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

Oil and Gas 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

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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.

CAMILO SIERRAGeology and Geophysics Manager, Lewis Energy

THE APPLICATION OF MACHINE LEARNING AND DEEP LEARNING IN A COMPLEX DEPOSITIONAL ENVIRONMENT


Revealing stratigraphic patterns in seismic data in the early stages of exploration and development can be very challenging without sufficient well information and a clear understating of the depositional environment of the reservoir. Implementation of Machine learning technologies such as unsupervised classification methods and supervised convolutional neuronal networks enables a greater understanding of patterns in the data, similarities, or heterogeneities efficiently and effectively. Using machine learning and deep learning methods, interpreters are better positioned to forecast rock types and assess reservoir quality, thereby increasing the probability of success in wildcat wells and maximizing recovery efficiencies in future development projects.

The methodology and workflow that applied these new tools were successful in differentiating the main rock types in an exploratory project in a frontier basin in South America. The geology was a complex depositional environment with lateral discontinuity and compartmentalization of the reservoir. The environment was challenging to drilling. Together, these conditions made conventional interpretation workflows insufficient for proper exploration analysis and selection of future well locations.

CAMILO SIERRAManager Geology and Geophysics Colombia Asset, Lewis Energy Group

Camilo Sierra Cardenas is Geology and Geophysics Exploration Manager, Colombia Asset, at Lewis Energy in San Antonio, Texas. Mr. Cardenas holds a B.Sc. in Geology from the National University of Colombia, 2005, Bogota, and has 14 years of Experience in conventional reservoir exploration. He has participated in over 50 wildcat projects in different structural styles, complex tectonic settings, and depositional environments in Colombia. 

Crystal Lui Analytics & AI Manager, IBM Canada

OVERCOME SILOED APPROACH FOR OPTIMIZATION TO IMPROVE END-TO-END PRODUCTION WITH THE 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

Deborah Sacrey is a geologist/geophysicist with 41 years of oil and gas exploration experience in the Texas, Louisiana Gulf Coast, and Mid-Continent areas of the US. Deborah specializes in 2D and 3D interpretation for clients in the US and internationally.

She received her degree in Geology from the University of Oklahoma in 1976 and began her career with Gulf Oil in Oklahoma City. She started Auburn Energy in 1990 and built her first geophysical workstation using the Kingdom software in 1996. Deborah then worked closely with SMT (now part of IHS) for 18 years developing and testing Kingdom. For the past eight 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 community, guided by Dr. Tom Smith, founder of SMT. Deborah has become an expert in the use of the Paradise® software and has over five discoveries for clients using the technology.

Deborah is 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 currently the incoming President of the Gulf Coast Association of Geological Societies (GCAGS) and is a member of the GCAGS representation on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She is active in the Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

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. 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.

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. 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. 

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. 

Federico GiannangeliDirector E&P Technology and Operating Model, Repsol

A SNAPSHOT OF AI TECHNOLOGY PRODUCTS SHORTENING E&P DEVELOPMENT CYCLE


Technology and innovation respond to the global and sectorial challenges that Energy Companies faces. These challenges are mainly driven by regulatory, societal demands and economics, as well as, the emergence of new technology enablers. Artificial intelligence, as key enabler, has become the protagonist in the decision-making process in multiple industries. This presentation will provide a pill of information on three of Repsol’s TechLab technology products that are in the process of adoption (i.e. MVP and Scale-up Phase) within Repsol E&P: Automatic Seismic Interpretation (ASI[TechLab]): Automatic Horizon and Fault Extraction + Top Salt Picking functionalities (machine learning) Deterministic Image Analysis (DIA[TechLab]): Digital Rock Characterization with Cutting using image recognition and application to properties prediction (machine learning) Dynamic Field Development Plan Optimization (DFPO[TechLab]): End to End fast reservoir simulation for decision making workflows (reinforced learning and deep neural network)
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 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.

Jaco Fok Chief Innovation & Digitalization, OMV Petrom

DIGITAL DEMOCRACY TO SCALE DIGITAL TRANSFORMATIONShape


  • Mobilize and enable people to easily acquire the tools, knowledge and skills for solving their own digitalization opportunities
  • Focus on Data visualization, Desktop Automation and Advanced Analytics.
  • Upskilling the workforce to increase the speed of adoption of the corporate digital programs
  • Reduced number of reports and paper use; Faster decisions by moving from static tables with number to fresh insight driven reporting.
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). 

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. 

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 VishnumolakalaPh.D., Petroleum 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 VishnumolakalaPh.D., Petroleum 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.

ROCKY RODENSr. Geoscience Consultant, Geophysical Insights

WILL MACHINE LEARNING “PROFOUNDLY” CHANGE GEOSCIENCE INTERPRETATION? – AN INTERPRETER’S PERSPECTIVE


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 RODENSr. Geoscience Consultant, Geophysical Insights

Rocky R. Roden has been involved in the application, evaluation, testing, and development of geoscience technical approaches for the last 44 years (past Chairman – The Leading Edge Editorial Board). In the previous 18 years at his consulting company Rocky Ridge Resources he has worked with numerous oil companies and geoscience software development companies on geoscience technology. As former Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised 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 is 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. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, and Texaco. He holds a B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M.

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. 

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 GregoryExecutive Vice President Sustainability and Chief 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 GregoryExecutive Vice President, 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. 

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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