Document Type
Article
Publication Date
Spring 4-10-2020
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Petroleum and Natural Gas Engineering
Abstract
Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most appropriate relationships between all the measured data in a given reservoir.
Digital Commons Citation
Mohaghegh, Shahab D., "Subsurface Analytics: Contribution of Artificial Intelligence and Machine Learning to Reservoir Engineering, Reservoir Modeling, and Reservoir Management" (2020). Faculty & Staff Scholarship. 3089.
https://researchrepository.wvu.edu/faculty_publications/3089
Source Citation
ScienceDirect.com PETROLEUM EXPLORATION AND DEVELOPMENT Volume 47, Issue 2, April 2020 Elsevier. DOI:10.1016/S1876-3804(20)60041-6
Included in
Data Science Commons, Earth Sciences Commons, Engineering Science and Materials Commons, Physics Commons, Statistics and Probability Commons, Systems Engineering Commons