Super-resolution reconstruction of reservoir saturation map with physical constraints using generative adversarial network

Nandita Doloi, Somnath Ghosh, Jyoti Phirani

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
9 Downloads (Pure)

Abstract

Complete physics-based numerical simulations currently provide the most accurate approach for predicting fluid flow behavior in geological reservoirs. However, the amount of computational resources required to perform these simulations increase exponentially with the increase in resolution to the point that they are infeasible. Therefore, a common practice is to upscale the reservoir model to reduce the resolution such that numerous simulations, as required, can be performed within a reasonable time. The problem we are trying to solve here is that the simulation results from these upscaled models, although they provide a zoomed-out and global view of the reservoir dynamics, however, they lack a detailed zoomed-in view of a local region in the reservoir, which is required to take actionable decisions. This work proposes using super-resolution techniques, recently developed using machine learning methods, to obtain fine-scale flow behavior given flow behavior from a low-resolution simulation of an upscaled-reservoir model. We demonstrate our model on a two-phase, deal-oil, and heterogenous oil reservoir, and we reconstruct the oil saturation map of the reservoir. We also demonstrate how the network can be trained using dynamic coarse geological properties at various resolutions. The findings imply that even when coarse geological features and with limited resolution, the super-resolution reconstructions are able to recreate missing information that is close to the ground facts.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Reservoir Characterisation and Simulation Conference and Exhibition 2023, RCSC 2023
Place of PublicationRichardson, TX.
PublisherSociety of Petroleum Engineers (SPE)
ISBN (Electronic)9781613999738
DOIs
Publication statusPublished - 24 Jan 2023
Event2023 SPE Reservoir Characterisation and Simulation Conference and Exhibition, RCSC 2023 - Abu Dhabi, United Arab Emirates
Duration: 24 Jan 202326 Jan 2023

Publication series

NameSociety of Petroleum Engineers - SPE Reservoir Characterisation and Simulation Conference and Exhibition 2023, RCSC 2023

Conference

Conference2023 SPE Reservoir Characterisation and Simulation Conference and Exhibition, RCSC 2023
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period24/01/2326/01/23

Keywords

  • asia government
  • reservoir
  • upstream oil & gas
  • deep learning
  • artificial intelligence
  • machine learning
  • production control
  • reservoir simulation
  • production monitoring
  • reservoir surveillance

Fingerprint

Dive into the research topics of 'Super-resolution reconstruction of reservoir saturation map with physical constraints using generative adversarial network'. Together they form a unique fingerprint.

Cite this