SC-14 | Applied Statistical Modeling and Data Analytics for Reservoir Performance Analysis
Society of Petroleum Engineers
Sunday, 21 July 2019, 8:00 a.m.–5:00 p.m. | Denver, Colorado
Who Should Attend
This course is for designed for petroleum engineers, geoscientists, and managers interested in becoming smart users of statistical modeling and data analytics.
Objectives
This training course will provide an introduction to statistical modeling and big data analytics for extracting data-driven insights in unconventional reservoirs as an alternative to conventional analytical or numerical modeling studies. It will cover such topics as regression modeling, multivariate statistics, machine learning for regression and classification, proxy modeling, and uncertainty quantification – supplemented by case studies. This course will inform engineers and geologists about techniques for data-driven analysis that can convert data into actionable information for reducing cost, improving efficiency and/or increasing productivity in unconventional oil and gas operations.
Course Content
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. Participants are encouraged to bring their own laptops to follow along the exercises in the workshop.
Topics
- Terminology and basic concepts of statistical modeling and data analytics
- 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)
- Uncertainty quantification for performance forecasting
Why Should You Attend
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).
Fees
Members: $750
Non-members: $950
Students: $300
Limit: 40 People
CEU: 0.8
Includes:
- Printed Course Material
- 1-day course with lecture
- Class exercises and discussion
- Morning and afternoon refreshments and lunch
Venue
Colorado Convention Center
700 14th St
Denver,
Colorado
80202
United States
Instructor