OPTIMASI MULTIOBJEKTIF PENJADWALAN PRODUKSI DAN INJEKSI AIR/GAS PADA SUMUR MINYAK : STUDI KASUS RESERVOIR LAPANGAN X DENGAN ALGORITMA GENETIKA HIBRIDA
DOI:
https://doi.org/10.53026/prosidingsntem.v5i1.684Keywords:
Enhanced Oil Recovery, Hybrid Genetic Algorithm (HGA), Model Proxy, NPV, NSGAII, Optimasi Multi-Objektif, Pareto Front, Penjadwalan Sumur, Sequential Quadratic Programming (SQP)Abstract
Penelitian ini menerapkan Hybrid Genetic Algorithm (HGA) yang menggabungkan NSGA-II dan SQP untuk mengoptimalkan NPV, COP, dan Water Cut pada lima skenario reservoir base case, high pressure, mature field, heavy oil, dan gas cap, menggunakan model proxy analitik yang efisien. HGA menghasilkan 93–200 solusi Pareto dalam 11.57–88.90 detik, dengan base case memberi NPV tertinggi, high pressure menunjukkan water cut tinggi dan NPV negatif, mature field membuktikan skalabilitas algoritma, heavy oil memiliki konvergensi paling stabil, dan gas cap tidak menghasilkan solusi feasible tanpa upgrade fasilitas. Analisis konvergensi menunjukkan local search efektif menjaga kualitas dan keberagaman solusi, meski model masih memiliki keterbatasan seperti penyederhanaan fisika reservoir, tidak adanya geomechanical constraints, dan belum mempertimbangkan uncertainty. Penelitian lanjutan disarankan mengintegrasikan simulator high-fidelity, uncertainty analysis, dan realtime opimization untuk meningkatkan realisme dan penerapannya pada skala lapangan.
References
M. A. S. Pinto, E. Gildin, and D. J. Schiozer, “Short-Term and Long-Term Optimizations for Reservoir Management with Intelligent Wells,” SPE Lat. Am. Caribb. Pet. Eng. Conf. Proc., vol. 2015-Janua, 2015, doi: 10.2118/177255-ms.
D. Sulistyanto, “Optimasi Produksi Sumur-Sumur Gas Lift Di Lapangan a,” PETROJurnal Ilm. Tek. Perminyakan, vol. 5, no. 1, Sep. 2018, doi: 10.25105/petro.v5i1.1981.
Z. Aghayev, D. Voulanas, E. Gildin, and B. Beykal, “Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling,” Ind. Eng. Chem. Res., vol. 64, no. 15, pp. 7751–7766, Apr. 2025, doi: 10.1021/acs.iecr.4c03294.
C. Science, “Operational Optimization of Oil Reservoirs in the Danish North Sea,” 2019.
A. Rostamian, M. V. de Sousa Miranda, A. Mirzaei-Paiaman, V. E. Botechia, and D. J. Schiozer, “Analysis of different objective functions in petroleum field development optimization,” J. Pet. Explor. Prod. Technol., vol. 14, no. 10, pp. 2785–2805, 2024, doi: 10.1007/s13202-024-01848-x.
M. Rajaa, Hybrid Proxy Model for Extrapolating Cumulative Oil Production with Different Locations: Polynomial- Fast Marching Method. 2024. doi: 10.21203/rs.3.rs-5299485/v1.
P. Jedlicka, A. D. Bird, and H. Cuntz, “Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons,” Open Biol., vol. 12, no. 7, p. 20073, Jul. 2022, doi: 10.1098/rsob.220073.
F. Meng et al., “Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches,” Sci. Rep., vol. 15, no. 1, p. 25643, 2025, doi: 10.1038/s41598-025-10111-0.
A. Sori, M. Sharifi, and N. Jabari, “Robust multi-objective optimization for water flooding under geological uncertainty,” Discov. Geosci., vol. 3, no. 1, p. 26, 2025, doi: 10.1007/s44288-025-00131-8.
Y. G. Woldesenbet, G. G. Yen, and B. G. Tessema, “Constraint handling in multiobjective evolutionary optimization,” IEEE Trans. Evol. Comput., vol. 13, no. 3, pp. 514–525, 2009, doi: 10.1109/TEVC.2008.2009032.
A. Bhosekar and M. Ierapetritou, “Advances in surrogate based modeling, feasibility analysis and and optimization: A review,” Comput. Chem. Eng., vol. 108, Sep. 2017, doi: 10.1016/j.compchemeng.2017.09.017.
S. Afzali, N. Rezaei, and S. Zendehboudi, “A comprehensive review on Enhanced Oil Recovery by Water Alternating Gas (WAG) injection,” Fuel, vol. 227, pp. 218–246, Sep. 2018, doi: 10.1016/j.fuel.2018.04.015.
N. Saber Rashid and I. M. I. Zebari, “A comprehensive review of metaheuristic algorithms for combinatorial optimization problems,” Int. J. Sci. World, vol. 11, no. 1, pp. 83–92, 2025, doi: 10.14419/d5pxkg39.
F. Meng et al., “Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches,” Sci. Rep., vol. 15, no. 1, pp. 1–22, 2025, doi: 10.1038/s41598-025-10111-0.
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