4D Seismic Feasibility Study for Well and Reservoir Monitoring in a Deepwater Nigeria Field

Osayande, Francesca; Poon, Becky B.; Horst, Peter A.; Quadt, Edwin; Otoh, George A.

doi:10.2118/172474-ms

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“…T he production and injection of fluids are known to cause variations in seismic velocities, and analysis of these variations through time can provide a valuable way to monitor the movement of fluids in the subsurface, one that is exploited in various 4D seismic monitoring technologies. [1][2][3][4] Fluid saturation changes within the injection zone can be monitored by computing seismic attributes from the processed seismic data. Among these attributes, the time-lapse time or depth shifts are widely employed to observe the travel time differences within the injection zone for several repeated seismic surveys.…”

Section: Introductionmentioning

confidence: 99%

Quantitative interpretation of time‐lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time‐lapse velocity responses

et al. 2022

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This study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 -water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

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Section: Introductionmentioning

confidence: 99%