A Two-Level MCMC Based On The Distributed Gauss-Newton Method For Uncertainty Quantification

Rafiee, Javad; Reynolds, Albert C.

doi:10.3997/2214-4609.201802140

Cited by 17 publications

(1 citation statement)

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“…The model calibration methods typically involve a challenging minimization problem as this problem is usually ill-posed and the number of unknown model parameters can be very large. The ill-posedness of model calibration can be mitigated by reducing the number of parameters through an appropriate parameterization such as TSVD (Shirangi, 2011(Shirangi, , 2014Shirangi and Emerick, 2016;Bjarkason et al, 2017;Dickstein et al, 2017), ensemble-based methods (Rafiee and Reynolds, 2017;Rafiee, 2017;Rafiee and Reynolds, 2018), and PCA (Vo and Durlofsky, 2016). Durlofsky (2015, 2014) presented a differentiable PCA-based parameterization (O-PCA) that enables application of efficient gradient-based approaches for model calibration of complex channelized systems.…”

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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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