2019 Event - Oil and Gas Machine Learning Symposium

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.

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.

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.

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.

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

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.

HIGHLIGHTS FROM 2019

THANK YOU FOR A STELLAR, SOLD-OUT 2019 EDITION!

2019 SPEAKERS

Plenary Session

DAVID HOLMES

DR. ARVIND SHARMA

DR. GERMAN LARRAZABAL

ROCKY RODEN

Breakout Sessions

DR. MIKE BRHLIK

HANI ELSHAHAWI

DR. KURT MARFURT

DR. ULISSES MELLO

DAVID MOORE

NANCY HOUSE

DARIUSZ PIOTROWSKI

CAMILO SIERRA

KENNETH HESTER

DUSTIN DEWETT

DR. ALI SHAHKARAMI

SARATH KETINENI

Panel Discussion

DR. LENNART JOHNSSON

NEIL PANCHAL

DR. TOM SMITH

ROB SCHAPIRO

2019 Sponsors

Hosting Sponsor

Platinum Sponsors

GOLD SPONSOR

Silver Sponsors

Media Partner

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Don’t miss updates on the 2020 Machine Learning Symposium.

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

TRANSFER LEARNING FOR SUBSURFACE FAULT AND SALT INTERPRETATION

CALIBRATING SOM RESULTS TO WELLS – IMPROVING STRATIGRAPHIC RESOLUTION IN THE NIOBRARA

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

TRENDS AND APPLICATIONS OF COGNITIVE COMPUTING IN GEOSCIENCE AND PRODUCTION OPTIMIZATION

DELIVERING IMPACTFUL AI INNOVATION IN UPSTREAM

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

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

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

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

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

AI TREND IN OIL & GAS

ACCELERATING SEISMIC INTERPRETATION TASK

FINDING THE BEST ATTRIBUTE COMBINATION FOR SEISMIC FACIES CLASSIFICATION

DESCRIBING THE RESERVOIR: SEISMIC MACHINE LEARNING AND DATA ANALYTICS

ARTIFICIAL INTELLIGENCE: A KNOWLEDGE-ENHANCED MACHINE LEARNING APPROACH

MAKE DEEP LEARNING ACCESSIBLE TO SEISMIC INTERPRETERS

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

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

ACCELERATING SHELL’S SUBSURFACE TRANSFORMATION THROUGH MACHINE LEARNING

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

Automated, Unmanned Detection and Quantification of Methane Fugitive Emissions

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

DIGITAL DEMOCRACY TO SCALE DIGITAL TRANSFORMATION

ARE YOU ON THE EDGE?

UNDERSTANDING THE RESERVOIR QUALITY AND HETEROGENEITY BY USING UNSUPERVISED MACHINE LEARNING METHODS APPLIED TO 3D SEISMIC DATA

THE IMPACT OF MACHINE LEARNING ON OIL AND GAS SERVICE INDUSTRIES

INTELLIGENT APPROACH FOR GOSP OIL RECOVERY ENHANCEMENT

THE UNCONVENTIONAL REVOLUTION IN EXPLORATION GEOPHYSICS

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

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

MACHINE LEARNING APPLICATIONS AND USE CASES IN RESERVOIR ENGINEERING

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

THE HUMAN FACTOR: KEY TO SUCCESS IN MACHINE LEARNING