Conditioning Truncated Pluri-Gaussian Models to Facies Observations in Ensemble-Kalman-Based Data Assimilation

Астракова, А. С.; Oliver, Dean S.

doi:10.1007/s11004-014-9532-3

Cited by 35 publications

(11 citation statements)

References 38 publications

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“…We expect the facies approach to be less effective in such cases. We assumed homogeneity within each facies, while a hierarchical facies structure, such as the one used by Astrakova and Oliver (), could be incorporated to handle the within‐facies heterogeneity when it is not negligible.…”

Section: Discussionmentioning

confidence: 99%

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Song

Dai

et al. 2019

Water Resources Research

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The facies‐based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble‐based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. We introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability‐based geostatistics. A level‐set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies‐based permeability field. The first example is a two‐dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three‐dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.

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Section: Discussionmentioning

confidence: 99%

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Song

Dai

et al. 2019

Water Resources Research

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“…Astrakova and Oliver [11] used the pseudoproperties approach for joint updating of permeability and facies variables where they considered the problem of history matching of 3D TPG models with strongly correlated hard data. The synthetic model consists of 5 different facies and 22 layers.…”

Section: Truncated Pluri-gaussianmentioning

confidence: 99%

Ensemble-based data assimilation with curvelets regularization

Zhang

Oliver

Chauris

et al. 2015

Journal of Petroleum Science and Engineering

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“…With nodes of the triangles representing the surface of a channel, Rahon et al (1997) conditioned geological bodies to production data. By parameterizing the location of the central line, the width, and the thickness of a channel all along the channel length, Bi et al (2000) and Zhang et al (2002) investigated methods of conditioning 3D stochastic channels to pressure data by use of the gradientbased optimization methods. Their methods worked quite well for a reservoir with a single channel.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al (2011Zhou et al ( , 2012aZhou et al ( , 2012b applied normal-score transform on each element of the state vector (e.g., pressure and saturation fields) before the EnKF updating step and transformed back after the updating step. Liu and Oliver (2004, 2005a, 2005b, Oliver (2008, 2011), and Astrakova and Oliver (2014) investigated methods for history matching of geologic facies by use of pluri-Gaussian simulation and EnKF. Zhao et al (2008) applied the truncated-pluri-Gaussian method to generate facies models and the EnKF to update the associated permeability field.…”

Section: Introductionmentioning

confidence: 99%

Assisted History Matching of Channelized Models by Use of Pluri-Principal-Component Analysis

et al. 2016

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Assisted history matching (AHM) of a channelized reservoir is still a very-challenging task because it is very difficult to gradually deform the discrete facies in an automated fashion, while preserving geological realism. In this paper, a pluri-principalcomponent-analysis (PCA) method, which supports PCA with a pluri-Gaussian model, is proposed to reconstruct geological and reservoir models with multiple facies. PCA extracts the major geological features from a large collection of training channelized models and generates gridblock-based properties and real-valued (i.e., noninteger-valued) facies. The real-valued facies are mapped to discrete facies indicators according to rock-type rules (RTRs) that determine the fraction of each facies and neighboring connections between different facies. Pluri-PCA preserves the main (or principal) features of both geological and geostatistical characteristics of the prior models. A new method is also proposed to automatically build the RTRs with an ensemble of training realizations. An AHM work flow is developed by integrating pluri-PCA with a derivative-free optimization algorithm. This work flow is validated on a synthetic model with four facies types and a real-field channelized model with three facies types, and it is applied to update both the facies model and the reservoir model by conditioning to production data and/or hard data. The models generated by pluri-PCA preserve the major geological/geostatistical descriptions of the original training models. This has great potential for practical applications in large-scale history matching and uncertainty quantification.

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Conditioning Truncated Pluri-Gaussian Models to Facies Observations in Ensemble-Kalman-Based Data Assimilation

Cited by 35 publications

References 38 publications

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Ensemble-based data assimilation with curvelets regularization

Assisted History Matching of Channelized Models by Use of Pluri-Principal-Component Analysis

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