Fast geostatistical stochastic inversion in a stratigraphic grid

Escobar, I.; Williamson, Paul; Cherrett, Adam; Doyen, P.M.; Bornard, R.; Moyen, R.; Crozat, T.

doi:10.1190/1.2369943

Cited by 24 publications

(7 citation statements)

References 7 publications

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“…The method applied uses a Bayesian framework and a linearized AVA (Amplitude Versus Angle) model to calculate a joint posterior distribution for Pwave (Ip) and S-wave (Is) impedances (Escobar et al 2006). …”

Section: Pre-stack Stochastic Inversionmentioning

confidence: 99%

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

et al. 2014

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This paper describes a methodology to characterize lithology variations impacting the porosity distribution in a carbonate reservoir. We performed a stochastic seismic inversion to obtain multiple realizations of elastic properties discriminating between the target lithologies. These multi-realizations were then used in a supervised lithoseismic classification for obtaining cubes of occurrence probability of lithologies. Finally, a pseudo porosity cube is obtained using a linear combination between the acoustic impedance and the lithology probabilities. The entire workflow is performed in a stratigraphic grid layeringconsistent with geomodeling grid. Such capability is important for further use of the results as 3D constraint during the geomodel infilling. Lithology probability cubes show proportions in agreement with vertical facies proportions observed from well data.

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Section: Pre-stack Stochastic Inversionmentioning

confidence: 99%

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

et al. 2014

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“…been recast as geostatistical stochastic AVA inversions to evolve toward automation of the processing and more rigorous model appraisal (Ma, 2002;Hicks and Williamson, 2003;Veeken and Silva, 2004;Walia et al, 2004;Escobar et al, 2006). Unlike stratigraphic inversion used in reservoir characterization, the AVA analysis is directly embedded in the depth migration process in our approach and the stochastic inversion is applied to the depth-migrated image in the poststack domain.…”

Section: R24mentioning

confidence: 99%

Joint ray + Born least-squares migration and simulated annealing optimization for target-oriented quantitative seismic imaging

et al. 2011

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A seismic processing workflow based on iterative ray þ Born migration/inversion and target-oriented postprocessing of the migrated image is developed for fine-scale quantitative characterization of reflectors. The first step of the workflow involves linear iterations of the ray þ Born migration/inversion. The output of the first step is a true-amplitude migrated image parameterized by velocity perturbations. In a second step, postprocessing of the migrated image is performed through a random search with a very-fast simulated annealing (VFSA) algorithm. The forward problem of the global optimization is a simple convolutional model that linearly relates a vertical profile of the band-limited migrated image after depth-to-time conversion to a 1D velocity model composed of a stack of homogeneous layers of arbitrary velocity and thickness. The aim of the postprocessing is to eliminate the limited bandwidth effects of the source from the migrated image for resolution improvement and enhanced geological interpretation of selected targets. The global optimization approach allows for uncertainty analysis required by the intrinsic nonuniqueness of the velocity model output by the postprocessing. The relevance of the convolutional model when applied to the output of the ray þ Born migrated inversion is first illustrated with a one-layer model. The accuracy and the robustness of the workflow to image geologically complicated models are then illustrated with an application to the synthetic Marmousi model. Some practical issues (e.g., the source wavelet estimate and the scaling of the migrated image required by the VFSA optimization) are discussed with an application to a 2D real seismic multichannel reflection data set collected in the Gulf of Guayaquil (Ecuador). The postprocessing is applied to derive the fine-scale velocity structure of a décollement zone on top of the subduction channel. The postprocessing allows for mapping structural variations along different segments of the décollement, which can be associated with changes in fluid content and porosity.

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“…Stochastic inversion is considered a better option than deterministic inversion to generate multiple elastic property models which are also useful for uncertainty analysis [13]. In this study, a Bayesian framework is used for inversion [6] and [12]. The basic philosophy of stochastic inversion is to generate multiple realizations of elastic properties with high-frequency content that are consistent with both seismic amplitude and well-derived data or geological knowledge.…”

Section: Benefits Of Stochastic Over Deterministic Inversionmentioning

confidence: 99%

Ranking of Stochastic Seismic Inversion Realizations Using Streamline Simulations

Virk

Muneeb²

2011

All Days

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Because stochastic seismic inversion generates a large number of elastic impedance realizations, only a few of these realizations must be selected for more rigorous geological interpretation and reservoir modelling. Static sand body volume (SBV) and SBV geometrically connected to the wells are generally used as the ranking measures. However, this approach does not take into account the dynamic flow field and the impact of reservoir heterogeneity and petrophysical properties on the fluid flow. Hence, this approach under or overestimates the P10, P50 & P90 ranking most of the time and invalidates the field development studies in turn. The main goal of this study is to prove the concept of using dynamic measures to rank the seismic inversion realizations and develop a ranking work flow. Streamline simulations, because of its generic option of reduced flow physics provides a faster way to run hundred of millions cell realizations, enabling a quick ranking based on dynamic measures. Typical seismic inversion models consists of several million cells (6 million in this case), and flow simulation over models of this size is computationally extensive. To reach reasonable runtimes, the size of the model is reduced; either by extracting a sector model (2 million cells sector in this case), or by decimating/up-scaling the full field model (1.7 million cells in this case). Tracer simulations over both of the reduced size models are compared with the full field tracer simulation model. The upscaled model is shown to be a closer approximation to the full field model. Finally, the tracer upscaled model is compared with a physically more realistic waterflood upscaled simulation model and is shown as a valid approximation. The sensitivity of various dynamic parameters to connectivity variations is analyzed to determine which parameter is the best suited ranking measure. Due to significant variability in spatial sand bodies distribution and connectivity between the sand bodies across the realizations, cumulative oil recovery (COR) has proved to be a much better ranking measure in comparison to the recovery factor (RF) . The static and dynamic ranking measures are compared. The difference between SBV or connected sand body volume (CSBV) and COR rankings highlights the importance of ranking based on dynamic measures. Additionally, the factors which can generally influence the ranking based on dynamic measure (COR) are determined and it is concluded that the changes in ranking will be dependent on the CSBV and petrophysical properties of the connected sand bodies across the realizations. Finally, ranking based on the best ranking measure as well on a combination of COR, STOIIP and RF is done and individual realizations (P10, P50 and P90) are selected in order to cover the range of uncertainty in spatial distribution and volumes of sand bodies. This uncertainty range can be translated into economic uncertainty for the determined optimum number of wells and distances. The approach defined in this paper to rank and select the P10, P50 & ...

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Fast geostatistical stochastic inversion in a stratigraphic grid

Cited by 24 publications

References 7 publications

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

Joint ray + Born least-squares migration and simulated annealing optimization for target-oriented quantitative seismic imaging

Ranking of Stochastic Seismic Inversion Realizations Using Streamline Simulations

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