Reservoir history matching represents a crucial stage in the reservoir development process and purposes to match model predictions with various observed field data, including production, seismic, and electromagnetic d...
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Reservoir history matching represents a crucial stage in the reservoir development process and purposes to match model predictions with various observed field data, including production, seismic, and electromagnetic data. In contrast to the traditional manual approach, automatic history matching (AHM) significantly reduces the workload of reservoir engineers by automatically tuning the reservoir model parameters. AHM can be viewed as an automated solution to an inverse problem, and the selection of optimization algorithms is crucial for achieving effective model matching. However, the optimization process requires running numerous simulations. Surrogate models, achieved through simplification or approximation of the realistic model, offer a significant reduction in computational costs during the simulation process. In this paper, we provide an overview of commonly prevalent optimization algorithms and surrogate models in the AHM process, presenting the latest advancements in these methods. We analyze the strengths and limitations of these approaches and discuss the future challenges and directions of AHM, aiming to provide valuable references for further research and applications in this field.
A unique way evolutionary algorithms (EAs) are different from other search and optimization methods is their recombination operator. For real-parameter problems, it takes two or more high-performing population members...
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ISBN:
(纸本)9781728183923
A unique way evolutionary algorithms (EAs) are different from other search and optimization methods is their recombination operator. For real-parameter problems, it takes two or more high-performing population members and blends them to create one or more new solutions. Many real-parameter recombination operators have been proposed in the literature. Each operator involves at least a parameter that controls the extent of exploration (diversity) of the generated offspring population. It has been observed that different recombination operators and specific parameters produce the best performance for different problems. This fact imposes the user to use different operator and parameter combinations for every new problem. While an automated algorithm configuration method can be applied to find the best combination, in this paper, we propose an ensembled Crossover based Evolutionary algorithm (EnXEA), which considers a number of recombination operators simultaneously. Their parameter values and applies them with a probability updated adaptively in proportion to their success in creating better offspring solutions. Results on single-objective and multi-objective, constrained, and unconstrained problems indicate that EnXEA's performance is close to the best individual recombination operation for each problem. This alleviates the use of expensive parameter tuning either adaptively or manually for solving a new problem.
In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our res...
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ISBN:
(纸本)9781424441228
In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our research group was used;this provided promising results in our earlier experiments. At its best, the 1200 image of the Messidor database is classified by this detector with a sensitivity of 96%, a specificity of 51% and achieved an AUC of 0.87. As anticipated, larger microaneurysm counts are recognized with higher level of certainty. Therefore, this approach might be expected to have good performance in relation to the severity of the disease.
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