Oilfield Data Analytics - An Advanced Look at Engineering Application of Artificial Intelligence & Machine Learning
“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.
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.
0.8 CEUs (Continuing Education Units) are awarded for this 1-day course.
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.
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We reserve the right to substitute course instructors as necessary.
Shahab D. Mohaghegh, a pioneer in the application of AI, machine learning and data mining 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 holds BS, MS, and PhD degrees in petroleum and natural gas engineering.
He has authored three books, more than 170 technical papers and carried out more than 60 projects for independents, national and international oil companies. He is an SPE Distinguished Lecturer and has been featured four times as a Distinguished Author in SPE’s Journal of Petroleum Technology. He is the founder of the SPE Petroleum Data-Driven Analytics Technical Section dedicated to AI, machine learning and data mining. He was 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 the U.S. Secretary of Energy’s Unconventional Resources Technical Advisory Committee in two administrations (2008-2014). He recently represented the United States on the International Standard Organization (ISO) carbon capture and storage technical committee (2014-2016).