Truncated conjugate gradient and improved LBFGS and TSVD for history matching

Dickstein, Flávio; Goldfeld, Paulo; Pfeiffer, Gustavo; Pinto, Renan Vicente

doi:10.1007/s10596-017-9694-4

Cited by 4 publications

(7 citation statements)

References 29 publications

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“…More details can be found in [2]. We use Gauss-Newton, so that at each step g(m(θ)) is replaced by its linearization around the current estimate θ k , i.e., g(m(θ))…”

Section: Tcgmentioning

confidence: 99%

“…Our PUNQ model displays some minor changes with respect to the original (see [2]) to accommodate for SIMPAR limitations. In our algorithm, we actually introduce a change of variables for the porosity, in order to generate an unconstrained optimization problem.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The total time divided by the number of successes is comparable for these two schemes. For others experiments in which TCG had clearly the best overall performance, see [2]. …”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Most recently, we have proposed the Truncated Conjugate Gradient 2 (TCG) algorithm for HM [2]. As we describe in this work, TCG is based on the GaussNewton method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Truncated Conjugate Gradient Method for History Matching in Reservoir Simulation

Dickstein¹,

Goldfeld²,

Pfeiffer³

et al. 2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…More details can be found in [2]. We use Gauss-Newton, so that at each step g(m(θ)) is replaced by its linearization around the current estimate θ k , i.e., g(m(θ))…”

Section: Tcgmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

“…The total time divided by the number of successes is comparable for these two schemes. For others experiments in which TCG had clearly the best overall performance, see [2]. …”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Most recently, we have proposed the Truncated Conjugate Gradient 2 (TCG) algorithm for HM [2]. As we describe in this work, TCG is based on the GaussNewton method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Truncated Conjugate Gradient Method for History Matching in Reservoir Simulation

Dickstein¹,

Goldfeld²,

Pfeiffer³

et al. 2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

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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A learning-from-data approach with soft clustering and path relinking to the history-matching problem

Cavalcante

Souza

Maschio

et al. 2021

J Petrol Explor Prod Technol

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History matching is an important reservoir engineering process whereby the values of uncertain attributes of a reservoir model are changed to find models that have a better chance of reproducing the performance of an actual reservoir. As a typical inverse and ill-posed problem, different combinations of reservoir uncertain attributes lead to equally well-matched models and the success of a history-matching approach is usually measured in terms of its ability to efficiently find multiple history-matched models inside the search space defined by the parameterization of the problem (multiple-matched models have a higher chance of better representing the reservoir performance forecast). While studies on history-matching approaches have produced remarkable progress over the last two decades, given the uniqueness of each reservoir’s history-matching problem, no strategy is proven effective for all cases, and finding alternative, efficient, and effective history-matching methodologies is still a research challenge. In this work, we introduce a learning-from-data approach with path relinking and soft clustering to the history-matching problem. The proposed algorithm is designed to learn the patterns of input attributes that are associated with good matching quality from the set of available solutions, and has two stages that handle different types of reservoir uncertain attributes. In each stage, the algorithm evaluates the data of all-available solutions continuously and, based on the acquired information, dynamically decides what needs to be changed, where the changes shall take place, and how such changes will occur in order to generate new (and hopefully better) solutions. We validate our approach using the UNISIM-I-H benchmark, a complex synthetic case constructed with real data from the Namorado Field, Campos Basin, Brazil. Experimental results indicate the potential of the proposed approach in finding models with significantly better history-matching quality. Considering a global misfit quality metric, the final best solutions found by our approach are up to 77% better than the corresponding initial best solutions in the datasets used in the experiments. Moreover, compared with previous work for the same benchmark, the proposed learning-from-data approach is competitive regarding the quality of solutions found and, above all, it offers a significant reduction (up to 30 × less) in the number of simulations.

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Truncated conjugate gradient and improved LBFGS and TSVD for history matching

Cited by 4 publications

References 29 publications

Truncated Conjugate Gradient Method for History Matching in Reservoir Simulation

Truncated Conjugate Gradient Method for History Matching in Reservoir Simulation

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

A learning-from-data approach with soft clustering and path relinking to the history-matching problem

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