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Time-lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

Journal Article · · Greenhouse Gases: Science and Technology
DOI:https://doi.org/10.1002/ghg.2237· OSTI ID:2367501
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  1. New Mexico Institute of Mining and Technology, Socorro, NM (United States). Petroleum Research Recovery Center (PRRC)
  2. Tom Bratton LLC, Denver, CO (United States)
  3. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse vertical seismic profiling (VSP) integration. The selected study site is a region around the injector well located within Farnsworth field unit (FWU), where there is an ongoing CO2-enhanced oil recovery (EOR) operation. In our study, a site-specific rock physics model was created from extensive geological, geophysical, and geomechanical characterization through 3D seismic data, well logs, and core assessed as part of the 1D MEM conducted on the characterization well within the study area. Here, the Biot-Gassmann workflow was utilized to combine the rock physics and reservoir simulation outputs to determine the seismic velocity change due to fluid substitution. Modeled seismic velocities attributed to mean effective stress were determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from ultrasonic seismic velocity measurements. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) was utilized to minimize a penalty function created between the modeled seismic velocities and the observed time-lapse VSP dataset. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy and Carbon Management (FECM)
Grant/Contract Number:
89233218CNA000001; FC26-05NT42591; FE0031684
OSTI ID:
2367501
Alternate ID(s):
OSTI ID: 1995300
Report Number(s):
LA-UR--23-25868
Journal Information:
Greenhouse Gases: Science and Technology, Journal Name: Greenhouse Gases: Science and Technology Journal Issue: 5 Vol. 13; ISSN 2152-3878
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

58 GEOSCIENCES
CO2-WAG
EOR
carbon capture
geomechanics
machine learning
utilization and storage
vertical seismic profiling