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Multi objective optimization of a discrete fracture geothermal reservoir using Bi-LSTM network

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  • Yadav, Abhishek
  • Ansari, Md Irshad
  • Govindarajan, Suresh Kumar

Abstract

The hot dry rock holds a substantial amount of geothermal energy. An optimization study is necessary for field development in these reservoirs. The current study presents a multi objective optimization of a discrete fractured geothermal reservoir in the Puga geothermal field, Ladakh, using scCO2 as a geofluid. Initially, a numerical model is developed and verified with the existing analytical and numerical solutions. A set of simulations is designed using the sobol sequence sampling method to generate data for a machine learning model. Then, a surrogate model is proposed using a Bi-LSTM network architecture with multiple dense layers in the neural network to reduce the computational cost required for running multiple numerical simulations at different combinations of operational parameters. A genetic algorithm is employed for hyperparameter tuning to improve the performance and accuracy of model. Then, a Pareto front is obtained for three objective functions: average output power, reservoir flow impedance, and overall heat recovery, through an evolutionary algorithm (NSGA-II). Finally, the best scheme is selected using TOPSIS method for decision making. The optimal scheme has a well spacing of 700 m, injection fluid temperature of 313.15 K and pressures of 36 and 29 MPa at injection and production wells, respectively. On comparison with the base model, optimal scheme can extract 44.55 % additional geothermal energy from reservoir. The current research presents a reliable method for developing a geothermal system which can also be applied to design other similar geothermal projects.

Suggested Citation

  • Yadav, Abhishek & Ansari, Md Irshad & Govindarajan, Suresh Kumar, 2026. "Multi objective optimization of a discrete fracture geothermal reservoir using Bi-LSTM network," Energy, Elsevier, vol. 347(C).
    • Handle: RePEc:eee:energy:v:347:y:2026:i:c:s0360544226004238
    DOI: 10.1016/j.energy.2026.140320
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