Estimation of reservoir fluid saturation from 4D seismic data: effects of noise on seismic amplitude and impedance attributes

Souza, Rafael; Lumley, David; Shragge, Jeffrey

doi:10.1088/1742-2132/14/1/51

Cited by 19 publications

(7 citation statements)

References 25 publications

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“…7. Based on the principle of superposition of seismic wavefields, the changes in (22) where dV refers to dV S and dV P . The superscript monitor refers to the three VSP monitor surveys acquired in this study.…”

Section: Formulation Of Objective Functionmentioning

confidence: 99%

“…13,[19][20][21] Attributes such as the seismic wave travel times, velocities, amplitudes, and seismic reflectivity have been utilized to update reservoir flow properties and monitor the preferential fluid movement. 4,5,11,22 For instance, In Salah, a well-known CCUS project in Algeria has CO 2 stripped from produced natural gas and reinjected into the Krebka formations' aquifer leg. Calibrated a coupled hydro-mechanical model over 5 years by modifying the young's modulus with interferometric synthetic aperture radar (InSAR) datasets for uplift A case study of the morrow B formation, FWU over three horizontal CO 2 injector wells.…”

Section: Introductionmentioning

confidence: 99%

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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

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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Section: Formulation Of Objective Functionmentioning

confidence: 99%

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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“…Both 26 , 28 the studies used Gassmann equations 29 for fluid substitution. Another three-dimentional (3D) modelling study 30 related AI changes with the water saturation (Sw), and quantitatively demonstrated that seismic amplitudes can be more precise than seismic impedances for quantifying Sw changes with 4D seismic data.…”

Section: Introductionmentioning

confidence: 99%

Monitoring geological storage of CO2 using a new rock physics model

Fawad

Mondol

2022

Sci Rep

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To mitigate the global warming crisis, one of the effective ways is to capture CO2 at an emitting source and inject it underground in saline aquifers, depleted oil and gas reservoirs, or in coal beds. This process is known as carbon capture and storage (CCS). With CCS, CO2 is considered a waste product that has to be disposed of properly, like sewage and other pollutants. While and after CO2 injection, monitoring of the CO2 storage site is necessary to observe CO2 plume movement and detect potential leakage. For CO2 monitoring, various physical property changes are employed to delineate the plume area and migration pathways with their pros and cons. We introduce a new rock physics model to facilitate the time-lapse estimation of CO2 saturation and possible pressure changes within a CO2 storage reservoir based on physical properties obtained from the prestack seismic inversion. We demonstrate that the CO2 plume delineation, saturation, and pressure changes estimations are possible using a combination of Acoustic Impedance (AI) and P- to S-wave velocity ratio (Vp/Vs) inverted from time-lapse or four-dimensional (4D) seismic. We assumed a scenario over a period of 40 years comprising an initial 25 year injection period. Our results show that monitoring the CO2 plume in terms of extent and saturation can be carried out using our rock physics-derived method. The suggested method, without going into the elastic moduli level, handles the elastic property cubes, which are commonly obtained from the prestack seismic inversion. Pressure changes quantification is also possible within un-cemented sands; however, the stress/cementation coefficient in our proposed model needs further study to relate that with effective stress in various types of sandstones. The three-dimensional (3D) seismic usually covers the area from the reservoir's base to the surface making it possible to detect the CO2 plume's lateral and vertical migration. However, the comparatively low resolution of seismic, the inversion uncertainties, lateral mineral, and shale property variations are some limitations, which warrant consideration. This method can also be applied for the exploration and monitoring of hydrocarbon production.

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“…Seismic time-lapse signals are sensitive to changes in subsurface elastic properties, where changes due to contrasts in both saturation and pressure are important for CO 2 monitoring. Discrimination between saturation and pressure effects is discussed, e.g., in [16,33,47,58,68,73,74]. Obtaining reliable saturation and pressure estimates from AVO data can be difficult, due to data and modelling errors, poor conditioning of the linearized AVO system, and significant uncertainties in the petroelastic model.…”

Section: Introductionmentioning

confidence: 99%

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

et al. 2020

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A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO 2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO 2 monitoring, the first step is done with either controlled source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A model-based parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO 2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO 2 sequestration in the Skade formation. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

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Estimation of reservoir fluid saturation from 4D seismic data: effects of noise on seismic amplitude and impedance attributes

Cited by 19 publications

References 25 publications

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

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

Monitoring geological storage of CO2 using a new rock physics model

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

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