Value of information in closed-loop reservoir management

Barros, E.G.D.; Hof, van den Pmj Paul

doi:10.1007/s10596-015-9509-4

Cited by 34 publications

(15 citation statements)

References 26 publications

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“…Filtering method has been widely used in the industry, together with proxies to obtain posterior distribution (Bhark and Dehghani 2014). Theoretically, if we have a reliable model for the distribution and the correlation of the data error (which includes measurement error and modeling error), a corresponding acceptance probability can be derived with the Bayes' rule.…”

Section: Methodsmentioning

confidence: 99%

“…The expected prediction error (difference between the history-matched model and the assumed true model) of the plume size defines the effectiveness of the sensor location. More recently, Barros et al (2015) proposed to perform the multiple history-matching runs with the ensemble Kalman filter (EnKF) to obtain the posterior distribution and to subsequently quantify decision-based value of information (VOI). However, in their method, the number of simulations grows quadratically with respect to the size of the ensemble.…”

Section: Introductionmentioning

confidence: 99%

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Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Xie

Sarma

et al. 2016

SPE Journal

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Data-acquisition programs, such as surveillance and pilot, play an important role in reservoir management, and are crucial for minimizing subsurface risks and improving decision quality. Optimal design of the data-acquisition plan requires predicting the performance (e.g., in terms of the expected amount of uncertainty reduction in an objective function) of a given design before it is implemented. Because the data from the acquisition program are uncertain at the time of the analysis, multiple history-matching runs are required for different plausible realizations of the observed data to evaluate the expected effectiveness of the program in reducing uncertainty. As such, the computational cost may be prohibitive because the number of reservoir simulations needed for the multiple history-matching runs would be substantial. This paper proposes a framework on the basis of proxies and rejection sampling (filtering) to perform the multiple historymatching runs with a manageable number of reservoir simulations. The work flow proposed does not depend on the linear Gaussian assumption that is a common, yet questionable, assumption in existing methods. The work flow also enables both qualitative and quantitative analysis of a surveillance plan. Qualitatively, heavy-hitter alignment analysis for the objective function and the observed data provides actionable measures for screening different surveillance designs. Quantitatively, the evaluation of expected uncertainty reduction from different surveillance plans allows for optimal design and selection of surveillance plans.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Xie

Sarma

et al. 2016

SPE Journal

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“…Hanea et al (2017), however, did not present a procedure for performance assessment (as TruMAP) and used too few cases. Barros et al (2016) also applied closed-loop optimization for multiple true-model cases, though in a different context, i.e., quantifying value of information in CLRM.…”

Section: Trumap: True-model-based Assessment Of Performancementioning

confidence: 99%

Closed-loop field development with multipoint geostatistics and statistical performance assessment

Shirangi

2019

Journal of Computational Physics

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Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of subsurface resources. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. This work introduces a new CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. For performance assessment by TruMAP, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%-80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate the effectiveness of performing multiple steps of closed-loop optimization. This massive computational experiment involved about 9.5 million reservoir simulation runs that took about 320,000 CPU hours.

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“…For this reason, the application of the CLRM framework in combination with UQ can be extremely computationally expensive. Workflows to assess the value of information (VOI) in CLRM during the field development planning (FDP) phase require even more simulations, which makes real-field applications unfeasible (Barros et al, 2016). Therefore, we look for alternatives to reduce this computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…Eidsvik et al (2015) have envisaged more sophisticated design of experiments to be a promising alternative to alleviate the computational costs of VOI assessment workflows. This paper explores the use of clustering techniques to select a subset of representative model realizations to achieve what Eidsvik et al (2015) suggest within the workflow for VOI assessment proposed by Barros et al (2016). In the Background section we briefly recap our previously proposed methodology for VOI assessment in CLRM and review some previous work on cluster analysis.…”

Section: Introductionmentioning

confidence: 99%

Clustering Techniques for Value-of-information Assessment in Closed-loop Reservoir Management

Barros

Yap

Insuasty³

et al. 2016

Proceedings

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Closed-loop reservoir management (CLRM) is a combination of life-cycle optimization and computerassisted history matching. The application of the CLRM framework to real field cases can be computationally demanding. An even higher computational load results from procedures to assess the value of information (VOI) in CLRM. Such procedures, which are performed prior to field operation, i.e. during the field development planning (FDP) phase, require extreme amounts of simulations. Therefore, we look for alternatives to reduce this computational cost. In particular we compare various clustering techniques to select a limited number of representative members from an ensemble of reservoir models. Using K-means clustering, multi-dimensional scaling and tensor decomposition techniques, we test the effectiveness of different dissimilarity measures such as distance in parameter space, distance in terms of flow patterns and distance in optimal sets of controls. As a first step towards large-scale application we apply several of these measures to a VOI-CLRM exercise using a simple 2D reservoir model which results in a reduction of the necessary number of forward reservoir simulations from millions to thousands.

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Value of information in closed-loop reservoir management

Cited by 34 publications

References 26 publications

Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Closed-loop field development with multipoint geostatistics and statistical performance assessment

Clustering Techniques for Value-of-information Assessment in Closed-loop Reservoir Management

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