A general method to select representative models for decision making and optimization under uncertainty

Shirangi, Mehrdad G.; Durlofsky, Louis J.

doi:10.1016/j.cageo.2016.08.002

Cited by 68 publications

(27 citation statements)

References 42 publications

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“…Optimization under geological uncertainty is another important aspect that should be addressed. In this case realization selection, using procedures described in [52], could be applied. The twostep ST may be particularly useful within the context of field development optimization, where well number, locations, types and controls must be determined.…”

Section: Discussionmentioning

confidence: 99%

Well control optimization using a two-step surrogate treatment

Brito

Durlofsky

2020

Journal of Petroleum Science and Engineering

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Large numbers of flow simulations are typically required for the determination of optimal well settings. These simulations are often computationally demanding, which poses challenges for the optimizations. In this paper we present a new two-step surrogate treatment (ST) that reduces the computational expense associated with well control optimization. The method is applicable for oil production via waterflood, with well rates optimized at a single control period. The two-step ST entails two separate optimizations, which can both be performed very efficiently. In the first optimization, optimal well-rate ratios (i.e., the fraction of total injection or production associated with each well) are determined such that a measure of velocity variability over the field is minimized, leading to more uniform sweep. In the second step, overall injection and production rates are determined. The flow physics in the first step is highly simplified, while the actual physical system is simulated in the second step. Near-globally-optimal results can be determined in both cases, as the first optimization is posed as a QP problem, and the second step entails just a single optimization variable. Under full parallelization, the overall elapsed time for the ST corresponds to the runtime for 1-2 full-order simulations. Results are presented for multiple well configurations, for 2D and 3D channelized models, and comparisons with formal optimization procedures (mesh adaptive direct search or MADS, and an adjoint-gradient method) are conducted. Three different fluid mobility ratios (M = 1, 3 and 5) are considered. Optimization results demonstrate that the two-step ST provides results in reasonable agreement with those from MADS and adjoint-gradient methods, with speedups of 5× or more. We also show that the ST is applicable in the inner-loop in field development optimization, where it will be especially useful since many different well configurations must be evaluated.

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Section: Discussionmentioning

confidence: 99%

Well control optimization using a two-step surrogate treatment

Brito

Durlofsky

2020

Journal of Petroleum Science and Engineering

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“…The models are selected such that two of them correspond to P10 and P90 (for n r ≤ 10) or P5 and P95 (for n r > 10), and the rest of n r − 2 models correspond to even increments in NPV percentile (e.g., for n r = 5, the models correspond to P10, P30, P50, P70, and P90). Other selection methods such as equally weighted flow-based and permeability-based clustering (Shirangi and Durlofsky, 2016) could be applied and tested here 1 .…”

Section: Optimization With Sample Validation and Selection Of Represementioning

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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“…This study also increases the computational efficiency of the CLFD process by using representative models of the uncertain system. Techniques to select representative models were the focus of recent research (Jiang et al, 2016;Meira et al, 2016Meira et al, , 2017Shirangi and Durlofsky, 2016). This work applies a method developed in related research (Meira et al, 2016(Meira et al, , 2017 to reduce the number of scenarios for production strategy selection and optimization.…”

Section: Introductionmentioning

confidence: 99%

Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

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A general method to select representative models for decision making and optimization under uncertainty

Cited by 68 publications

References 42 publications

Well control optimization using a two-step surrogate treatment

Well control optimization using a two-step surrogate treatment

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

Model-based decision analysis applied to petroleum field development and management

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