ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2009.11693
10
1
v1v2 (latest)

A Variational Auto-Encoder for Reservoir Monitoring

23 September 2020
K. Gundersen
S. Hosseini
A. Oleynik
G. Alendal
ArXiv (abs)PDFHTML
Abstract

Carbon dioxide Capture and Storage (CCS) is an important strategy in mitigating anthropogenic CO2_22​ emissions. In order for CCS to be successful, large quantities of CO2_22​ must be stored and the storage site conformance must be monitored. Here we present a deep learning method to reconstruct pressure fields and classify the flux out of the storage formation based on the pressure data from Above Zone Monitoring Interval (AZMI) wells. The deep learning method is a version of a semi conditional variational auto-encoder tailored to solve two tasks: reconstruction of an incremental pressure field and leakage rate classification. The method, predictions and associated uncertainty estimates are illustrated on the synthetic data from a high-fidelity heterogeneous 2D numerical reservoir model, which was used to simulate subsurface CO2_22​ movement and pressure changes in the AZMI due to a CO2_22​ leakage.

View on arXiv
Comments on this paper