4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Socio-Inspired Multi-Cohort Intelligence and Teaching-Learning-Based Optimization for Hydraulic Fracturing Parameters Design in Tight Formations

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Hydraulic fracturing is one of the revolutionary technologies widely applied to develop tight hydrocarbon reservoirs. Moreover, hydraulic fracture design optimization is an essential step to optimize production from tight reservoirs. This study presents the implementation of three new socio-inspired algorithms on hydraulic fracturing optimization. The work integrates reservoir simulation, artificial neural networks, and preceding optimization algorithms to attain the optimized fractures. For this study, a tight gas production dataset is initially generated numerically for a defined set of the fracture half-length, fracture height, fracture width, fracture conductivity, and the number of fractures’ values. Secondly, the generated dataset is trained through a neural network to predict the effects of preceding parameters on gas production. Lastly, three new socio-inspired algorithms including cohort intelligence (CI), multi-cohort intelligence (multi-CI), and teaching learning-based optimization (TLBO) are applied to the regressor output to obtain optimized gas production performance with the combination of optimum fracture design parameters. The results are then compared with the traditionally used optimizers including particle swarm optimization (PSO) and genetic algorithm (GA). The results demonstrated that the multi-CI and TLBO converge at the global best position more often with a success rate of at least 95% as compared to CI, PSO, and GA. Moreover, the CI, PSO, and GA are found to stuck many times at the local maximum. This concludes that the multi-CI and TLBO are good alternatives to PSO and GA considering their high performance in determining the optimum fracture design parameters in comparison.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          :{unav)

          Journal of Global Optimization, 11(4), 341-359
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Genetic Algorithms

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

                Bookmark

                Author and article information

                Journal
                Journal of Energy Resources Technology
                ASME International
                0195-0738
                1528-8994
                July 01 2022
                July 01 2022
                September 03 2021
                : 144
                : 7
                Affiliations
                [1 ]Department of Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS 66045
                Article
                10.1115/1.4052182
                5b13c54e-e412-4cda-af79-2519be798385
                © 2021

                https://www.asme.org/publications-submissions/publishing-information/legal-policies

                History

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content82

                Cited by9

                Most referenced authors165