From Logs Scale to Reservoir Scale: Upscaling of the Petroelastic Model

Menezes, Carlos; Gosselin, O

doi:10.2523/100233-ms

Cited by 5 publications

(6 citation statements)

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“…As stated earlier, a PEM could be more than just a simple function. Often Gassmann fluid substitution is employed to determine compressional and shear velocities with the knowledge of fluid densities, fluid elastic moduli, matrix elastic moduli and the rock frame moduli (Menezes and Gosselin 2006;Bornard et al 2005). The first step of the workflow is the evaluation of reservoir properties i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies (Huang et al 1997;Waggoner et al 2002;Menezes and Gosselin 2006) employed the use of an optimization technique to adapt the fine-scale PEMs to an upscaled reservoir grid. The optimization technique involved minimizing the mismatch between observed and predicted impendences.…”

Section: Error Evaluationmentioning

confidence: 99%

“…With the advent of 4D seismic monitoring, petroelastic modeling has become a key element in 4D history matching and reservoir characterization (Falcone et al 2004). Previous studies (Menezes and Gosselin 2006) have proposed a methodology to define an upscaled PEM by adjusting the most sensitive and relevant parameters of a fine-scaled PEM through an optimization procedure.…”

Section: Introductionmentioning

confidence: 99%

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Petro-Elastic Models: How Many and at What Scale?

Alfred

Atan

Hamman

et al. 2008

Europec/Eage Conference and Exhibition

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This paper describes an integrated workflow that determines the minimum number of petro elastic models required to adequately predict elastic properties for a given flow scale. The workflow involves the formulation of scale-dependent petro-elastic models (PEM) and petroelastic facies from well logs. Then through an error evaluation technique, determines the minimum number of PEMs required and the maximum vertical grid size limit for the flow scale. The validity of the fine scale PEMs across different scales is also tested in the workflow.Elastic properties of reservoir rocks can be predicted through petro-elastic models which relate fluid and intrinsic reservoir properties to elastic properties through mathematical functions or rock physics models. Although we would like to generate PEMs at flow scale, very often the PEMs are generated from logs that are consistent with core data. A PEM is lithology and rock fabric specific. In an upscaled flow model, the lithologies or textures are mixed. Fine scale PEMs may no longer adequately describe the elastic properties of the reservoir at flow and seismic scale.Prediction is improved in a flow model that integrates all available measurements. Scale inconsistencies of the measurements need to be addressed for better integration and prediction. In this paper we will show that through a unique and iterative approach, PEMs must be generated at flow scale for improved integration. The methodology has been tested with synthetic data and resulted in a better predictive flow scale model, which is consistent across all scale resolutions.

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Section: Methodsmentioning

confidence: 99%

Section: Error Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Petro-Elastic Models: How Many and at What Scale?

Alfred

Atan

Hamman

et al. 2008

Europec/Eage Conference and Exhibition

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“…In an integrated sim2seis study, three different vertical scales are dealt with: log scale, simulation model scale, and the seismic scale. Differing concepts and approaches associated with the PEM scale are addressed in the literature (for example, Menezes et al, 2006 andAlfred et al, 2008). In this study we evaluate the effect of upscaling the log data to the seismic scale.…”

Section: Pem and Scalementioning

confidence: 99%

Finding a Petro-elastic Model Suitable for Sim2seis Calculation

Amini¹,

Alvarez²,

MacBeth³

et al. 2012

Proceedings

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The petro-elastic model (PEM) is a necessary step in simulator to seismic modelling, intended to close the loop between the seismic and engineering domains. In this work, we discuss some fundamental issues within the conventional PEM algorithm, not commonly covered by published literature. Firstly, we explain the importance of the porosity rock model for the PEM. It is shown that both total/effective porosity models are able to generate satisfactory seismic results, provided that the density and bulk/shear moduli of the solid components are set correctly using an optimisation problem. We find the underlying connections between the simulation model parameterisation and the effective porosity model from the petrophysical domain. Finally, we discuss the effect of vertical upscaling on the seismic domain. We highlight the differences between property upscaling and reflectivity upscaling, and challenge the idea of developing a scale-dependent PEM based on Backus averaging. In addition to a sim2seis analysis, the results of this work have direct impact on seismic inversion via the PEM for pressure and saturation change or impedance change onto the reservoir grid.

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“…The primary objective of this work is to perform full-wavefield, time-lapse seismic modeling from the flow-simulation output for the Dickman Oilfield, Kansas case. Many researchers have investigated the flow-to-seismic problem, which includes downscaling and upscaling processes due to different grid cell sizes (Castro and Caers, 2005;Stoffa et al, 2005;Menezes and Gosselin, 2006;Emerick et al, 2007;Avansi et al, 2010). However, most of their input geologic models are fairly simple and rarely include realistic geologic features such as pinchouts and unconformities in the flow-simulation model.…”

Section: Introductionmentioning

confidence: 99%

Time-lapse seismic modeling for CO2 sequestration at the Dickman Oilfield, Kansas

Liner

Stewart

2014

GEOPHYSICS

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Carbon dioxide capture and injection into the subsurface has aroused great interest in the past few years as a method to enhance oil recovery and mitigate CO 2 emissions. The Dickman Oilfield located in Kansas provides two possible CO 2 sequestration targets: a regional deep saline reservoir (the primary objective) and a shallower mature depleted oil reservoir (secondary). We focused on the shallower depleted oil reservoir through a 250-year flow-simulation scenario and a fault leakage test. Seismic responses at various time intervals were simulated to help monitor CO 2 flow paths and injection stability. A complex and realistic geologic model with unconformity was embedded in the flow-simulation model. A regridding technique was used that assigned geologic values to a regular seismic grid that allowed 2D acoustic and elastic finite-difference simulation. Gassmann fluid substitution theory was used to obtain the reservoir properties with different CO 2 saturations, and the vertical seismic profile was used to assist in identifying geologic layers. A CO 2 plume and flow path from the leakage test can be detected from the differences in seismic data (5 to 10 ms time shift) from the first year of injection and the last year of monitoring. This was supported by comparison with the prestack field data available in the Dickman Oilfield. CO 2 -induced reflectivity changes are relatively larger for PS events than for PP events, implying that multicomponent data acquisition and processing may give added value to characterization and monitoring of carbon capture and storage projects. We assessed 4D seismic monitoring in the evaluation of long-term CO 2 containment stability for the Dickman Oilfield and suggested that time-lapse surveys will be useful.

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From Logs Scale to Reservoir Scale: Upscaling of the Petroelastic Model

Cited by 5 publications

References 0 publications

Petro-Elastic Models: How Many and at What Scale?

Petro-Elastic Models: How Many and at What Scale?

Finding a Petro-elastic Model Suitable for Sim2seis Calculation

Time-lapse seismic modeling for CO2 sequestration at the Dickman Oilfield, Kansas

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