Recently, the team of Zhang Kai has made new progress in the field of reservoir production optimization, and the related research has been published in the Journal of Petroleum Science and Engineering. The paper is titled "Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization".
Innovation: Geological properties are the main source of uncertainty in reservoir numerical modeling and geological parameters such as permeability are the key parameters of automatic history matching. However, geological parameters usually are high-dimensional grid-based parameters with wide spatial distribution, and it is difficult to eliminate the uncertainty completely with sparse observation data, so the solving of history matching inevitably faces the problem of multiple solutions. In this study, a novel inversion algorithm with distributed search capability is proposed based on the niching technology enhanced differential evolution algorithm, and the local-surrogate assisted optimization method is adopted to accelerate the search, and the control parameters of the algorithm are adjusted adaptively with the optimization search process through the parameter adaptive control strategy.
Abstract: History matching is a typical inverse problem that adjusts the uncertainty parameters of the reservoir numerical model with limited dynamic response data. In most situations, various parameter combinations can result in the same data fit, termed non-uniqueness of inversion. It is desirable to find as many global or local optima as possible in a single optimization run, which may help to reveal the distribution of the uncertainty parameters in the posterior space, which is particularly important for robust optimization, risk analysis, and decision making in reservoir management. However, many factors, such as the nonlinearity of inversion problems and the time-consuming numerical simulation, limit the performance of most existing inverse algorithms. In this paper, we propose a novel data-driven niching differential evolution algorithm with adaptive parameter control for non-uniqueness of inversion, called DNDE-APC. On the basis of the differential evolution (DE) framework, the proposed algorithm integrates the clustering approach, niching technique, and local surrogate assistant method, which is designed to balance exploration and convergence in solving the multimodal inverse problems. Empirical studies on three benchmark problems demonstrate that the proposed algorithm is able to locate multiple solutions for complex multimodal problems on a limited computational budget. Integrated with convolutional variational autoencoder (CVAE) for parameterization of the high-dimensional uncertainty parameters, a history matching workflow is developed. The effectiveness of the proposed workflow is validated with heterogeneous waterflooding reservoir case studies. By analyzing the fitting and prediction of production data, history-matched realizations, the distribution of inversion parameters, and uncertainty quantization of forecasts, the results indicate that the new method can effectively tackle the non-uniqueness of inversion and the prediction result is more robust.
SPE Journal covers novel theories and emerging concepts (not including review articles or multi-part articles) spanning all aspects of engineering for oil and gas exploration and production, including drilling and completions, geomechanics, production and facilities, oilfield chemistry, CO2 sequestration and injection, reservoir evaluation and engineering, numerical simulation, data analytics, economics and externalities including health, safety, environment, and sustainability. The latest impact factor of the journal is 4.346, and the average impact factor IF in the past 3 years is 3.646. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 3.
Paper link:
doi: https://doi.org/10.2118/205014-PA
Citation:
Xiaopeng, Kai Zhang, Liming Zhang, et al. Data-Driven Niching Differential Evolution with Adaptive Parameters Control for History Matching and Uncertainty Quantification [J]. SPE Journal, 2021, 26(02): 993-1010.
