians have one day or another received a core analysis report, a geochemistry report, a pressure-volume-temperature report with several pages of tabulated values with no other choice but to retype the values in Excel to make them usable. This issue not only occurs in the subsurface domain but in all of the O&G sector as well as in other industries. With the support of TOTAL, Technip, Saipem, Schlumberger, Subsea7 and IFPen, Agile Data Decisions has developed an open source solution to automate the extraction of tabular data from documents. This solution combines probabilistic modeling of sequences with machine learning to detect and segment the tables. The approach is very similar to the human approach: A reader can localize a table in a page by looking at the text alignment, the spacing between strings, the ratio between numerical and letters, etc. All of these features are captured by machine learning and deep learning methods for training and detection of tabular data in this project. As required by our sponsors the technology will be released to an open source platform in October 2020 under MIT license for the benefit of the Data Management community. The presentation will describe the novel machine learning approach and provide examples of table automatic detection and segmentation.

Dr. Amit JunejaPrincipal Data Scientist, Agile Data Decisions

Dr. Amit Juneja is an experienced machine learning scientist, developer and business leader. He is currently VP of Data Science at Agile Data Decisions LLC where he leads machine learning and software development projects. For the past 15 years he has led artificial intelligence (AI) and full stack projects in diverse industry sectors including US defense, oil and gas, and automotive.

At Agile Data Decisions LLC, he has led the development an AI system for information extraction from documents that is being used by several major oil and gas companies. At IBM, Amit collaborated with oil and gas companies to build optimal 2-D and 3-D vision models with deep learning and enabled several US manufacturing plants with deep learning based visual quality inspection of products in assembly lines. At Think A Move Ltd. Amit proposed and led development for $3 million of US Defense contracts to build speech recognition and natural language modelling (NLP) applications. At Goodyear Tire and Rubber Company, he led an agile and lean development for a major Internet of Things application and took the concept from prototype to production on a scalable cloud environment.

Amit pursued his love of learning by obtaining a PhD in Machine Learning/Automatic Speech Recognition from University of Maryland, College Park. He holds an MS in Electrical Engineering from Boston University and a BS in Electronics and Communication Engineering from Indian Institute of Technology.

DR. ARVIND SHARMAVP of Data & Analytics, TGS

AI TREND IN OIL & GAS

I will share a broad overview of the forces behind AI and ML.  An overview of current challenges to E&P will be discussed and ways in which ML can address these challenges.  Additionally, where can ML take the industry in the future and how companies can prepare now for maximizing their benefit as technology in this area rapidly grows. 

DR. ARVIND SHARMAVP of Data & Analytics, TGS

Dr. Arvind Sharma is the VP of Data & Analytics at TGS. In this role, he is responsible for Machine Learning initiatives as well as broader digital transformation. He has 10+ years of experience in various seismic and software-related work. Arvind has bachelors and masters degrees in Applied Geology and Exploration Geophysics respectively from the Indian Institute of Technology (IIT) Kharagpur. He has a Ph.D. from Virginia Tech (VT) in Geophysics.

Arvind has a broad background in the oil and gas industry as well as outside the industry. He has worked in jobs ranging from software engineering (Infosys) to efficient seismic acquisition design (PGS) to developing seismic image algorithms (BP) to prospecting and drilling exploration wells (BP). Most recently Arvind was Chief Geophysicist at PGS and held a similar role at TGS before his current position where he led the industry’s first crowdsourcing challenge “TGS-Kaggle Salt Identification Challenge.” Additionally, he holds several patents, has been the keynote speaker at major conferences and has been featured on several ML podcasts.

At TGS, his mission is to create a platform to integrate and analyze all available sub-surface information for risking and decision making. Arvind believes that data integration and machine learning will be pivotal to this industry’s future success.

DR. GERMAN LARRAZABALR&D Geophysics Advisor, Repsol Technology Lab

ACCELERATING SEISMIC INTERPRETATION TASK

In the Oil & Gas industries’ quest for efficiency and cost reduction, the need to significantly reduce the cycle time of conventional interpretation workflows while utilizing maximum detail of the seismic information (Big Data) for all exploration projects is mandatory. This talk will demonstrate our approach to enhance and accelerate the seismic interpretation task based on technology democratization. 

DR. GERMAN LARRAZABALR&D Geophysics Advisor

German Larrazabal has been an R&D Geophysics Advisor for Repsol since 2011. Previously, Dr. Larrazabal was an Applied Mathematician and Computer Scientist from Universidad Central de Venezuela (UCV), Faculty of Sciences, School of Mathematics. Also, Larrazabal has a Master of Sciences in Computer Science from “Universidad Central de Venezuela”, Faculty of Science, School of Computer Science. Moreover, Larrazabal has a Ph.D. in Computer Sciences, Cum Laude award, from University Polytechnic of Catalonia (UPC), Barcelona Supercomputing Center (BSC), Barcelona, Spain.

Larrazabal has been a Professor and Researcher at the University of Carabobo, Venezuela, San Diego State University, USA and University of Texas at El Paso, USA. Also, Larrazabal has been Visiting Professor in the Computational Science Research Center (CSRC) at San Diego State University, USA. He has authored numerous papers and is a prominent speaker at digital transformation conferences.

A Combined Deep Learning and Unsupervised Machine Learning Fault Detection WorkflowDr. Jie Qi - Research Geophysicist, Geophysical Insights

Seismic fault detection is one of the critical steps in seismic interpretation. Identifying faults is crucial for characterizing and finding the potential oil and gas reservoirs. Machine learning holds promise for eliminating some of the tedious and repetitive steps in fault interpretation. Seismic amplitude data serves as input for automatic fault detection and deep learning Convolutional Neural Networks (CNN) perform well on fault detection without any human interactive work.  This presentation shows an integrated CNN-based fault detection workflow which enhances the final fault detection volume by applying pre and post processing and an unsupervised seismic classification to ultimately isolate faults within a 3D volume. The pre and post processing objectives were to suppress noise or stratigraphic anomalies subparallel to reflector dip and to sharpen fault and other discontinuities that cut reflectors.  To suppress cross-cutting noise as well as sharpen fault edges, a principal component edge-preserving structure-oriented filter is first applied. The conditioned amplitude volume is then fed to a pre-trained 3D synthetic CNN model to compute fault probability. Finally, a 3D Laplacian of Gaussian filter is applied to the CNN fault probability to enhance fault images. The resulting fault detection volumes (fault probability, fault dip magnitude and fault dip azimuth) compare favorably with traditional human interpretation and in complex structural settings, provide a more complete and unbiased image of faults. Finally, the fault volume is input into an unsupervised machine learning seismic classification (SOM) to generate a 3D volume in which the faults volumes are classified into discrete neurons with known values.  This provides superior final results which can subsequently be used to generate geobodies of individual faults or used directly as input to other fault surface extraction tools.

Dr. Jie QiResearch Geophysicist, Geophysical Insights

Jie Qi received a Ph. D. (2017) in geophysics from the University of Oklahoma, Norman. He was a postdoctoral research associate at University of Oklahoma from 2017 to 2020. He is currently a research geophysicist at Geophysical Insights. His research interests include machine learning-based seismic interpretation, pattern recognition, image processing, seismic attribute development and interpretation, and seismic facies analysis.

DR. KURT MARFURTPrincipal Investigator, AASPI Consortium, the University of Oklahoma

FINDING THE BEST ATTRIBUTE COMBINATION FOR SEISMIC FACIES CLASSIFICATION

Interpreters face two main challenges in computer-assisted seismic facies analysis. The first challenge is to define, or “label”, the facies of interest. The second challenge is to select a suite of attributes that can differentiate target facies from each other and from the background reflectivity. Accurately defining the seismic expression of a given seismic facies requires an understanding of not only geologic processes but also the limits of seismic acquisition, processing, and imaging. Our goals are to provide a good classification model in terms of validation accuracy, provide quantitative metrics (and ideally, geological insight) as to why a given attribute suite is chosen, and to minimize the computational and memory required.

In principle, a desirable attribute subset is built by detecting relevant and discarding irrelevant attributes. Relevant attributes are those that are highly correlated with the output classes using a technique called univariate attribute analysis. In contrast, redundant attributes are highly correlated with each other. We hypothesize that the redundant and useless attributes that confuse human interpreters also pose problems in machine-learning classification.

DR. KURT MARFURTPrincipal Investigator, AASPI Consortium, the University of Oklahoma

Kurt J. Marfurt joined The University of Oklahoma in 2007 where he serves as the Frank and Henrietta Schultz Professor of Geophysics within the ConocoPhillips School of Geology and Geophysics. Marfurt’s primary research interest is in the development and calibration of new seismic attributes to aid in seismic processing, seismic interpretation, and reservoir characterization. Recent work has focused on applying coherence, spectral decomposition, structure-oriented filtering, and volumetric curvature to mapping fractures and karst with a particular focus on resource plays. Marfurt earned a Ph.D. in applied geophysics at Columbia University’s Henry Krumb School of Mines in New York in 1978 where he also taught as an Assistant Professor for four years. He worked 18 years in a wide range of research projects at Amoco’s Tulsa Research Center after which he joined the University of Houston for eight years as a Professor of Geophysics and the Director of the Allied Geophysics Lab. He has received the SEG best paper (for coherence), SEG best presentation (for seismic modeling), as a coauthor with Satinder Chopra best SEG poster (one on curvature, one on principal component analysis) and best AAPG technical presentation, and as a coauthor with Roderick Perez Altimar, AAPG/SEG Interpretation best paper (on brittleness) awards. Marfurt also served as the SEG/EAGE Distinguished Short Course Instructor for 2006 (on seismic attributes). In addition to teaching and research duties at OU, Marfurt leads short courses on attributes for SEG and AAPG and currently serves as Editor-in-Chief of the AAPG/SEG journal Interpretation.

DR. LENNART JOHNSSONDistinguished Chair of Computer Science, Mathematics, and Electrical & Computer Engineering, the University of Houston

Lennart Johnsson is a Hugh Roy and Lillie Cranz Cullen Distinguished University Chair of Computer Science, Mathematics, and Electrical and Computer Engineering at the University of Houston and is Professor Emeritus at the Royal Institute of Technology, Stockholm, Sweden. Professor Johnsson has served on the Faculties of California Institute of Technology, Yale University, Harvard University, and the Royal Institute of Technology). He has served as Manager of Systems Engineering, Electrical Systems, ABB Corporate Research, Sweden and Director of Computational Sciences at Thinking Machines Corp. (TMC). 

DR. MIKE BRHLIKStaff Geophysicist, ConocoPhillips

DESCRIBING THE RESERVOIR: SEISMIC MACHINE LEARNING AND DATA ANALYTICS

Quantitative seismic reservoir characterization poses a mathematically ill-constrained inversion problem traditionally solved by methods relying on pre-stack seismic inversion and subsequent rock physics transforms. Alternatively, subsurface models can be matched to field seismic data by seismic forward modeling using wells as calibration points. Both these approaches face practical limitations in the sparsity of calibration data and severe non-linearity of the problem requiring multiple simplifying assumptions. Recent extensive developments in machine learning and data-driven model building can provide significant accuracy and efficiency uplift in solving this problem by streamlining seismic attribute analysis and avoiding the need to pass through the elastic domain. We present various approaches to seismic machine learning and their application to both static and dynamic reservoir characterization projects and discuss comparisons to conventional 3D and 4D quantitative interpretation workflows. Emphasis will be given to practical approaches enhancing cross-discipline integration and validation of data analytics methods using both geophysical and data science approaches. We highlight the advantages, challenges and systematic biases encountered in this type of analysis and discuss potential extensions of the data analytics approach using deep learning methods. 

DR. MIKE BRHLIKStaff Geophysicist, ConocoPhillips

After receiving his Ph.D. in Physics from Florida State University, Mike has worked as a researcher in particle physics and cosmology with a keen interest in phenomenological modeling and the interface between theory and experiment. He joined Shell in 2001 and focused on various aspects of geophysics, both seismic and non-seismic, most importantly the applications of inversion theory to quantitative interpretation and reservoir characterization. Since 2010, he has been working for ConocoPhillips in a similar capacity and with the onset of data analytics and machine learning has taken a strong interest in applications of data-driven model building approaches to solving geophysical problems

Dr. Rahul GajbhiyeAssistant Professor of Petroleum Engineering, King Fahd University of Petroleum and Minerals - Saudi Arabia

Dr. Rahul Gajbhiye working as an Assistant Professor in the Department of Petroleum Engineering at KFUPM. He earned his Ph.D. degree in petroleum engineering from Louisiana State University. Before joining KFUPM he worked as a post-doc research associate at Tulsa University Drilling Research Project (TUDRP) at Tulsa University, Oklahoma. His research area includes Surface Production facilities, Optimization and Automation, AI applications in the petroleum industry, multiphase flow in pipes, and EOR.

He is serving as a faculty advisor for the SPE-KFUPM student chapter since 2013 and reviewer for several journals including Arabian Journal for Science and Engineering, Petroleum Science and Engineering, Colloids and Surfaces A: Physicochemical and Engineering Aspects, and Industrial and Engineering Chemistry. He is also the principal investigator of projects on EOR, Multiphase flow, and GOSP optimization.

He is a member of SPE and AADE and received awards for presentations at the GOM Deepwater Technical Symposium, New Orleans (2009), and the AADE (Premier Fluid Conference), Houston (2010). Recently he is awarded the SPE regional service award 2020, from the Middle East and North Africa region.

Dr. Randall GentryDirector of R&D, Petrolern

Randall (Randy) W Gentry (Ph.D., Civil Engineering, University of Memphis, 1998) has over 20 years of experience leading research teams and organizations across academia and federal research organizations within federal agencies focused on energy and environmental issues. Dr. Gentry led many of these organizations through strategic planning and on-going organizational change while targeting mission critical programmatic research needs at the U.S. Department of Energy laboratories (National Energy Technology Laboratory and Argonne National Laboratory), U.S. EPA and served as a tenured faculty member at the University of Tennessee while also leading several research initiatives. Dr. Gentry joined the Petrolern team as R&D Director in early October 2021 and is excited to work with such a strong entrepreneurial group.

Prior to joining Petrolern, Dr. Gentry served as the Deputy Director and Chief Research Officer at the National Energy Technology Laboratory, one of the U.S. DOE’s National Laboratories. At the National Energy Technology Laboratory Dr. Gentry commissioned the strategic planning and inauguration of the Science-based Artificial Intelligence and Machine Learning Institute (SAMI) and served on its inaugural advisory board.

Dr. Gentry has pursued interdisciplinary research during his career and built teams around complex subsurface behavior and phenomena to better understand aquifer mixing behavior and characterize temporal scale response to various stress induced states on those systems as predictors of risk for better management tools, and for better understanding fundamental conceptual model development of reservoir systems. Dr. Gentry has co-authored papers with many of his team members in top tier international journals and is a recognized subject matter expert in multi-layer aquifer mixing behavior. Dr. Gentry was on two teams recognized in January 2021 by U.S. DOE Secretary’s Achievement Awards, the National Virtual Biotechnology Laboratory Team and the Science and Technology Risk Matrix Team for their work performed in 2020 across the National Laboratory system.

Building Robust Data-Driven Machine Learning Models for Geologic Carbon Storage (GCS) Applications: Are We There Yet?Dr. Srikanta Mishra - Technical Director, Geo-energy Modeling & Analysis at Battelle
The focus of this talk will be to examine where things stand vis-à-vis the application of data-driven modeling using machine learning (ML) techniques in the context of geologic carbon storage (GCS) in deep saline formations. GCS is an emerging technology for emissions reduction with a significant subsurface operations component, while ML as the process for building a model between predictors and response, where a black-box algorithm is used to infer the underlying input-output relationship. Examples and case studies will be presented from geologic and geophysical characterization, reservoir model development and operational data analysis, highlighting the promises and perils of this new technology as a complement to traditional mechanistic analysis and modeling and future outlook.
Dr. Srikanta MishraTechnical Director, Geo-energy Modeling & Analytics, Battelle

Dr. Srikanta Mishra is Technical Director for Geo-energy Modeling & Analytics at Battelle Memorial Institute, the world’s largest not-for-profit private R&D organization.  He is a recognized expert on integrating computational modeling and machine-learning assisted data-driven activities for various subsurface energy resource projects, and the recipient of the 2021 SPE International Award for Distinguished Membership.  He was an SPE Distinguished Lecturer on Big Data Analytics during the 2018-19 season, visiting 16 countries to deliver 32 lectures.  He is the author of ~200 refereed publications, conference papers and technical reports, and the book "Applied Statistical Modeling and Data Analytics for the Petroleum Geosciences" published by Elsevier.  He is also a popular instructor of short courses on statistical modeling and data analytics for SPE as well as other organizations.  He holds a PhD degree in Petroleum Engineering from Stanford University.

Dr. Tom SmithPresident and CEO, Geophysical Insights

Dr. Tom Smith, the founder of Seismic Micro-Technology (SMT) and creator of the KINGDOM Software Suite, is the President and CEO of Geophysical Insights (geoinsights.com), where he leads a team of geophysicists, geologists and computer scientists in developing machine learning technologies for interpretation. Dr. Tom Smith received BS and MS degreeS in Geology from Iowa State University and a Ph.D. in Geophysics from the University of Houston. Over a 50-year career, Dr. Smith has been recognized numerous times for his accomplishments in pioneering the science of geophysics. The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) recognized Dr. Smith’s accomplished career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award. The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award. 

DR. ULISSES MELLODirector, IBM Research – Brazil, IBM

ARTIFICIAL INTELLIGENCE: A KNOWLEDGE-ENHANCED MACHINE LEARNING APPROACH

Today most of the focus of AI on the Machine Learning / Deep Learning industry requires a large amount of data that is not always available in early phases of Exploration and Production. To address the data restrictions, I will discuss neuro-symbolic approaches to combine Machine Learning / Deep Learning with formal pre-existing domain knowledge and formal knowledge representation and reasoning. This knowledge-enhanced Machine Learning approach, coupled with transfer learning techniques, allows working with a smaller amount of data and effort necessary to train AI-based models. We will discuss how we can augment ML technologies with domain and contextual knowledge, and enabled more effective transfer learning in applications to real cases for AI-assisted seismic Interpretation and well-log analysis. 

DR. ULISSES MELLODirector, IBM Research -- Brazil, IBM

Ulisses T. Mello is the director of IBM Research – Brazil with sites in São Paulo and Rio de Janeiro. Ulisses is also an IBM global research executive for the chemicals and petroleum Industry sector. He holds a Ph.D. (1994) and MA (1992) in geology from Columbia University, an M.Sc. in geology from Federal University of Ouro Preto (1987), and a B.Sc. (1983) in geology from the University of Sao Paulo, Brazil. His research interests are large-scale basin modeling, hydrogeological modeling, digital oil fields, integrated operation optimization, data assimilation, unstructured meshing, parallel computing, advanced water management, and computational geosciences. 

Challenges and Opportunities for Machine Learning in Upstream ApplicationsDr. Weichang Li, Head of Machine Learning Group - Houston Research Center, Aramco Americas

In recent years we have seen very active development and applications of machine learning/AI technologies in various upstream sectors, such as geophysics and geosciences in exploration, process and asset monitoring and optimization in development and production for both conventional and unconventional fields, as well as sustainability challenges such as carbon storage and sequestration. The progress is enabled by technology gains in powerful computing infrastructure, deep learning algorithms and models, and more importantly the understanding learned at the interface between and integration of subject matter knowledge and machine learning/AI techniques. The diversity and complexity of many applications in the industry involving data from wide range of time-spatial scales and measurement modality also pose interesting research challenges for machine learning.

In this talk, I will share a number of examples for machine learning in upstream applications, including the generalizability and risk of overfitting in deep learning based geophysical inversion, integrating surrogate physics constraints in machine learning based coherent seismic noise removal, cross-modality machine learning in quantitative microscale characterization, and fiber optic DAS based hydraulic fracturing monitoring using deep learning. While the results from these examples are encouraging, the goal is to highlight the challenges and opportunities for machine learning/AI research and development in upstream applications.

Dr. Weichang LiHead of Machine Learning Group - Houston Research Center, Aramco Americas

Weichang Li is the head of the machine learning group at Aramco Americas’ Houston Research Center where he joined in 2015. His current research is focused on developing machine learning and signal processing algorithms/models for geophysics, geoscience and petroleum engineering applications. Prior to Aramco, he had been with ExxonMobil’s Corporate Strategic Research lab. since 2008 where he led the machine learning team from 2011-2014. Weichang has co-organized the SEG machine learning post-convention workshop from 2018 to 2021, the SIAM Data Mining workshop in Geoscience Applications in 2018, and is the associate editor for Geophysics special section on Machine Learning, and recently IEEE Transaction on Neural Network and Learning Systems special issue on Deep Learning for Earth Sciences and Planetary Geosciences. He is a member of the SEG research committee and the NSF IRIS working group on machine learning for fiber optic DAS. Weichang obtained his M.S. (dual) in Electrical Engineering and Computer Sciences, and Ocean Engineering (2002), and Ph.D. in Electrical and Oceanographic Engineering (2006), all from MIT. He was also an Office of Naval Research (ONR) postdoctoral fellow at Woods Hole Oceanographic Institution from 2006-2007.

DUSTIN DEWETTProduct Manager, Geophysical Insights

MAKE DEEP LEARNING ACCESSIBLE TO SEISMIC INTERPRETERS

The past a few years mark the fastest development of machine learning in seismic interpretation community we have ever seen. One of the major accelerators of such growth is the success of deep learning methods, originated from the computer vision discipline. Over the past three years, we witnessed the rapid adoption of deep learning techniques in seismic interpretation. However, most of such adoption is still limited to academia and research institutes, primarily due to the fact that, albeit providing a high-quality result, deep learning methods usually requires much more training data to work effectively. Preparing such training data is often time-consuming, if not impractical, for a seismic interpreter. This presentation is focused on deep learning applications that require limited or even no training data from a general seismic interpreter, which make deep learning more accessible to general seismic interpreters. Examples on deep learning-based seismic facies classification and fault detections demonstrate that a general seismic interpreter can benefit from the high-quality result, while also with greatly improved efficiency. 

DUSTIN DEWETTProduct Manager, Geophysical Insights

Dustin Dewitt has spent the last 10 years in pursuit of geoscience excellence, integrating advanced seismic interpretation techniques with general geoscience workflows to develop methods, which enhance geologic interpretations. After service in the United States Navy, Dustin moved into the private sector, attaining both a B.S. and an M.S. in Geophysics and Seismology from the University of Oklahoma. Upon graduating, he gained experience with BHP Billiton as a QI Geophysicist and subsequently, an Exploration Geophysicist. Currently, he is a Product Manager for Geophysical Insights, contributing to the development of Paradise, the AI workbench. 

Energy Absorption and Traveling Waves

Waves of elastic energy travel through the Earth with the same physical principles as other waves travel through different mediums. This video focuses on the physics of traveling waves and why energy absorption is important to an understanding of seismic data. Starting with a liner second-order vibrating system as a mathematical model, the 50-minute short course presents the classes of waves and their associated measurements. The basics of energy loss in traveling waves are described, as well as the relationship between vibration and energy loss. A simplified model of the seismic geophone is used to described attenuation and damping ratio. Read more

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: No
Total classroom time: 1 hour
Cost: $20

Next-Generation Technologies Powers Value Creation in Oil and GasEric Andersen - Head of Geoscience Solutions and Chief Geoscientist, PETRONAS

Despite its time-honored use of technologies, the upstream oil and gas sector has been slower than other industries to embrace the breakthrough solutions that have transformed several sectors over the past decades. That may be about to change. Much has happened and COVID-19 has had multiple impacts on the oil and gas industry including plunged prices and government revenues, significantly lowered demand, and inflated stockpiles of crude oil. As the industry seeks ways to return to profitability, Next-Generation technologies are emerging as a part of the answer, presenting the possibility of a radically more efficient new reality.

The development of Next-Generation technologies has the potential to transform the upstream oil and gas industry’s fortune by sustainably improving exploration portfolio, capital efficiency, lowering exploration and development costs, and reducing time to first oil. These new technologies bring significant changes to an upstream oil industry’s ecosystem, disrupting the traditional value chain and redefining business models. We took a Next-Generation Technology Stack Lens approach to explore how these breakthrough solutions will alter the upstream industry eco-system by examining Digital Programs, Workflow efficiency and Innovative Projects. This breakthrough approach offers the structure that the upstream industry needs to understand Next-Generation Technologies.

To this end, this presentation will dive deep into PETRONAS’s NexTGEN Technology plan for exploration that is converting data to insights and taking actions based on those insights. We believe that the process of NexTGEN will be unique to each step because upstream exploration contains a discrete set of technologies such as Machine Learning, Data Analytics, and Cloud-based Solutions for a distinct flow of data, a particular set of challenges, different digital maturity, and nature of data generated and utilized in each step differs from that others. NexTGEN is set to have a profound impact on PETRONAS’ exploration delivery mandates. A wave of Next-Generation Technologies is bringing greater technological innovation that powers value creation in Oil and Gas.

Eric AndersenHead of Geoscience Solutions and Chief Geoscientist, PETRONAS
Eric is the Head of Geoscience Solutions and Chief Geoscientist for Exploration/Upstream at PETRONAS. With over 35 years of industry experience. he is responsible for the G&G COE consisting of Geophysical and Geologic Specialists providing advanced technical solutions to PETRONAS’ global portfolio. Eric is also leading PETRONAS’ Next Generation technology plan (NexTGEN) that commits to develop a data driven, integrated G&G solution to Maximize Accuracy and Minimize Uncertainty on a Cloud Platform that will improve Exploration Pace from Basin Entry to Mature Prospect. The initiative deals with all aspects of data access and liberalization, System and software integration, ML/AI and Cloud computing with aim of increasing exploration success.
Fabian RadaSr. Geophysicist, Petroleum Oil & Gas Services

STATISTICAL CALIBRATION OF SOM RESULTS WITH WELL LOG DATA (CASE STUDY)

The paper describes the combined use of machine learning and statistics to correlate to reservoir properties, thereby discriminating between the presence or absence of a reservoir. The results, presented using multiple statistical techniques, are repeatable and dependable in positive reservoir identification, including the reservoir's extent.

The first stage of the proposed statistical method has proven to be very useful in testing whether or not there is a relationship between two qualitative variables (nominal or ordinal) or categorical quantitative variables in the fields of health and social sciences. Its application in the oil industry allows geoscientists not only to test dependence between discrete variables but to measure their degree of correlation (weak, moderate or strong). The talk shows its application to reveal the relationship between a SOM classification volume of a set of nine seismic attributes (whose vertical sampling interval is three meters) and different well data (sedimentary facies, Net Reservoir, and effective porosity grouped by ranges). The data were prepared to construct the contingency tables, where the dependent (response) variable and independent (explanatory) variable were defined, the observed frequencies were obtained, and the frequencies that would be expected if the variables were independent were calculated, and then the difference between the two magnitudes was studied using the contrast statistic called Chi-Square. The second stage implies the calibration of the SOM volume extracted along the wellbore path through statistical analysis of the petrophysical properties VCL and PHIE, and SW for each neuron, which allowed to identify the neurons with the best petrophysical values in a carbonate reservoir.

Fabian RadaSr. Geophysicist, Petroleum Oil & Gas Servicest

Fabian Rada joined Petroleum Oil and Gas Services, Inc (POGS) in January 2015 as Business Development Manager and Consultant to PEMEX. In Mexico, he has participated in several integrated oil and gas reservoir studies. He has consulted with PEMEX Activos and the G&G Technology group to apply the Paradise AI workbench and other tools. Since January 2015, he has been working with Geophysical Insights staff to provide and implement the multi-attribute analysis software Paradise in Petróleos Mexicanos (PEMEX), running a successful pilot test in Litoral Tabasco Tsimin Xux Asset. Mr. Rada began his career in the Venezuelan National Foundation for Seismological Research, where he participated in several geophysical projects, including seismic and gravity data for micro zonation surveys. He then joined China National Petroleum Corporation (CNPC) as QC Geophysicist until he became the Chief Geophysicist in the QA/QC Department. Then, he transitioned to a subsidiary of Petróleos de Venezuela (PDVSA), as a member of the QA/QC and Chief of Potential Field Methods section. Mr. Rada has also participated in processing land seismic data and marine seismic/gravity acquisition surveys. Mr. Rada earned a B.S. in Geophysics from the Central University of Venezuela.

From Subsurface to Surface in 2 AI ProductsFederico Giannangeli - Director E&P Technology and Operating Model, Repsol

Technology and innovation respond to the global and sectorial challenges that Energy Companies face. These challenges are mainly driven by regulatory, societal, and economic demands, as well as the emergence of new technology enablers.  Artificial intelligence, a key enabler, has become the protagonist in the decision-making process in multiple industries.

This presentation will be focused in two AI products releases that joined Repsol E&P's technology catalogue during 2021:

  • Optimized Seismic Acquisition (OSA[TechLab]): Process simultaneous and sparse gathers maintaining quality of seismic data, while reducing costs associated to Acquisition
  • P-Watch Gas Lift Optimization (GLO[TechLab]): Improve distribution of gas lift among wells in scenarios with restricted gas supply or unstable compressor performance
Federico Giannangeli Director of Technology E&P Project, Repsol

Federico Giannangeli is the E&P Director of Technology and Operating Model at Repsol.  He currently leads a multidisciplinary organization with strong focus in value driven technology product development applications to the upstream portfolio.  Federico has spent over 18 years in the Energy Industry with primary contribution in delivering global Oil & Gas developments offshore (i.e. ultra-deep, deep and shallow water), onshore and in remote harsh environments.  He graduated as a Mechanical Engineer in Venezuela and holds a business certificate from Georgetown University.   

Gabriel GuerraVice President of Transformation, Shell

ACCELERATING SHELL’S SUBSURFACE TRANSFORMATION THROUGH MACHINE LEARNING

Shell has long recognised the importance of digital technologies and used them to create a competitive edge for many decades. We are at a tipping point of exponential technological advancement. Machine learning and artificial intelligence are no longer science fiction; they are already automating and optimising operations. In subsurface, digitalisation provides us the opportunity to finally put a single data source at the centre of all subsurface activities, integrating across technical disciplines to enable the best business decisions. Continually improving our understanding of the subsurface enables us to discover more, maximise recovery from new and existing fields. Key focus areas include: getting the most value and information out of data, disrupting our existing workflows and empowering people. Business value and true transformation of our work will come from the speed at which we can turn data into insight, and insights into action at scale. To ensure the right focus, we have narrowed in on a few core technologies and digital solutions such as machine learning and advanced analytics to improve speed and quality of decision-making; and to make the most out of these we need an optimal combination of clear business use-cases, articulated by an upskilled work-force that understands what machine learning can deliver (for example) and an infrastructure that allows us to deploy these workflows seamlessly. Ultimately we see significant business transformation potential through Digital and Machine learning techniques, deployed widely and sitting at the fingertips of our staff. Devising and embracing new work flows, while continuing to deliver business value is the big challenge, where we see also significant potential for disruptive approaches to our ways of working.
Gabriel Guerra VP Exploration Transformation, Shell

Gabriel Guerra, currently is the leader of Shell's Exploration transformation journey, integrating the introduction of new business practices and ways of working to an established technology, digital and data strategy; providing the platform for lasting and enhanced exploration performance. Gabriel is a geologist by background and brings a breadth of experience and energy, after 18 years in various exploration positions with Enterprise Oil and Shell. He also has been involved from the start in shaping and leading exploration digitalisation and is deeply involved in leading the digital agenda for Shell. Gabriel is based in London but hails from Rio de Janeiro, in Brazil, and is married with two kids.

HANI ELSHAHAWIDigitalization Lead – Deepwater Technology, Shell

MACHINE LEARNING IN OIL & GAS - MOVING FROM HYPE TO REALITY

There is much hype around all topics related to digitalization and IR 4.0 and their underlying technologies. At the same time, industries have already seen some real-life applications and benefits as well as some real disruption to their value chains, making it more urgent than ever for oil & gas companies to accelerate their digital transformations. Despite the hype and the promise of machine learning, adoption outside of the tech sector is still at an early, often experimental stage with few firms having deployed it at scale.

This presentation will discuss some of the successes and challenges of moving machine learning in Oil & Gas from hype to reality. There have been plenty of promising successful deployments across robotics and autonomous vehicles, computer vision, virtual agents, and machine learning, with the latter including deep learning and underpinning many recent advances in the other AI technologies. However, the fact that it requires big data that must be trained on what’s often sparse, incomplete, and messy data, its tendency to cut across functional, geographic, and organizational silos, and its dependence on having a digital foundation and an upskilled workforce pose formidable challenges to scaling digital and machine learning in particular.

HANI ELSHAHAWIDigitalization Lead -- Deepwater Technology, Shell

Hani Elshahawi is Digitalization Lead – Deepwater Technologies at Shell where he has spent the last 14 years. Before that, he led FEAST-Shell’s Fluid Evaluation and Sampling Technologies center of excellence, before becoming Deepwater Technology Advisor. Prior to Shell, Hani spent 15 years with Schlumberger in over 10 countries in Africa, Asia, and North America during which he has held various positions in interpretation, consulting, operations, marketing, and technology development. He holds several patents and has authored over 130 technical papers in various areas of petroleum engineering and the geosciences. He was the 2009-2010 President of the SPWLA, distinguished lecturer for the SPE and the SPWLA 2010-2011 and 2013, and recipient of the SPWLA Distinguished Technical Achievement Award in 2012.

Heath SpidleResearch Engineer, Southwest Research Institute

Automated, Unmanned Detection and Quantification of Methane Fugitive Emissions

Compressor stations used to move natural gas are one of the largest sources of fugitive methane emissions in the midstream sector, accounting for approximately 50% of all fugitive emissions(Zimmerle et al., 2015). This problem is most widespread at reciprocating compressors (Subramanian et al., 2015) where faulty seals are a key contributor to methane emissions (Johnson et al., 2015). As such, there is a significant need for a robust technology that could provide an early indication of an unexpected emission. Equally important, the technology needs to be able to account for biogenic versus anthropogenic sources of methane. One means of indirectly making this determination is to leverage optical technologies that can autonomously pinpoint the source of such leaks.

This presentation discusses recent work funded by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), focused on the development of an innovative remote sensing technology that can reliably and autonomously detect fugitive methane emissions in near real-time, using computer vision and deep learning. The technology called the Smart Methane Leak Detection (SLED/M) system was initially developed to monitor facilities such as compressor stations in a stationary, pan-tilt-zoom configuration.

The system has recently been adapted to monitor facilities from an unmanned aerial system (UAS). The speed and maneuverability of UAS platforms are attractive to leak detection and repair program operators but introduce several challenges. Many existing methane detection algorithms rely on mostly static backgrounds becoming unusable with motion. In addition, top-down views of fugitive methane emissions present differently in Optical Gas Imagers (OGI) compared to looking across the plume. Our work has focused on overcoming these challenges, enhancing the operators' ability to detect methane emissions and pinpoint their sources. Another recent adaptation to SLED/M is the ability to quantify methane emissions using passive sensors (OGI, thermal camera), environmental conditions, plume modeling, and deep learning.

SLED/M advances the state-of-the-art for methane emission detection and quantification by focusing on three key critical criteria for effective methane emission mitigation: (1) autonomy (no need for a human to be in the loop), (2) high reliability (low false alarm rates), and (3) real-time performance. Results from this work will be presented.

Heath SpidleResearch Engineer, Southwest Research Institute

Mr. Spidle is a research engineer in the Advanced Inspection Systems Section at Southwest Research Institute. He has a background in digital image processing, computer vision, and machine learning. Mr. Spidle is the Principal Investigator and lead developer on a project for the US Department of Energy, focused on autonomous detection of methane leaks using onlyMidwave Infrared (MWIR) optical sensing and deep learning.

Heather BedleAssistant Professor, University of Oklahoma

Gas Hydrates, Reefs, Channel Architecture, and Fizz Gas: SOM Applications in a Variety of Geologic Settings

Students at the University of Oklahoma have been exploring the uses of SOM techniques for the last year. This presentation will review learnings and results from a few of these research projects. Two projects have investigated the ability of SOMs to aid in identification of pore space materials – both trying to qualitatively identify gas hydrates and under-saturated gas reservoirs. A third study investigated individual attributes and SOMs in recognizing various carbonate facies in a pinnacle reef in the Michigan Basin. The fourth study took a deep dive of various machine learning algorithms, of which SOMs will be discussed, to understand how much machine learning can aid in the identification of deepwater channel architectures.
Heather BedleAssistant Professor, University of Oklahoma
Heather Bedle received a B.S. (1999) in physics from Wake Forest University, and then worked as a systems engineer in the defense industry. She later received a M.S. (2005) and a Ph. D. (2008) degree from Northwestern University. After graduate school, she joined Chevron and worked as both a development geologist and geophysicist in the Gulf of Mexico before joining Chevron’s Energy Technology Company Unit in Houston, TX. In this position, she worked with the Rock Physics from Seismic team analyzing global assets in Chevron’s portfolio. Dr. Bedle is currently an assistant professor of applied geophysics at the University of Oklahoma’s School of Geosciences. She joined OU in 2018, after instructing at the University of Houston for two years. Dr. Bedle and her student research team at OU primarily work with seismic reflection data, using advanced techniques such as machine learning, attribute analysis, and rock physics to reveal additional structural, stratigraphic and tectonic insights of the subsurface.
Introduction to Machine Learning for Interpreters

Every day our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning. Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how do these applications relate to geoscience interpretation and finding oil and gas? More importantly, do these Machine Learning methods produce better results than conventional interpretation approaches? This course will initially wade through the vernacular of Machine Learning and Data Science as it relates to the geoscientist. An overview of how these methods are being employed, as well as, interpretation case studies of different machine learning applications will be presented. An overview of how high-performance computing and the utilization of Cloud Services related to Machine Learning will be described. Machine Learning is a disruptive technology that holds great promise and this course will be presented from an interpreter’s perspective, not a data scientist. This course will provide an understanding of how Machine Learning for interpretation is being utilized today and provide insights on future directions and trends.

Instructor: Rocky Roden, Sr. Consulting Geophysicist, Geophysical Insights
Certification Available: No
Total classroom time: 1 hour
Cost: $20

Jaco Fok Chief Innovation & Digitalization, OMV Petrom

DIGITAL DEMOCRACY TO SCALE DIGITAL TRANSFORMATION

The aim of the Digital Democracy program is scaling digitalization to speed up time to value.
We will mobilize and empower the OMV Petrom workforce to use Data visualization (PowerBI), eSignatures, Desktop automation and Advanced analytics to solve personal or departmental digitalization challenges and make our company more agile and efficient.
Empowering people (Citizen Developers) through access to data relevant to their job and providing them with the tools and skills to solve local digitalization opportunities will create insight and efficiency driven savings and identify and grab new opportunities faster.
Upskilling the workforce and moving them to a digital mindset will also increase the speed of adoption of the corporate digital programs.
By combining the top down large digitalization projects with the bottom up Digital Democracy approach, we expect to scale faster and be more agile.

Jaco Fok Chief Innovation & Digitalization, OMV Petrom

Jaco is Chief Innovation at OMV Petrom, where he is driving the change towards a more innovative and digitally dexterous company with a healthy portfolio of new business options. He does so a.o. by sponsoring a portfolio of proof of concepts to kick start innovations, building a digital academy to develop skills, forging collaborations with external partners, and leading a cross-divisional innovation council to drive alignment and implementation throughout the company. 

In his four years at Shell, he helped their transition to a more Open Innovation approach. He re-invigorated initiatives like GameChanger  and External Technology Collaborations, and set up internal tribes and learning programs to drive culture change. He built Shell TechWorks, an innovative outfit that maximizes the use of external technologies in a ‘skunkworks’ approach.  

Jaco also spent 23 years with Royal DSM in progressive roles across four different businesses, including Business manager Flavors, BU Director (super)Fibers, VP Business Incubator, and Director of the China Innovation Centre. As a member of the DSM Innovation Council, he drove the company change from operational excellence to innovation excellence.  

KENNETH HESTERSolutions Architect Manager, NVIDIA

ARE YOU ON THE EDGE?

Enterprises are now operating at the edge. On factory floors. In stores. On city streets. In urgent care facilities. On Rigs. In Refineries. On Utility Lines. In Smart Meters. At the edge, data flows from billions of IoT sensors to be processed by edge devices and servers, driving real-time decisions where they are needed. All of this is possible—smart retail, cities, manufacturing, utilities, and oil and gas—by bringing the power of AI to the edge.

The presentation discusses methods of leveraging a cloud-native, edge-first, and scalable software stack that enable quick and easy provisioning of infrastructure across a range of devices and servers. Come to the session to discuss the many opportunities to deliver the power of accelerated AI computing at the edge.

KENNETH HESTERSolutions Architect Manager, NVIDIA

Ken Hester is a Solution Architect Manager for NVIDIA supporting the Energy / O&G Industry in HPC, AI Deep Learning and Machine Learning, and CUDA GPU compute. He is based out of Houston, Texas, and has been with NVIDIA for over 5 years. Prior to NVIDIA, Ken worked in Energy for 15+ years as an industry expert in data science, software architecture, software design and development.

For more information about Ken, visit LinkedIn (https://www.linkedin.com/in/kenhester). 

Dr. Kurt MarfurtEmeritus Professor of Geophysics, The University of Oklahoma

 Kurt Marfurt is an Emeritus Professor of Geophysics at the University of Oklahoma, where he mentors students and conducts research to aid seismic interpretation. Marfurt‘s experience includes 23 years as an academician, first at Columbia University, then later at the University of Houston and the University of Oklahoma.  His career also includes 18 years in technology development at Amoco’s Tulsa Research Center working on a wide range of topics.  At OU, Marfurt contributes to  the Attribute-Assisted Seismic Processing and Interpretation (AASPI) consortium with the goal of developing and calibrating new seismic attributes to aid in seismic processing, seismic interpretation, and data integration using both interactive and machine learning tools. He has served as an SEG distinguished lecture short course instructor, as  Editor-in-Chief for the AAPG/SEG journal Interpretation, and is currently Director-at-Large for the SEG and the AAPG/SEG distinguished lecturer for 2021-2022.

Lamia RouisReservoir Department Manager (Studies), Dragon Oil

Post Graduate Degree in Geoscientist from Science University in Tunisia, with 18 years’ experience in geomodeling, reservoir management and development de-risking. Prior to joining Dragon Oil, Lamia was in a leadership position with OMV, involved in many subsurface studies. She implements the latest advanced workflow for Reservoir Characterization, such as OBN, AI and Machine Learning.

Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest.

Instructor: Dr. Tao Zhao
Certification Available: No
Total classroom time: 45 minutes
Cost: Free

Machine Learning Essentialswith/without certification

The course is ideal for geoscientists, engineers, and data analysts at all experience levels. Concepts are supported with ample illustrations and case studies, complemented by mathematical rigor benefiting the subject. Aspects of supervised learning, unsupervised learning, classification, and reclassification are introduced to illustrate how these methods apply to seismic data. For this version of the class, assessments are given at intervals throughout the course to gauge comprehension. Upon completion with a passing total score, a certificate is issue, certified by Geophysical Insights.

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: Yes
Total classroom time: 12 hours
Cost: $75 (with certification), $50 (without certification)

Machine Learning for Incomplete Geoscientists (COMING SOON)

This course covers big-picture machine learning buzz words with both humor and unassailable frankness. The goal of the course is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, seismic wavelets and multi-attribute samples, and several interpretation examples. On conclusion, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and some of their limitations.

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights

Unsupervised Machine Learning for Time-Lapse Seismic Studies and Reservoir MonitoringDr. Marwa Hussein - Assistant Professor, Ain Shams University

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

Dr. Marwa Hussein Assistant Professor, Ain Shams University

Marwa Hussein received a B.S. (2010) in Geophysics from Ain Shams University, and then worked as teaching assistant in the Geophysics department at Ain Shams University for five years. She received her M.S. (2014) in applied Geophysics from Ain Shams University. She later received her Ph.D. in Geophysics (2020) from the University of Houston. She did different research projects and internships for different companies such as BP, Shell, Schlumberger and many joint ventures. Dr. Hussein is currently assistant lecture and researcher at Ain Shams University. Her research focusses primarily on advanced seismic interpretation techniques, seismic attribute analysis, reservoir characterization, machine learning and time-lapse seismic studies.

Maurice Nessim President WesternGeco - Schlumberger, President-elect of the SEG and Past Chairman of The IAGC Board

THE IMPACT OF MACHINE LEARNING ON OIL AND GAS SERVICE INDUSTRIES

Machine learning has evolved over several decades. Since the mid-2000s, neural networks have re-emerged along with various deep learning architectures. These advances have enabled successful applications of deep learning methods in many industries. However, these methods are not being fully exploited in the Oil and Gas industry. Today, many researchers and engineers have taken the initiative to actively research and develop numerous modern machine learning applications in various domains in this industry, including geosciences, drilling, field development planning, completions, production forecasting, to name a few. During the talk, I will demystify the Machine Learning journey and share examples of how machine learning is changing our industry for the years to come. I will share lessons learned from our own company’s experience in this domain.
Data – its availability, volume, and diversity, along with advances in machine learning and computational power, have created unique opportunities to not only achieve significant improvements in both our and our clients, performance, but also to transform how the oil and gas service industry operates today, and in the future.

Maurice Nessim President WesternGeco - Schlumberger, President-elect of the SEG and Past Chairman of The IAGC Board

Maurice Nessim is the President of WesternGeco - Schlumberger, the world’s leading geophysical exploration company specialized in understanding complex subsurface challenges and designing an integrated data to discovery solution that will provide a better, faster, and cheaper answer using advanced processing and interpretation technologies. With over 35 years of oil and gas industry experience in various management and technical positions. Today, Maurice is pioneering WesternGeco’s progressive transformation into an asset-light business, built on WesternGeco’s leading position in multiclient, data processing and geophysical interpretation services. He has launched a digital subsurface platform using groundbreaking technology for a one of a kind access to data and technology. Driving a vision that capitalizes on Schlumberger’s digital technology and digital assets to accelerate energy discovery in exploration, development, and production cycles through forging long-lasting strategic collaboration and partnerships. 

Michael DunnSVP of Business Development, Geophysical Insights

Michael A. Dunn is an exploration executive with extensive global experience including the Gulf of Mexico, Central America, Australia, China and North Africa. Mr. Dunn has a proven a track record of successfully executing exploration strategies built on a foundation of new and innovative technologies. Currently, Michael serves as Senior Vice President of Business Development for Geophysical Insights.

He joined Shell in 1979 as an exploration geophysicist and party chief and held increasing levels or responsibility including Manager of Interpretation Research. In 1997, he participated in the launch of Geokinetics, which completed an IPO on the AMEX in 2007. His extensive experience with oil companies (Shell and Woodside) and the service sector (Geokinetics and Halliburton) has provided him with a unique perspective on technology and applications in oil and gas. Michael received a B.S. in Geology from Rutgers University and an M.S. in Geophysics from the University of Chicago.

Fifth Wave of AI and Fusion of Innovation-centric Energy Industry FutureDr. Mohamed Sidahmed - Deep Learning and Artificial Intelligence Manager, Shell

The energy industry is one of few sectors in the economy that established new technologies incubation for more than a century. Strive to enhance efficiencies, mitigate risks, and ensure safety across workflows resulted in significant discoveries and accumulated knowledge by SMEs.

The field of artificial intelligence has evolved through four distinctive waves of evolution. Witnessing the emergence of the fifth wave of AI is coincidental with a disruptive era of the energy transition. Primary agents of change, largely driven by fundamentals, are not the primary players enabling the radical shift in defining energy futures. We provide examples of AI development waves with examples of applications to the energy industry in general, and to the oil and gas, in particular. We introduce the fifth wave of AI and highlight intertwined opportunities for addressing complex science and engineering innovations to achieve natural transition to carbon-neutral future.

Dr. Mohamed SidahmedDeep Learning and Artificial Intelligence Manager, Shell

Dr. Mohamed Sidahmed leads the Deep Learning and Artificial Intelligence R&D at Shell. Dr. Sidahmed does research in Petroleum Engineering, Machine Learning and Artificial Intelligence applications. 

Yokogawa Saudi Arabia, R&D Department. King Fahd University of Petroleum and Minerals (KFUPM), Petroleum Engineering Department

INTELLIGENT APPROACH FOR GOSP OIL RECOVERY ENHANCEMENT

Traditional industry practice is to operate gas-oil separation plant (GOSP) plant at fixed condition ignoring the variation in the ambient temperature leading to a loss in oil recovery and associated revenue. The study aims to determine the optimal pressure settings of high-pressure production trap (HPPT) and low-pressure production trap (LPPT) that adapt to the variation in ambient temperature in order to maximize the oil recovery. The ultimate goal is an intelligent operational advisory solution that guides the plant operation team to optimal HPPT/LPPT pressure settings that compensate for the variation in ambient temperature effect in order to maximize plant revenue. To develop a correlation, a GOSP model was constructed by OmegaLand dynamic simulator using a typical GOSP design. Oil recovery values were determined by running the process simulation for a typical range of HPPT pressure, low-pressure production trap (LPPT), and ambient temperature. Then, an intelligent approach was built to determine the optimum pressure of the HPPT/LPPT unit for each ambient temperature condition using artificial intelligence techniques. The result shows that oil recovery decreases with an increase in ambient temperature at constant HPPT and LPPT pressure, indicating adjustment in HPPT or LPPT pressure responding to the temperature variations can improve the oil recovery. Results show that oil recovery increases with an increase in HPPT or LPPT pressure until it reaches the optimum value and then decreases with further increase in the HPPT pressure suggesting that there is an optimum HPPT pressure at which oil recovery is maximum. Our proposed advisory solution can determine the optimal operation settings of the separators.
Nam Hoai Pham Principal Geophysicist, Idemitsu Petroleum Norge

Dr. Nam Hoai Pham is currently principal geophysicist and QI specialist (Quantitative Interpretation) at Idemitsu Petroleum Norge AS. Nam spent almost 20 years working both as consultant and employee in various oil and gas companies, Statoil(now called Equinor), Den Norske, Envision, and Idemitsu with his specialties in Quantitative seismic Interpretation, Rock-physics, AVO, and Inversion. His academic background is master’s degrees in reservoir engineering at the University of Stavanger - Norway, and Ph.D. in Geophysics and Rock-physics at NTNU (Norwegian University of Science and Technology). 

NANCY HOUSEPast SEG President (2018-2019)

THE UNCONVENTIONAL REVOLUTION IN EXPLORATION GEOPHYSICS

3D seismic imaging revolutionized hydrocarbon exploration providing a robust picture of the subsurface. Higher prices enabled expensive technologies and investments in the development of previously uneconomic deposits. The balance between development and the market value of the gas or oil is critical. Recent advances in 3D seismic allow interpreters to map areas of higher productivity and identify bypassed reserves. MicroSeismic mapping has made completion more efficient and safer. Geophysical data is now an accepted early development tool of successful oil and gas companies.

NANCY HOUSEPast SEG President (2018-2019)

Nancy House, a member of SEG for nearly 40 years, joining in 1978 as a graduate student at CSM, has worked as a geophysicist for multinational corporations and small independent oil companies primarily as an interpreter on and offshore US, South America and Africa (West and East), and other areas. She is a second-generation geoscientist, growing up in South America and Singapore. She has a BA in Geology/Geophysics from the University of Wyoming, (1976), an MSc in Geophysics from Colorado School of Mines (1979), and did additional postgraduate work at Colorado School of Mines in Reservoir Characterization, Economics and Geophysics (2000-2002).

From the first SEG Annual Meeting Nancy attended in San Francisco in 1978 as a student, she knew that SEG would play an essential part in her career. Early on, SEG provided valuable training, networking opportunities and guidance in professional standards and ethics. Nancy has served on numerous committees including, GAC, Women’s Network Committee, Finance Committee, Membership Committees in SEG. She served as Denver Geophysical Society President/Past President from 2008-2010, General Chairman for SEG AM 2010, and Secretary-Treasurer 2011-2012, Chairman of SEG Women’s Network Committee 2012-2013, and the Finance Committee 2012-2014. Nancy has been a regular contributor to TLE, presenter at meetings (Best Poster 1995), a reviewer for Geophysics and a session chair for various meetings. She also served on several task forces to understand critical business issues around SEGs global activities. She has been a member of AAPG, Dallas GS, Den GS, RMAG, DivEnvirGeol(AAPG), AGU, AWG, and EAEG.

As SEG President 2017-2018, she focused on increasing diversity and inclusion in the profession of geophysics, continued strategies implemented by Rd. Bradford and Bill Abriel and recognized the social contribution of Geophysics and applied geophysics in areas additional to oil and Gas

Narendra VishnumolakalaPetroleum Engineering, Texas A&M University

GO BEYOND DASHBOARDS: DIGITAL TWINS FOR OIL & GAS DRILLING OPERATIONS

Over the last decade, the O&G Energy industry has slowly evolved to incorporate essential digitalization components from the so-called “Industry 4.0 Revolution”. Nevertheless, this digitalization process has led to an explosive increase in the magnitude and value of data generated from several interconnected systems, each with their proprietary framework, standards, and analytical platforms that leave the engineers in charge of critical decision making overwhelmed by the amount of endless segregated information flow. Companies are required to break an ongoing project into small individual tasks and develop the required digital interfaces between every single moving subsystem in-house. This leads to unnecessary complexity, lack of long-term support, and inefficiencies. Furthermore, progress is still lacking in artificial intelligence, visualization, and interoperability of the drilling/energy/business processes.

This presentation will focus on how we aim to solve these problems by developing a state-of-the-art energy analytics and visualization digital twin platform that leverages photorealistic real-time graphics, high-fidelity numerical simulation, and trained physics-based machine learning algorithms to provide the entire energy extraction processes at the fingertips of the operator through a revolutionizing cloud-based platform.

Narendra VishnumolakalaPetroleum Engineering, Texas A&M University

Narendra Vishnumolakala (Vish) is currently a doctoral student at Texas A&M University. He is currently working on developing a reinforcement learning based autonomous downhole drilling tool. Vish has a Master’s in Petroleum Engineering and Bachelor’s in Electronics and Instrumentation Engineering. Before joining the Ph.D. program, Vish worked for ExxonMobil as a Subject Matter Expert in Automation for about 3 years. Prior to that, he worked in the downstream sector, for Indian Oil Corporation Limited in India as an Operations Officer for 3 years. He is currently co-founder of Teale, a tech startup focused on accelerating digital transformation of the Energy Industry. 

NEIL PANCHALPrincipal Data Scientist, QuantumBlack, a McKinsey company

Neil is a Principal Data Scientist for McKinsey QuantumBlack. Neil started his career working as a Field Engineer for Schlumberger working in the Manifa and Shaybah fields in Saudi Arabia in Drilling and Measurements. Neil then joined Shell in Houston as an Automation Wells Engineer. As part of Shell's digital transformation, Neil moved into the role of Deep Learning Lead where he led the Geodesic – Digital Accelerator Product and founded the Shell AI Residency Programme.

Neil received his undergraduate degree in 2007 in Astrophysics from Peterhouse College at the University of Cambridge. Neil then went on to receive his Engineering Doctorate from the Department of Aerospace Sciences from Cranfield University in the UK on the topic of Trajectory Control for Autonomous Systems. Neil is currently an Adjunct Professor in the Practice at Rice University in the Statistics Dept and holds eleven patents and patent-pendings in Machine Learning and Control systems for oil and gas applications. Recently Neil co-organizes the Houston Machine Learning Meetup Group.

ROB SCHAPIROPrincipal Program Manager – Azure Energy, Microsoft

Rob is a Principal Program Manager on the Azure Global Energy Team at Microsoft. He recently joined Microsoft after 17 years at ExxonMobil. He brings industry experience and geoscience expertise to Microsoft where he is focused on helping oil and gas customers find value through innovation. Over the past 17 years at ExxonMobil, he held 15 different technical and leadership positions in Exploration, Development, and Production. He’s worked with partners and governments in 10 different countries on 5 continents.

Most recently, Rob led the Upstream Innovation team within ExxonMobil. His team was focused on rapidly delivering solutions to help ExxonMobil increase profitability from exploration to production. They were also working to instill a culture of innovation by bringing an entrepreneurial mindset to all employees. Leveraging design thinking and lean startup, the team demonstrated they can challenge paradigms, empower others, and deliver value.

Rob earned a Bachelor of Science in Geology from Vanderbilt University and a Master of Science from the University of California Santa Cruz. He lives in the Woodlands, Texas with his wife and two sons (11 and 14 years old). Rob is a former Loaned Executive and Young Leaders Chair with United Way of Greater Houston. He enjoys snow skiing, traveling, and inspiring others to innovate.

Five Trends in Machine Learning for Geoscience InterpretationRocky Roden - President and Chief Geophysicist, Rocky Ridge Resources

Every day our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning.  Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how does this technology relate to geoscience interpretation and finding oil and gas?  More importantly, do Machine Learning methods produce better results than conventional interpretation approaches, or are they simply a means of automating existing processes?  Traditional interpretation approaches that geologists and geophysicists employ are physics-based solutions and now Machine Learning threatens to alter that accepted practice.  Will the integration of machine learning improve our present interpretation workflows, provide moderate to no improvements (overhyped) or produce “profound” results that have not been identified previously?  Machine Learning is a disruptive technology that holds great promise and this presentation will explore that potential from a geoscience interpreter’s perspective.

ROCKY RODENPresident and Chief Geophysicist, Rocky Ridge Resources

Rocky R. Roden owns Rocky Ridge Resources, a consulting practice, and works with several oil companies on technical and prospect evaluation issues. Mr. Roden advises Geophysical Insights in technology development direction and consulting engagements. He also is a principal in the Rose and Associates DHI Risk Analysis Consortium, where he works with producers worldwide. Rocky is a proven oil finder (36 years in the industry) with extensive knowledge of modern geoscience technical approaches and past Chairman of The Leading Edge Editorial Board. As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role included advising corporate officers, geoscientists, and managers on interpretation, strategy, and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. His previous experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco.  Mr. Roden holds a B.S. in Oceanographic Technology-Geology from Lamar University and an M.S. in Geological and Geophysical Oceanography from

SARATH KETINENISenior Reservoir Engineer, Chevron

MACHINE LEARNING APPLICATIONS AND USE CASES IN RESERVOIR ENGINEERING

Different tech industries in silicon valley were successfully able to reap the benefits of integrating machine learning into their business models. The oil and gas industry is slowly but surely catching up to the trend of utilizing machine learning in different aspects of the business. Several oil companies and service providers have partnered with tech companies like Microsoft and others to reap the benefits of data analytics for their organizations. In this talk, I will briefly touch upon different applications of data analytics possible in reservoir engineering and how some of the recent work done in this space have benefited the reservoir engineering community with reduced cycle times. Finding the right models to fit your data is of extreme importance and all engineers need to step up and get digitally fluent to be able to leverage data science into mainstream workflows fully. Traditional reservoir engineering workflows are time and labor-intensive. Integrating multiple sources and scale of data coming in from a variety of surveillance operations need an integrated approach to characterize reservoirs quickly and accurately. Three case studies will be presented from published literature that have presented new ways of using machine learning techniques to improve reservoir simulation, reserves forecasting and reservoir monitoring. 

SARATH KETINENISenior Reservoir Engineer, Chevron

Sarath Ketineni currently works as a senior reservoir engineer at Chevron’s Mid-Continent Business Unit, working in an Asset Development role. He began his career 4 years ago with Chevron and has had two years of prior downstream experience. In his current role, he optimizes field development plans for conventional waterflood and tertiary floods within Chevron’s portfolio and unconventional EOR projects. Prior to this role, he worked as a reservoir simulation engineer at Chevron’s Energy Technology Company. He also serves as a Technical Editor for several SPE journals, the Journal of Natural Gas Science and Engineering, and the Journal of Petroleum Science and Engineering. Apart from these, he is also an SPE e-mentor, student paper contest judge, and virtual career pathways advisor at SPE. He holds a B.Tech in Chemical Engineering from IIT Madras and M.S., Ph.D. in Petroleum Engineering from Penn State. His broad research interests lie in artificial intelligence for oil and gas, advanced reservoir simulation techniques, 4D seismic data integration, and unconventional EOR. Sarath Ketineni currently serves on SPE GCS Young Professionals Board as Roughneck Camp co-Chair. 

Seismic Attributes for the Environment of Deposition

The evaluation of seismic attributes is a powerful tool in the interpretation of different geologic environments of deposition. Seismic attributes, specifically geometric and spectral decomposition attributes, provide a framework for interpreting geologic features that define depositional environments. This video course identifies the appropriate seismic attributes for various geologic settings and describes how these attributes are applied. Lecture and demonstrations cover the use of attributes in interpretation workflows and manipulate attribute parameters to highlight geologic features. The last video segment of the course describes how sets of attributes are analyzed and classified using multi-attribute, Machine Learning processes to extract more information from the seismic response. Read more

Instructors: Dr. Kurt Marfurt, The University of Oklahoma | Dr. ChingWen Chen, Geophysical Insights | Rocky Roden, Geophysical Insights
Certification Available: No
Total classroom time: 5 hours
Cost: $40

Sharareh ManouchehriPrincipal Geophysicist, Idemitsu Petroleum Norge

A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description - Wisting case study

A multidisciplinary approach that is maximizing information extraction from seismic to predict lithofacies and reservoir properties, based on the following steps is presented:
Multi-attribute seismic analysis was applied based on an unsupervised machine learning process called Self- Organizing Maps (SOMs) in Paradise software. The selection of input attributes was thoroughly tested and optimized, based on close co-operation between geophysicists and geologists to extract more extensive and detailed geological features from seismic.

Using the information from nearby wells and knowledge of rock physics, the individual neural classes were quantified and validated and then reorganized and translated to formation properties such as lithofacies, porosity, and clay content.

The study focused on the benefits and additional information that can be gained with this new approach compared to traditional quantitative interpretation approaches (i.e., a prediction from acoustic impedance alone). Multi-attribute classification using machine learning, SOM, gave a better representation of seismic characters, detecting the geologic trends in the field. A detailed quantitative interpretation of SOM neural classes was established to validate and translate formation-related classes optimally for reservoir prediction, and to eliminate classes irrelevant to the formations (i.e., seismic noise).
The result from the Wisting case study shows that the new method gives the best match to the well data and extracts more reservoir related information from seismic compared to the conventional quantitative interpretation (QI) approach. In the upper part of the Triassic, with fluvial sediments assigned to the RG2 unit (Fruholmen Fm.), the reservoir quality and extent of mud clast rich channel intervals are debated. In two of the wells (E and B), a thicker mud unit was observed, and it was debated if this could act as a barrier towards the overlying good reservoir. The result shows that the mud intervals are deposited locally and do not represent a regional mud layer. The additional information from seismic seems to be valuable when used as input and refinement to the digital geological model.

Sharareh ManouchehriPrincipal Geophysicist, Idemitsu Petroleum Norge

Sharareh is currently working as principal geophysicist for Idemitsu Petroleum Norge (IPN). Sharareh Holds a BSc. In physics and MSc in geophysics. She has been working for 17 years as geophysicist and Quantitative Interpretation Specialist for Norsk Hydro, Statoil (currently known as Equinor) and Idemitsu Petroleum Norge. She has been engaged in various exploration projects in Brazil, Gulf of Mexico, offshore Canada, Nigeria, Angola, Tanzania, Mozambique and Norwegian Continental Shelf.  

Shaun GregoryChief Technology Officer, Woodside Energy Ltd.

THE HUMAN FACTOR: KEY TO SUCCESS IN MACHINE LEARNING

The promise of Machine Learning applications in oil and gas is well-known, with most of the sector some way along the journey – particularly upstream. But the journey is not straightforward, with plenty of cautionary tales about high costs, low uptake, and dashed expectations.

Defining the right problem – and then developing the right solution - requires strong partnerships with technology companies and academia. Sophisticated and semi-automated data science tools are becoming increasingly accessible to technical staff, but the complementary skillsets of the data science discipline are vital to avoid common pitfalls like overtraining and poor data quality.

Woodside Energy has developed AI/ML solutions that impact almost everybody in the business. From natural language assistants capable of booking leave and retrieving purchase orders, to fast numerical simulations of complex physics, this presentation will reflect on Woodside’s experiences and learnings of the past five years, with insights into the importance of working effectively with people of all skill and awareness levels to fully unlock value.

Shaun GregoryChief Technology Officer, Woodside Energy

Shaun Gregory has a Bachelor of Science (Hons) from the University of Western Australia in Mathematical Geophysics and a Master of Business and Technology from the University of New South Wales.  

Shaun has over 25 years industry experience and leads Woodside’s Sustainability Division, including Exploration, Technology, Digital, New Energy and Carbon management.  He is passionate about technology innovation, the role they play in enabling business outcomes and the future skills needed to be successful.  

Shaun is a member of Dean’s Council for the faculty of Engineering, Computing and Mathematics at UWA and is a Board member of Scitech WA. 

Single Trace Attributeswith/without certification

The 49-minute short-course focuses on Single trace seismic attributes, which include two general varieties:  instantaneous and banded, sometimes called Wavelet attributes.  The material starts with an organization of seven principal groups or types of attributes and proceeds to set out five primary groups of Single Trace attributes, including Instantaneous, the ‘Tool Kit’ attributes, Instantaneous Layer attributes, Banded attributes, and additional Banded attributes on phase breaks.

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: Yes
Total classroom time: 1 hour
Cost: $30 (with certification), $20 (without certification)

Teresa SantanaChief Geophysicist, YPF

Teresa Santana is currently Chief Geophysicist, Advisor and Diversity Officer at YPF S.A., the national energy company in Argentina. Her role is to ensure technical excellence of the discipline in projects looking for innovation and certifies seniorities for the geoscience community.

With more than 30 years of experience in the Energy industry, Teresa is among pioneers in applied geoscience for quantitative and volume interpretation. She worked for domestic and international companies, such as Shell in Argentina, Europe and the US for 20 years, before joining YPF. Teresa participated and led multidisciplinary teams at numerous onshore/offshore basins around the world for conventional/unconventional reservoirs at different scales. During her international career, Teresa master’s in quantitative seismic interpretation and seismic characterization to predict reservoir properties and fluids from the subsurface. As a result, she was immersed in huge global discoveries such as fields in offshore Guyana and offshore North West Australia. Then, she decided to return to Argentina, her home country, to give back her expertise to the local community.

Teresa’s aspiration for equal business opportunities leads her to volunteer as Diversity Officer at YPF, executive member of the SEG Women’s network, and as ambassador of the WomenTech network. In 2020, she received the Globant Women that Build award in the category Technology Executive in Argentina, an international recognition of global women leaders with STEM training, who occupy a leadership position and are an inspiration for other women and the industry at large. Teresa is also an active mentor for young professionals and students at local and international associations (SEG, EAGE, Fundación YPF).

Innovation and Diversity Impact for Artificial Intelligence Success in Applied GeosciencesTeresa Santana - Chief Geophysicist, Advisor and Diversity Officer, YPF S.A.

Machine learning has been used over several decades in applied geoscience within the energy industry. Machine learning and deep learning can provide a clearer and faster understanding of the reservoirs with more efficient data analysis and integration. Artificial intelligence evolved with the development of sophisticated techniques and software. Additionally, powerful CPUs/GPUs allow complex algorithms to increase their prediction power and reduce both uncertainties and running time. Lately, innovative learning algorithms allow computers to re learn from their own predictions.


With artificial intelligence, geoscientists are capable to make reliable and faster prognosis based on existing logs, seismic, core to support the numerous requirements at the different scales of the subsurface, from frontier exploration to development projects. Besides, geoscientists demand a searchable, organized, diverse, validated, and unique database for all data types, with historical and current data, to generate new play concepts for conventional and unconventional reservoirs in onshore and offshore basins.

During my talk, I will reinforce the opportunities that innovation and diversity have for artificial intelligence success in applied geoscience, for prediction, uncertainty management and efficiency in the years to come.

Terje A. HellemAdvisor Exploration, Idemitsu Petroleum Norway AS (IPN)

Born in 1953 and presently Advisor Exploration in Idemitsu Petroleum Norway AS (IPN). Worked as independent consultant for IPN from 2000 until he became employed in his present position in 2009. 

He is educated as sedimentologist and stratigrapher with a thesis on the Late Permian Tempelfjorden Group on Svalbard, at the University in Oslo in 1980 and worked there as Scientific Assistant until 1983 when he joined Saga Petroleum AS Geological Lab. as sedimentologist and worked with the depositional model for the Troll discovery. The earliest model was published in 1986, when he became partner in READ Geology Services and continued to work with the Troll model. A sequence stratigraphic model for this field was published in 1989 while he was working as an independent consultant for Saga Petroleum AS. In 1993 he became Senior Advisor in sedimentology in Saga and continued there until the company was bought up by Norsk Hydro AS. 

During the last 14 years he has been working in the Barents Sea area and has contributed to the discoveries of Wisting, Alta and Neiden. 

He has also an extensive experience in field geology, comprising eleven seasons on Svalbard, several fieldwork periods in Sinai (Egypt), Libya, several places in Europe and the USA. He has been leading excursions to Svalbard, Sinai and Luxembourg for the oil industry and proprietary excursion to Crete for IPN. 

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