Data-Driven Reservoir Modeling


Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir

Course Description

Data-Driven Reservoir Modeling (Reservoir Analytics) is defined as the application of Artificial Intelligence and Machine Learning in fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) is the manifestation of the digital transformation as it applies to the subsurface modeling in the upstream exploration and production industry. Effective and impactful use of this technology, which is the future of reservoir simulation and modeling, is becoming the important point of competitive differentiation in our industry.

The foundation of Data-Driven Reservoir Modeling (Reservoir Analytics) is solid domain expertise (reservoir engineering, reservoir modeling, and reservoir management) and comprehensive understanding of physics and geology of fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) overcomes the over-simplifications associated with applied statistics and curve fitting approaches (including CRM). The major distinguishing factors of Data-Driven Reservoir Modeling (Reservoir Analytics) when compared to traditional numerical reservoir simulation are (a) avoidance of preconceived notions and biases, (b) lack of inclusion of significant approximations and simplifications, (c) complete automation of the history matching process, (d) generation of accurate and fast subsurface models for practical reservoir management, and (e) performing comprehensive and practical Field Development Planning (FDP), Production and Recovery Optimization (PRO), and Uncertainty Quantification (UQ) with tens of millions of simulation runs.

Data-Driven Reservoir Modeling (Reservoir Analytics) includes a set of tools and techniques that provides the means for extraction of patterns and trends from all field measured data (drilling, completion, formation, seismic, operation, production, well test, well logs, cores, etc.) and construction of predictive models that are validated through blind history matching in time and space. Data-Driven Reservoir Modeling (Reservoir Analytics) provides the ultimate assistance in short, medium, and long term decision making and optimization. Attendees will become familiar with the fundamentals of data-driven analytics, Artificial Intelligence and Machine learning including the most popular techniques used to apply them such as artificial neural networks, evolutionary computing, and fuzzy set theory. This course will demonstrate through actual case studies (and real field data from thousands of wells) how to impact infill well placement, completion, and operational decision-making based on field measurements rather than human biases and preconceived notions.

Topics:

  • Basics of Artificial Intelligence (AI) and Machine Learning
  • Top-Down Modeling - TDM
  • The Spatio - Temporal Database
  • History Matching the Top - Down Model
  • Post-Modeling Analysis of the Top - Down Model
  • Examples and Case Studies

Daily Activities Agenda (pdf)

Learning Level

Introductory to Intermediate

Course Length

1 Day

Why Attend

Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of domain experts have dedicated an extensive amount of time and effort to develop and present the next generation of tools that incorporates these technologies in the petroleum industry. Unfortunately, hypes, buzz words, and marketing schemes around data analytics have overwhelmed the petroleum industry in the past couple of years. Many with little to no understanding and knowledge of the physics and the geology of fluid flow through porous media have been marketing these hypes.

This course will demonstrate the power of Artificial Intelligence and Machine Learning and the difference they can make for informed decision making when it comes to objectives such as infill location optimization and reservoir production and recovery optimization once domain expertise becomes the foundation of their use and application in the hydrocarbon reservoirs.

Who Attends

This course is designed for engineers, geoscientist, and managers. Specifically, those involved with reservoir, completion, and production in operating and service companies. In general, those involved in planning, completion, and operation of hydrocarbon assets are the main target audience.

CEUs

0.8 CEUs (Continuing Education Units) are awarded for this 1-day course.

Additional Resources

This course has a supplemental book located in our SPE Bookstore entitled Data-Driven Reservoir Modeling. Please check out this valuable resource!

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.

Instructor

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).


Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011).


He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

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