SPEI - Oilfield Data Mining

Speaker Shahab Mohaghegh
Intelligent Solutions and West Virginia University Shahab D. Mohaghegh is professor of Petroleum & Natural Gas Engineering at West Virginia University and founder and president of Intelligent Solutions, Inc., the leading company in providing the E&P industry with solutions based on Artificial Intelligence & Data Mining (AI & DM). With ...


Intelligent Solutions and West Virginia University




Shahab D. Mohaghegh is professor of Petroleum & Natural Gas Engineering at West Virginia University and founder and president of Intelligent Solutions, Inc., the leading company in providing the E&P industry with solutions based on Artificial Intelligence & Data Mining (AI & DM). With more than 18 years of experience, Dr. Mohaghegh has been a pioneer in the application of "AI&DM" in petroleum engineering, applying hybrid forms of neural networks, genetic algorithms and fuzzy logic to smart wells, smart completions, and smart fields as well as to drilling, completion, well stimulation, surface facility optimization, formation evaluation, seismic inversion, reservoir characterization, reservoir simulation and reservoir management.



He has published more than 100 technical papers during his career and has been a technical editor/reviewer for various SPE journals as well as other petroleum-related publications such as Journal of Petroleum Science and Engineering, Computers & Geosciences, Geophysics, and Energy & Fuels. His technical articles on the application of "AI&DM" in the E&P industry and their recent developments have appeared in the Distinguished Author Series of SPE’s Journal of Petroleum Technology during September, October and November of 2000 as well as the April 2005. He is a SPE Distinguished Lecturer for 2007-2008.



He is the technical review chair for SPE Reservoir Evaluation and Engineering Journal 97-99, & 2007-present. He has also served as chair, discussion leader and technical presenter in SPE forums and has served as a steering committee member in SPE Applied Technical Workshops. He has been a panelist in several international conference discussing topics related to "AI&DM" and smart fields.



Shahab D. Mohaghegh holds B.S. and M.S. degrees in Natural Gas Engineering from Texas A&I University and Ph.D. in Petroleum & Natural Gas Engineering from the Pennsylvania State University.




Full Description
 

Description

This short course will start with the fundamentals of Artificial Intelligence and Data Mining (AI&DM) covering artificial neural networks, evolutionary computing, and fuzzy logic. The course is devoted to field application of this technology with focus on production optimization and recovery enhancement.

Artificial Intelligence is a collection of several analytical tools that attempts to mimic life. This technology is used extensively in other industries such as automation and manufacturing, financial market and home land security. It has been predicted that use of AI technology will introduce a step change in how E&P industry does business in the future.

This short course examines the successful application of AI&DM in the E&P industry in the past several years.  These applications include Modeling and Optimization of Stimulation Designs and Practices and Identification of Candidate Wells, Reservoir Characterization, Optimization of Drilling Operations, New Workflows for Reservoir Simulation and Modeling that provide fast screening of the reservoir for identification of remaining reserves and optimum infill locations.

Topics Covered

  • Artificial Intelligence & Data Mining; an overview
  • Artificial Neural Networks, Evolutionary Computing and Fuzzy Logic
  • Field Applications & Hands on exercises
  • Empirical Modeling and Optimization of Stimulation Practices
  • Best Practices Analysis and Well Candidate Selection
  • Top-Down, intelligent Reservoir Modeling
  • Fast screening of alternative field development strategies.
  • Identification of Remaining Reserves and Sweet Spots as a function of time and Optimum Infill Locations.
  • Identification of Underperformer Wells.
  • Surrogate Reservoir Modeling
  • Replication of the results of numerical simulation models in seconds.
  • Unleashing the true potential of reservoir simulation models.
  • Quantification of uncertainties using Monte Carlo Simulation method.

Learning Objectives

  • Providing engineers and geoscientists with an alternative (new and innovative) set of tools and techniques to solve E&P related problems.
  • Identifying remaining reserves and sweet spots in reservoirs as a function of time and different field development strategies.
  • Optimizing stimulation and workover design and effectiveness by coupling reservoir characteristics with stimulation practices and forecasting stimulation outcome.
  • Tapping into the hidden and usually unrealized potentials of numerical reservoir simulation models.
  • Quantifying uncertainties associated with geological models and other parameters used in modeling production optimization and recovery enhancement.

Who Should Attend

This course is designed for reservoir, completion and production engineers of operating companies as well as service company personnel involved with planning, completion and operating wells.

CEUs

1.6 CEUs (Continuing Education Units/8 hours) awarded for this 2-day course

Organizer Cindy Davis

When?

Sat, Oct. 29 - Sun, Oct. 30, 2011
8 a.m. - 5 p.m. US/Mountain

Where?

SPE Annual Technical Conference and Exhibition
Denver, Colorado
USA

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