Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review

Bosch, Miguel; Mukerji, Tapan; González, Ezequiel F.

doi:10.1190/1.3478209

Cited by 413 publications

(166 citation statements)

References 76 publications

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“…Stochastic inversion integrates seismic data, well data, and geologic information and produces a series of realizations of target reservoir attributes with high-frequency components (Bosch et al, 2010). The multiple realizations from stochastic inversion provide a basis for quantifying and assessing the uncertainty.…”

Section: Appendix a A Fourier Integral Methods For Stochastic Inversionmentioning

confidence: 99%

Seismic characterization of a carbonate reservoir in Tarim Basin

Liu

Wang

2017

GEOPHYSICS

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Seismic characterization of carbonate reservoirs is a challenging task for geophysicists because of their special depositional environment and complex interior structures. We developed a case study of the seismic characterization of a karstified carbonate reservoir in the Tarim Basin, western China. The characterization procedure is sequential and includes fault and fracture detection, seismic facies classification, seismic impedance inversion, and lithofacies classification. We presented a dipsteered coherence algorithm for detecting faults and karst fractures in the carbonate reservoir. Incorporating the dip information improves the performance and robustness. We applied normalized seismic segments, rather than the amplitude values, as the input to seismic facies classification, so as to reduce the impact of strong amplitudes, such as karst fractures, and to enable the analysis of weak amplitudes in the background strata. For the impedance inversion, we adopted a Fourier integral method for fast simulation in the stochastic inversion in this karstified carbonate reservoir. The algorithm honors the lateral variation based on the seismic trace similarity, instead of the lateral variogram that is commonly used in stochastic inversion. We conducted lithofacies classification, in which we used seismic coherence as a prior knowledge, so as to honor the fracture-associated local lithofacies with dolomitization and to distinguish it from limestone without dolomitization. Based on reservoir characterization described above, we determined three drilling wells for potential oil/gas exploration.

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Section: Appendix a A Fourier Integral Methods For Stochastic Inversionmentioning

confidence: 99%

Seismic characterization of a carbonate reservoir in Tarim Basin

Liu

Wang

2017

GEOPHYSICS

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“…Within the deterministic framework, the sparse-spike and model-based methodologies are the most widespread inversion techniques among the geophysical community (Russel, 1988;Bosch et al, 2010). The uncertainty assessment of deterministic solutions is limited because it is represented by a linearization problem around least-squares inverse solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a variable degree of uncertainty associated with any inverted subsurface AI and EI models. This uncertainty needs to be assessed, not neglected, during the interpretation of these inverse models (Bosch et al, 2010;Tompkins et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Because the assumptions within this framework are less strict when compared with the Bayesian linearized assumptions, the uncertainty space is considered to be more extensively sampled. On the other hand, one loses the mathematical tractability of the inverse solution, increasing the computational costs involved (Tarantola, 2005;Bosch et al, 2010). Iterative stochastic seismic inversion methodologies (Bortoli et al, 1992;Haas and Dubrule, 1994;Soares et al, 2007;González et al, 2008;Grana et al, 2012;Nunes et al, 2012) solve the seismic inverse problem recurring to stochastic sequential simulations and cosimulations algorithms (Deutsch and Journel, 1998) as the perturbation technique of the elastic models at each iteration.…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional scaling for the evaluation of a geostatistical seismic elastic inversion methodology

et al. 2014

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Due to the nature of seismic inversion problems, there are multiple possible solutions that can equally fit the observed seismic data while diverging from the real subsurface model. Consequently, it is important to assess how inverse-impedance models are converging toward the real subsurface model. For this purpose, we evaluated a new methodology to combine the multidimensional scaling (MDS) technique with an iterative geostatistical elastic seismic inversion algorithm. The geostatistical inversion algorithm inverted partial angle stacks directly for acoustic and elastic impedance (AI and EI) models. It was based on a genetic algorithm in which the model perturbation at each iteration was performed recurring to stochastic sequential simulation. To assess the reliability and convergence of the inverted models at each step, the simulated models can be projected in a metric space computed by MDS. This projection allowed distinguishing similar from variable models and assessing the convergence of inverted models toward the real impedance ones. The geostatistical inversion results of a synthetic data set, in which the real AI and EI models are known, were plotted in this metric space along with the known impedance models. We applied the same principle to a real data set using a crossvalidation technique. These examples revealed that the MDS is a valuable tool to evaluate the convergence of the inverse methodology and the impedance model variability among each iteration of the inversion process. Particularly for the geostatistical inversion algorithm we evaluated, it retrieves reliable impedance models while still producing a set of simulated models with considerable variability.

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“…In the latter case, understanding the structure and properties of the subsurface determines where to explore for hydrocarbon resources and how to effectively manage them. Seismic inversion has proven to be a key method to better understand the subsurface (e.g., Woodward et al, 2008;Bosch et al, 2010), returning high-resolution images. Nevertheless, in the context of exploration geophysics, inverting seismic data for only elastic properties is often not sufficient because the reservoir properties of interest are usually lithologies, fluid saturation, porosity, and other physical quantities.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo reservoir analysis combining seismic reflection data and informed priors

et al. 2015

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Determination of a petroleum reservoir structure and rock bulk properties relies extensively on inference from reflection seismology. However, classic deterministic methods to invert seismic data for reservoir properties suffer from some limitations, among which are the difficulty of handling complex, possibly nonlinear forward models, and the lack of robust uncertainty estimations. To overcome these limitations, we studied a methodology to invert seismic reflection data in the framework of the probabilistic approach to inverse problems, using a Markov chain Monte Carlo (McMC) algorithm with the goal to directly infer the rock facies and porosity of a target reservoir zone. We thus combined a rock-physics model with seismic data in a single inversion algorithm. For large data sets, the McMC method may become computationally impractical, so we relied on multiple-point-based a priori information to quantify geologically plausible models. We tested this methodology on a synthetic reservoir model. The solution of the inverse problem was then represented by a collection of facies and porosity reservoir models, which were samples of the posterior distribution. The final product included probability maps of the reservoir properties in obtained by performing statistical analysis on the collection of solutions.

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Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review

Cited by 413 publications

References 76 publications

Seismic characterization of a carbonate reservoir in Tarim Basin

Seismic characterization of a carbonate reservoir in Tarim Basin

Multidimensional scaling for the evaluation of a geostatistical seismic elastic inversion methodology

Monte Carlo reservoir analysis combining seismic reflection data and informed priors

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