Artificial Intelligence & Data Mining

Denver SectionSociety of Petroleum Engineers
Continuing Education Short Course Offering

Application of Artificial Intelligence & Data Mining for Production Optimization in Tight Gas Formations

Instructors:           Shahab D. Mohaghegh, Ph.D., West Virginia University & Intelligent Solutions, Inc. 


Dates:                       Tuesday - Wednesday, February 26-27, 2008, 8:00 AM to 5:00 PM


Location:                  707 Seventeenth Street, Suite 3010, Denver, CO 80202 (Corner of 17th and California)


1.6 CEUs (Continuing Education Units) will be awarded by SPE-Americas Office for this 2-day course.


Course Description

This short course will cover the fundamentals of Artificial intelligence and Data Mining and will provide the theoretical background for its most used components such as artificial neural networks, evolutionary computing, and fuzzy logic. The short course will then provide some insight on the type of problems that can be solved using the artificial intelligence techniques and the types of problems that are not suited for AI. The second part of the short course will be devoted to actual application of these techniques in production optimization of tight gas formations.

Artificial Intelligence is a collection of several analytical tools that attempts to mimic life. AI tools are being used in many commercial products. They are an integrated part of many new cars such as Honda and Mitsubishi. They are used to provide smooth rides in subway systems and prevent fraud in use of credit cards. They are extensively used in the financial market to predict chaotic stock market behavior, or optimize financial portfolios. Their application in oil and gas industry is fairly new. A handful of researchers and practitioners have concentrated their efforts to provide intelligent tools for the petroleum industry. Artificial intelligence tools have been used to Analyze production data, Optimize hydraulic fracture designs, Characterize oil and gas reservoirs, Optimize drilling operation, Interpret well logs, Generate virtual magnetic resonance logs, Select candidate wells for stimulation and Predict post fracture deliverability.

Course Contents

• Artificial Intelligence & Data Mining; an over view

• Artificial Neural Networks

• Evolutionary Computing

• Fuzzy Logic

• Hybrid Intelligent Systems

• Field Applications & Hands on exercises

o Optimization of Hydraulic Fracturing

o Stimulation/Workover Candidate Selection

o Identification of Best Hydraulic Fracturing  Practices

o Intelligent Production Data Analysis for:

􀂃 Remaining reserve estimation

􀂃 Optimization of infill locations

􀂃 Identification of underperformer wells


In the second day of the short course participants will be presented with the evaluation version of a software application for some hands on exercises. Course participants are encouraged to bring:

  1. Their laptop computers to participate in hands on practices and examples that are offered during the second day of the short course.
  2. Their own data in order to test and apply the techniques covered during the short course on the data that they are familiar with (Examples of the data format in Excel are available upon request).

Who Should Attend

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

About the Instructor

Dr. 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 oil and gas industry with solutions based on artificial intelligence & data mining (AI & DM). With more than 16 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 oil and gas 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 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.



The registration deadline is February 1, 2008.  The registration fee is $600.00 and is fully refundable until that time.  For more details, contact Darien O’Brien, P.E. at or (303) 864-6015.  Make your check payable to SPE Denver Section and mail to: Darien O’Brien, Forest Oil Corporation, 707 Seventeenth Street, Suite 3600, Denver, Colorado 80202.  Please include the course name, your FULL name (as you would like it to appear on the course completion certificate), SPE member number (if applicable), Title, Company, e-mail address, postal address, phone and fax number. 

Name Sales End Price
Member Ended $600

On the Web

Data mining - Wikipedia, the free encyclopedia

Artificial Intelligence and Machine Learning - …

Research at Google is at the forefront of innovation in Machine Learning ... ACM Conference on Knowledge Discovery and Data Mining ... Artificial Intelligence, ...

Artificial Intelligence and Data Mining: Algorithms and ...

Artificial intelligence and data mining techniques have been used in many domains to solve classification, segmentation, association, diagnosis, and prediction problems.

Milan Jovovic | LinkedIn

Data Mining, Chemo/Bio-informatics, Computational Vision, Artificial Intelligence. Experience. Researcher Pavol Jozef Safarik University February 2014 – Present ...

Machine learning - Wikipedia, the free encyclopedia

artificial intelligence - Data mining vs Pattern ...

What is the difference between Data mining and Pattern recognition? Thanks.

Artificial Intelligence and Data Mining - Scribd - Read ...

Tech 101. Artificial Intelligence and Data Mining: Enabling Technology for Smart Fields Shahab D. Mohaghegh, Intelligent Solutions and West Virginia University

Data Mining | AITopics

AAAI's AITopics explores Data Mining -- AI-powered tools for discovering interesting relationships in large data sets that can be used to improve actions.