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A method for developing and calibrating optimization techniques for oil production management strategy applications

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Abstract

The hydrocarbon extraction process is complex and involves numerous design variables and mitigating risk. Numerous time-consuming simulations are required to maximize objective functions such as NPV from a particular field while contemplating a significant representation of uncertainty scenarios and various production strategies. Production strategies searches may result in a high-dimensional search space which can yield sub-optimal reservoir economical exploration. As a solution, appropriate optimization algorithms selection and tuning may provide good solutions with lesser simulations. This paper presents a methodology to calibrate, develop, and select optimization algorithms for oil production strategy applications while quantifying the dimension and optimum location effects. Global optimum location altered the best method to be selected. It presents a novel algorithm (ASLHC) and a modification of the Nelder-Mead method (NMNS) to improve its high dimensionality performance. Performances of six pre-calibrated techniques were compared using novel normalized mathematical functions. Optimizations were limited to a 500 evaluation functions computational budget. The PSO, ASLHC, NMNS, and IDLHC were selected and implemented to perform production strategy improvements regarding two parameterizations of the reservoir management variables for a real reservoir model with restricted platform. Results showed the implemented algorithms successfully improved NPV by at least 8% at each of the 24 real-case optimizations. After upscaling the selected techniques for a 115 variable parameterization, the NMNS and IDLHC demonstrated good resilience against local convergence and each technique kept improving during all iterations of the process. An optimization method recommendation chart is presented based on the computational budget of the application.

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Data Availability

The authors declare that the data supporting the findings of this study are available within the paper. Its data files are displayed for public access under the following address: https://data.world/ldanes/computational-geosciences-danes-avansi-schiozer-2023

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Acknowledgements

The authors want to acknowledge Shell Brazil for sponsoring this work, conducted in association with the ongoing project registered under ANP number 19061-1 as “Desenvolvimento de Uma Abordagem Para Construção de Modelos Multifidelidade para Reduzir Incertezas e Melhorar Previsão de Produção” (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil, under the ANP R &D levy as “Commitment to Research and Development Investments.” The authors also thank UNISIM, DE-FEM-UNICAMP and CEPETRO for supporting this work, and CMG for the access to software licenses crucial for performing simulations and analysis.

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Authors and Affiliations

  1. Center for Petroleum Studies, University of Campinas, R. Mendeleyev, 200, Campinas, 13083-860, SP, Brazil

    Leandro H. Danes, Guilherme D. Avansi & Denis J. Schiozer

  2. Department of Energy, School of Mechanical Engineering, University of Campinas, R. Cora Coralina, 350, Campinas, 13083-896, SP, Brazil

    Guilherme D. Avansi & Denis J. Schiozer

Authors
  1. Leandro H. Danes
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  2. Guilherme D. Avansi
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  3. Denis J. Schiozer
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Correspondence to Leandro H. Danes.

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Danes, L.H., Avansi, G.D. & Schiozer, D.J. A method for developing and calibrating optimization techniques for oil production management strategy applications. Comput Geosci (2024). https://doi.org/10.1007/s10596-024-10282-1

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