SPE Workshop: Learning From Reservoir Response - History Matching and Data Analytics 29 - 30 Nov 2017 JW Marriott Austin, Texas, USA

Training Course

Applied Statistical Modeling and Data Analytics for Reservoir Performance Analysis

Instructor: Dr. Srikanta Mishra
28 November 2017 | 0800-1700

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 with the exercises in the workshop.  Topics to be covered include:

  • 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

Learn more about this Training Course.