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Well controls and placement optimisation using response-fed and judgement-aided parameterisation: Olympus optimisation challenge

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Abstract

In this work, several techniques, with an emphasis on parameterisation, are proposed and used to address all the three exercises of Olympus optimisation challenge. For the well controls exercise, a response-fed parameterisation is introduced in which the decision variables are water-cut threshold and re-opening time for each producer and shut-in time and shut-in period for each injector. This parameterisation allows a non-uniform control across the realisations. In the parameterisation of the field development, reservoir engineering judgements are utilised to make the solution space smaller and accordingly expedite the convergence. These judgements allow constraining the wells to high oil-in-place regions, by imposing piecewise linear polynomials, and also constraining the wells to reservoir top and bottom, by approximating depths using kriging. Also, the preliminary analyses suggest that the variables corresponding to the location of platform can be separated from the placement of the wells. For the joint optimisation (sequential and simultaneous), the combination of the two sets of variables is used. A robust population-based algorithm, (μ + λ) Evolution Strategy, was utilised for all the three exercises. In addition, a stochastic adaptive selection of realisations is introduced to reduce the computation time, which works based on rank (Spearman’s) correlation coefficient between each realisation net-present-value (NPV) and the full-set NPV. In order to minimise the overall optimisation time, simulations were executed in parallel on a cluster. In the well controls exercise, the reactive strategy was outperformed by the proposed technique, from the first generation, and the algorithm converged after 16,500 (60 equivalent) simulations, to an E[NPV], 7.8%, higher than the reactive’s. In the field development exercise, an E[NPV] of 1.37 billion dollars was obtained with 45,000 simulations. The joint (simultaneous) optimisation with nearly 60,000 simulations resulted in an E[NPV] of 1.45 billion dollars, which was 40 million dollars more than the one achieved by the sequential with about the same amount of computation.

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Acknowledgements

Authors would like to thank Rock Flow Dynamics (RFD) and MathWorks for supporting this study by providing license for their software, tNavigator and MatLab. Special thanks to HPC team of the University of Adelaide for providing generous hardware support through Phoenix cluster for this research. Authors also wish to acknowledge Prof. Dominique Guerillot for the fruitful discussions.

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  1. The University of Adelaide, Adelaide, SA, 5005, Australia

    Mohammad Sayyafzadeh & Zaid Alrashdi

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  1. Mohammad Sayyafzadeh
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Correspondence to Mohammad Sayyafzadeh.

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Sayyafzadeh, M., Alrashdi, Z. Well controls and placement optimisation using response-fed and judgement-aided parameterisation: Olympus optimisation challenge. Comput Geosci 24, 2001–2025 (2020). https://doi.org/10.1007/s10596-019-09891-y

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