Applied Statistical Modeling and Data Analytics for Reservoir Performance Analysis
Disciplines: Data Science and Engineering Analytics | Reservoir
There is a growing trend towards the use of statistical modeling and data analytics for analyzing the performance of petroleum reservoirs. The goal is to “mine the data” and develop data-driven insights to understand and optimize reservoir response. The process involves: (1) acquiring and managing data in large volumes, of different varieties, and at high velocities, and (2) using statistical techniques to discover hidden patterns of association and relationships in these large, complex, multivariate datasets. However, the subject remains a mystery to most petroleum engineers and geoscientists because of the statistics-heavy jargon and the use of complex algorithms.
This workshop will provide an introduction to statistical modeling and data analytics for reservoir performance analysis by focusing on: (a) easy-to-understand descriptions of the commonly-used concepts and techniques, and (b) case studies demonstrating the value-added proposition for these methods. A software demonstration session will present the application of open-source software for solving problems.
- Terminology and basic concepts of statistical modeling and data analytics
- Exploratory data analysis and basic linear regression modeling (for building a baseline input-output model)
- Multivariate data reduction and clustering (for finding sub-groups of data that have similar attributes)
- Machine learning for regression and classification (for developing data-driven input-output models from production data as an alternative to physics-based models)
- Proxy construction using experimental design (for building fast statistical surrogate models of reservoir performance from simulator outputs for history matching and uncertainty analysis)
Introductory to Intermediate
As “big data” becomes more common place, it will be necessary to extract as much intelligence from our ever-expanding trove of dynamic data from petroleum reservoir to improve operational efficiencies and make better decisions. This course provides the background to understand and apply fundamental concepts of classical statistics, as well as emerging concepts from data analytics, in the analysis of reservoir performance related datasets. This will petroleum engineers/geoscientists to efficiently interact with data scientists and develop practical data-driven applications for their assets (without getting lost in the math).
This course is for designed for petroleum engineers, geoscientists, and managers interested in becoming smart users of statistical modeling and data analytics.
0.8 CEUs (Continuing Education Units) will be 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.
Dr. Srikanta Mishra is the Technical Director for geo-energy modeling and analytics at Battelle Memorial Institute, the world's largest independent contract R&D organization. He also serves as the Technical Lead for US DOE's SMART (Science-Informed Machine Learning for Accelerating Real-Time Decisions for Subsurface Applications) Initiative involving multple national labs, universitities and research organizations. He has taught short courses on statistical modeling and data analytics at various professional conferences and client locations in the US and overseas. He is the auther of " Applied Statistical Modeling and Data Analytics for the Petroleum Geosciences" published by Elsevier, editor of forthcoming book "Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis" to be published by CRC press and author/co-auther of over 200 technical publications. Dr Mishra is the recipient of the SPE Data Science and Engineering Analytics Award for 2022 and SPE Distinguished Membership award for 2021. He also served as SPE Distinguished Lecturer for 2018-2019 on "Big Data Analytics: what can it do for petroleum engineers and geoscientists" He holds a PhD degree from Stanford University, an MS degree from University of Texas and Btech degree from Indian School of Mines- all in petroleum engineering.