Oilfield Data Mining

Management and Information Reservoir Descriptions and Dynamics


This course examines the successful application of Artificial Intelligence and Data Mining (AI&DM) in the E&P industry in the past several years. It will start with the fundamentals of 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.

Topics Include:

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

Learning Level


Course Length

2 Days

Why Attend?

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, manufacturing, the financial market, and homeland security. It has been predicted that the use of AI technology will introduce a step-change in how E&P industry does business in the future.

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.


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

Cancellation Policy

To receive a full refund, all cancellations must be received in writing no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Send cancellation requests by email to trainingcourses@spe.org; by fax to +1.866.460.3032 (US) or +1.972.852.9292 (outside US); or mail to SPE Registration, PO Box 833836, Richardson, TX 75083.


Shahab D. Mohaghegh is professor of petroleum and natural gas engineering at West Virginia University. He is founder and president of Intelligent Solutions, the leading company in providing the E&P industry with solutions based on artificial intelligence and data mining (AI&DM). With more than 18 years of experience, 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 April 2005. He was an SPE Distinguished Lecturer for 2007–2008.

He is the technical review chair for SPE Reservoir Evaluation and Engineering 1997–1999 and 2007 to present. He is the current chair of the SPE Global Training Committee. He has also served as chair, discussion leader, and technical presenter in SPE forums and as a steering committee member in SPE workshops. He has been a panelist in several international conference discussing topics related to AI&DM and smart fields.

Mohaghegh holds BS and MS degrees in natural gas engineering from Texas A&I University and a PhD in petroleum and natural gas engineering from Pennsylvania State University.