Abstract
Abstract A novel modeling technology termed "Data Physics" that combines reservoir physics with machine learning is validated against a conventional reservoir simulator for thermal recovery problems, i.e., steam flooding and steam assisted gravity drainage (SAGD). The novelty of the new model is its combination of speed of data integration (less than a week) and runtime (minutes) with long-term predictive accuracy (years or decades). This is due to the unique integration of reservoir physics with fast data-driven methods. For accurate benchmarking, major sources of modeling errors in the finite difference simulations are screened and controlled. Two cases are studied, the SPE4 steamflood model, and a single pad SAGD model. The results demonstrate that the Data Physics model is able to reproduce production profiles and key reservoir physics accurately when numerical errors in simulation are properly accounted for, while also being immune to numerical issues like grid orientation effects that can have significant impact on results of reservoir simulation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
