Oilfield Data Mining
“Data Mining”, as a major component of Data-Driven Analytics, is becoming an important point of competitive differentiation in the upstream oil and gas industry. As the efficiency in production and enhancing recovery becomes an increasingly important issue in the oilfield, companies are realizing that in “Data”, they possess a vast source of important facts and information.
To support and/or substitute traditional approaches to analysis, modeling and optimization, “Data”, reflecting the field measurements, can provide much needed insight. Data Driven Analytics is the set of tools and techniques that provides the means for extraction of patterns and trends in data and construction of predictive models that can assist in decision making and optimization.
In this short course we demonstrate how integration of data from multiple sources, such as drilling, formation evaluation, well testing, reservoir engineering, reservoir modeling, wellbore modeling, artificial lift, surface facilities, etc., can result in cohesive workflows to minimize NPT in drilling, enhance completion design, build data-driven reservoir models, and in short change the way analysis, modeling, and optimization is performed in the upstream oil and gas industry.
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.
- 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
Intermediate to Advanced
Data driven analytics have become an integrated part of many new technologies used by everyone on their day-to-day lives such as smart automatic-transmission in many cars, detecting explosives in the airport security systems, providing smooth rides in subways, and preventing fraud in use of credit cards. They are extensively used in the financial market to predict chaotic stock market behavior, or optimize financial portfolios.
Large amount of data is routinely collected in the upstream oil and gas operation. The collected data can be utilized to gain a competitive advantage in optimizing production and increasing recovery. It has been predicted that the use of AI technologies will introduce a step-change in how E&P industry does business in the future. Get ahead of the curve by learning how this technology works in our industry.
Who Should Attend
This course is designed for engineers and managers. Specifically those involved with drilling, reservoir, completion and production in operating and service companies. In general, those involved in planning, completion, and operation in assets are the main target audience.
.8 CEUs (Continuing Education Units/8 hours) awarded for this 1-day course.
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 email@example.com; 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 the president and CEO of Intelligent Solutions, Inc. (ISI) the pioneers of data driven analytics in the upstream E&P industry. He is also professor of petroleum and natural gas engineering at West Virginia University. With more than 22 years of experience in the application of Artificial Intelligence & Data Mining in petroleum engineering, he has developed innovative workflows and technology that incorporates hybrid forms of neural networks, genetic algorithms and fuzzy logic in solving problems and building predictive models related 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 authored more than 180 technical papers. He was a SPE Distinguished Lecturer (2007-2008) and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. He was the former chair of the SPE Global Training Committee and currently chairs SPE Book committee.
He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources. Mohaghegh represents the Unites States in the International Organization for Standardization (ISO) for CO2 capture and storage.
Mohaghegh holds BS, MS and PhD degrees in petroleum and natural gas engineering.