Version 1
: Received: 28 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (11:41:15 CET)
How to cite:
Irawan, S.; Wayo, D.D.K.; Satyanaga, A.; Kim, J. Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs. Preprints2024, 2024021677. https://doi.org/10.20944/preprints202402.1677.v1
Irawan, S.; Wayo, D.D.K.; Satyanaga, A.; Kim, J. Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs. Preprints 2024, 2024021677. https://doi.org/10.20944/preprints202402.1677.v1
Irawan, S.; Wayo, D.D.K.; Satyanaga, A.; Kim, J. Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs. Preprints2024, 2024021677. https://doi.org/10.20944/preprints202402.1677.v1
APA Style
Irawan, S., Wayo, D.D.K., Satyanaga, A., & Kim, J. (2024). Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs. Preprints. https://doi.org/10.20944/preprints202402.1677.v1
Chicago/Turabian Style
Irawan, S., Alfrendo Satyanaga and Jong Kim. 2024 "Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs" Preprints. https://doi.org/10.20944/preprints202402.1677.v1
Abstract
The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.
Keywords
Well deployment; Genetic Algorithms; CMG; Global Optimization; Reservoir
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
