Estimation of pressure-saturation changes for unconsolidated reservoir rocks with highVP/VSratio

Bhakta, Tuhin; Landrø, Martin

doi:10.1190/geo2013-0434.1

Cited by 33 publications

(18 citation statements)

References 35 publications

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“…In the Biot theory, the effective stress is not explicitly expressed. However, for time-lapse applications, if we record a change in P-wave velocity, it can be related to a change in fluid saturation as well as a change in pore pressure (Bhakta and Landrø, 2014).…”

Section: Exact Input Data Errors On Input Datamentioning

confidence: 99%

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

2016

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The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

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Section: Exact Input Data Errors On Input Datamentioning

confidence: 99%

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

2016

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“…Because we have considered only the gas saturation change, our results are probably overestimated. Nevertheless, we can assume that the pore pressure effect is low because our estimation is already lower than those of Langseth and Landrø (2012) and Bhakta and Landrø (2014). Moreover, we think that the saturation in this highly porous sand layer is likely uniform, and promote the estimation using Brie equation (Figure 13), that bring up the estimation of 3%-4% of gas.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the uncertainties on FWI results, on input rock physics parameters (K f of gas and water may have influence on gas saturation estimation), and on the rock physics models themselves (the gas saturation value may reach the Voigt upper bound and be approximately 20%) can be the cause of an underestimation of the gas saturation in the sand layer. Indeed, pressure-saturation changes estimation of Bhakta and Landrø (2014) (using AVO data for the same blowout case) gives gas saturation values between 10% and 45% and pore pressure changes of the order of 1.6-2.8 MPa. Using 2D time-lapse seismic data coupled with reservoir simulation, Langseth and Landrø (2012) estimate that the gas saturation is approximately 35%-40% and the pore pressure approximately 9 MPa.…”

Section: Discussionmentioning

confidence: 99%

“…The leakage in the sand layer causes the increase of the saturation and the pore pressure and both effects are combined to decrease the P-wave velocity (the pore pressure increase causes a lower coupling between grains and a decrease in effective stress [Bhakta and Landrø, 2014]). Because we have considered only the gas saturation change, our results are probably overestimated.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of rock physics properties from seismic attributes — Part 2: Applications

et al. 2016

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The estimation of quantitative rock physics properties is of great importance for reservoir characterization and monitoring in CO 2 storage or enhanced oil recovery as an example. We have combined the high-resolution results of full-waveform inversion (FWI) methods with rock physics inversion. Because we consider a generic and dynamic rock physics model, our method is applicable to most kinds of rocks for a wide range of frequencies. The first step allows determination of viscoelastic effective properties, i.e., quantitative seismic attributes, whereas the rock physics inversion estimates rock physics properties (porosity, solid frame moduli, fluid phase properties, or saturation). This two-step workflow is applied to time-lapse synthetic and field cases. The sensitivity tests that we had previously carried out showed that it can be crucial to use multiparameter inputs to accurately recover fluid saturations and fluid properties. However, due to the limited data availability and difficulties in getting reliable multiparameter FWI results, we are limited to acoustic FWI results. The synthetic tests are conclusive even if they are favorable cases. For the first time-lapse fluid substitution synthetic case, we first characterize the rock frame parameters on the baseline model using P-wave velocity estimations obtained by acoustic FWI. Then, we obtain an accurate estimation of fluid bulk modulus from the time-lapse P-wave velocity. In the Marmousi synthetic case, the rock frame properties are accurately recovered for the baseline model, whereas the gas saturation change in the monitor model is not estimated correctly. On the field data example (time-lapse monitoring of an underground blowout in the North Sea), the estimation of rock frame properties gives results on a relatively narrow range, and we use this estimation as a starting model for the gas saturation inversion. We have found that the estimation of the gas saturation is not accurate enough, and the use of attenuation data is then required. However, the uncertainty on the estimation of baseline rock frame properties is not critical to monitor gas saturation changes.

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“…An example of stochastic inversion approach has been proposed in Veire, Borgos and Landrø (2006). Improvements to this formulation have been subsequently proposed by Meadows (2001), Trani et al (2011 and Bhakta and Landrø (2014). Despite the common use of the formulation proposed by Landrø (2001), the method has some limitations.…”

Section: Introductionmentioning

confidence: 99%

Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies

Lang

Grana

2019

Geophysical Prospecting

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Time‐lapse seismic data are generally used to monitor the changes in dynamic reservoir properties such as fluid saturation and pore or effective pressure. Changes in saturation and pressure due to hydrocarbon production usually cause changes in the seismic velocities and as a consequence changes in seismic amplitudes and travel times. This work proposes a new rock physics model to describe the relation between saturation‐pressure changes and seismic changes and a probabilistic workflow to quantify the changes in saturation and pressure from time‐lapse seismic changes. In the first part of this work, we propose a new quadratic approximation of the rock physics model. The novelty of the proposed formulation is that the coefficients of the model parameters (i.e. the saturation‐pressure changes) are functions of the porosity, initial saturation and initial pressure. The improvements in the results of the forward model are shown through some illustrative examples. In the second part of the work, we present a Bayesian inversion approach for saturation‐pressure 4D inversion in which we adopt the new formulation of the rock physics approximation. The inversion results are validated using synthetic pseudo‐logs and a 3D reservoir model for CO2 sequestration.

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Estimation of pressure-saturation changes for unconsolidated reservoir rocks with highV_P/V_Sratio

Cited by 33 publications

References 35 publications

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Estimation of rock physics properties from seismic attributes — Part 2: Applications

Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies

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