Time‐lapse vertical seismic profile study in Vacuum Field, New Mexico

Michaud, Gwénola

doi:10.1190/1.1815698

Cited by 3 publications

(2 citation statements)

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“…Vertical seismic profiles (VSPs) and crosswell seismic provide progressively greater resolution than surface seismic at the cost of the portion of a reservoir volume they are able to image. VSP data proved to be valuable for confirming the existence of fractures within the Vacuum Field, New Mexico, which is currently undergoing CO2 flooding for EOR, that were not resolvable on the surface 3-D seismic data (Michaud and Davis, 2000).…”

Section: Borehole Seismicmentioning

confidence: 99%

Preview Number 103

2003

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Section: Borehole Seismicmentioning

confidence: 99%

Preview Number 103

2003

Preview

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“…Again, the seismic velocities are influenced by subsurface stress changes, resulting from fluid production or injection 13 . During CO 2 ‐enhanced oil recovery, the anticipated changes in the pore pressures leads to stress redistribution 14,15 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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. 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.

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