Probabilistic integration of geologic scenarios, seismic, and production data—a West Africa turbidite reservoir case study

Caers, Jef; Hoffman, Todd; Strebelle, Sebastien; Wen, Xian‐Huan

doi:10.1190/1.2184087

Cited by 18 publications

(9 citation statements)

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“…From an imaging point of view, the key sources of uncertainty are data inaccuracy and limited bandwidth, incomplete knowledge and information about the typically complex physical system under investigation, and predictive modeling (theoretical and discretization) errors. These sources of uncertainty have been treated using either deterministic or probabilistic approaches [see, e.g., DeVolder et al , 2002; Reagan et al , 2003; Caers et al , 2006; Chappell and Lancaster , 2007; Vasco , 2007; Cardiff and Kitanidis , 2009; Meju , 2009 and references therein]. A particularly difficult challenge is error estimation in the solution of partial differential equations that constitute our numerical representation of the physical system being investigated [e.g., DeVolder et al , 2002; Reagan et al , 2003].…”

Section: What Is Structure‐coupled Joint Inversion?mentioning

confidence: 99%

“…The deterministic approaches include linear sensitivity analysis [e.g., Alumbaugh and Newman , 2000; Kalscheuer and Pedersen , 2007; Kalscheuer et al , 2010] and construction of extremal solutions [e.g., Vasco , 2007; Meju , 2009]. The probabilistic approaches include density function estimation [ Snieder , 2004; Caers et al , 2006; Cardiff and Kitanidis , 2009] and covariance estimation by Monte Carlo integration [e.g., Alkhatib and Schuh , 2007]. The key question here is, can we realistically quantify uncertainty in large‐scale applications of structure‐coupled multiphysics imaging?…”

Section: What Is Structure‐coupled Joint Inversion?mentioning

confidence: 99%

“…These data are thus heterogeneous and of multiple‐spatial (i.e., micro‐ to macro‐) scale in nature. Conventionally, geologic scenarios, seismic and petroleum production data are integrated using a probabilistic approach [ Caers et al , 2006]. Some workers [ Gallardo , 2007b; Lelièvre , 2009; Lelièvre and Oldenburg , 2009] demonstrate that geological and geophysical data can also be integrated naturally in joint image reconstruction using a deterministic approach with much less computational burden.…”

Section: Integration Of Physical and Nonphysical Informationmentioning

confidence: 99%

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Structure‐coupled multiphysics imaging in geophysical sciences

Gallardo

Meju

2011

Reviews of Geophysics

147

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[1] Multiphysics imaging or data inversion is of growing importance in many branches of science and engineering. In geophysical sciences, there is a need for combining information from multiple images acquired using different imaging devices and/or modalities because of the potential for accurate predictions. The major challenges are how to combine disparate data from unrelated physical phenomena, taking into account the different spatial scales of the measurement devices, model complexities, and how to quantify the associated uncertainties. This review paper summarizes the role played by the structural gradients-based approach for coupling fundamentally different physical fields in (mainly) geophysical inversion, develops further understanding of this approach to guide newcomers to the field, and defines the main challenges and directions for future research that may be useful in other fields of science and engineering.Citation: Gallardo, L. A., and M. A. Meju (2011), Structure-coupled multiphysics imaging in geophysical sciences, Rev.

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Section: What Is Structure‐coupled Joint Inversion?mentioning

confidence: 99%

Section: What Is Structure‐coupled Joint Inversion?mentioning

confidence: 99%

Section: Integration Of Physical and Nonphysical Informationmentioning

confidence: 99%

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Structure‐coupled multiphysics imaging in geophysical sciences

Gallardo

Meju

2011

Reviews of Geophysics

147

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“…To limit the analytical and computational complexity of modeling the joint distribution over all of the observed and hidden variables, previous research in geostatistical inversion assumed that the likelihoods are localized (or quasi-localized) and that the observed data are independent and identically distributed (e.g., Caers et al, 2006;Grana, 2018;Hoffman & Caers, 2007;Larsen et al, 2006;Nawaz & Curtis, 2017;Shahraeeni & Curtis, 2011;Shahraeeni et al, 2012;Ulvmoen & Omre, 2010;Walker & Curtis, 2014a). The localized-likelihood assumption models the probabilistic relationship between the data observed at a location and the model parameters at the same location independently from other locations.…”

Section: Introductionmentioning

confidence: 99%

Rapid Discriminative Variational Bayesian Inversion of Geophysical Data for the Spatial Distribution of Geological Properties

Nawaz

Curtis

2019

JGR Solid Earth

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We present a new, fully probabilistic and nonlinear inversion method to estimate the spatial distribution of geological properties (depositional facies, diagenetic rock types, or other rock properties) from geophysical data (e.g., seismic data). Contrary to the conventional generative approach that models solution probabilities via the likelihood of observed data, our method uses a discriminative approach that directly models the posterior distribution of the geological properties given the data. This reduces the modeling effort significantly and allows machine learning algorithms such as neural networks to be deployed to solve large geophysical inference problems. We show that our method honors spatial distributions of geological parameters supplied as prior information about local geology and can be trained using supervised learning to be robust against noise present in the data as long as we can provide statistical characteristics of the noise. Exact Bayesian inference is almost always infeasible in practice because it requires normalization of the posterior distribution; this is intractable for large models and must therefore be approximated. Most existing probabilistic inversion methods use stochastic sampling (e.g., Markov chain Monte Carlo, McMC) for approximate inference. However, McMC involves the use of subjective criteria to detect convergence. We use the variational Bayes method to transform probabilistic inference into numerical optimization. This is a more efficient, deterministic alternative to McMC‐based inference for suitably structured problems. Our method thus avoids extensive sampling during inference, yet provides fully probabilistic Bayesian results, and is therefore scalable to higher dimensional problems.

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“…Many authors have worked on the integration of 422 C 2014 European Association of Geoscientists & Engineers seismic time-lapse data in the simulation of conventional reservoirs (Huang, Meister and Workman 1997;Arenas, Oldenziel and Van kruijsdijk 2001;Gosselin et al 2003;Mezghani, Langlais and Lucet 2004;Caers, Strebelle and Wen 2006;Zou, Bentley and Lines 2006;Kazemi and Stephen 2008). Limited seismic timelapse monitoring can take advantage of abundant well production history data to interpret effectively the course of reservoir production.…”

Section: Introductionmentioning

confidence: 99%

Effect of oil composition on fluid substitution in heavy oil reservoirs

Shamsa

Lines²

2014

Geophysical Prospecting

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Unlike light oils, heavy oils do not have a well‐established scheme for modelling elastic moduli from dynamic reservoir properties. One of the main challenges in the fluid substitution of heavy oils is their viscoelastic nature, which is controlled by temperature, pressure, and fluid composition. Here, we develop a framework for fluid substitution modelling that is reliable yet practical for a wide range of cold and thermal recovery scenarios in producing heavy oils and that takes into account the reservoir fluid composition, grounded on the effective‐medium theories for estimating elastic moduli of an oil–rock system. We investigate the effect of fluid composition variations on oil–rock elastic moduli with temperature changes. The fluid compositional behaviour is determined by flash calculations. Elastic moduli are then determined using the double‐porosity coherent potential approximation method and the calculated viscosity based on the fluid composition. An increase in temperature imposes two opposing mechanisms on the viscosity behaviour of a heavy‐oil sample: gas liberation, which tends to increase the viscosity, and melting, which decreases the viscosity. We demonstrate that melting dominates gas liberation, and as a result, the viscosity and, consequently, the shear modulus of the heavy oils always decrease with increasing temperature. Furthermore, it turns out that one can disregard the effects of gas in the solution when modelling the elastic moduli of heavy oils. Here, we compare oil–rock elastic moduli when the rock is saturated with fluids that have different viscosity levels. The objective is to characterize a unique relation between the temperature, the frequency, and the elastic moduli of an oil–rock system. We have proposed an approach that takes advantage of this relation to find the temperature and, consequently, the viscosity in different regions of the reservoir.

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Probabilistic integration of geologic scenarios, seismic, and production data—a West Africa turbidite reservoir case study

Cited by 18 publications

References 0 publications

Structure‐coupled multiphysics imaging in geophysical sciences

Structure‐coupled multiphysics imaging in geophysical sciences

Rapid Discriminative Variational Bayesian Inversion of Geophysical Data for the Spatial Distribution of Geological Properties

Effect of oil composition on fluid substitution in heavy oil reservoirs

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