Using Multi-Objective Optimization Genetics Algorithm for Co-estimation of Saturation Functions by Piecewise Model

Document Type : Research Paper

Authors

Faculty of Petroleum and Natural Gas Engineering, Sahand Oil and Gas Research Institute, Sahand University of Technology, Tabriz, Iran

10.22078/pr.2024.5266.3337

Abstract

Saturation/flow functions, i.e., water and oil relative permeability and capillary pressure are the most critical input parameters that play a key role in porous media simulation studies and forecasting the fluids recovery. Various methods including laboratory tests (steady or unsteady core flooding), and mathematical methods can be utilized to measure or estimate the flow function curves. In this research, history matching technique was performed using optimization genetic algorithms to adjust the parameters of the proposed model based on laboratory water flooding data (pressure drop and cumulative oil production) and the results of core flooding simulation. To do so, multi-objective genetic algorithms were employed to find optimum solutions for piecewise model and then the results are compared with several empirical models (e.g. Corey and Brooks-Corey models). History matching results showed that single objective optimization, i.e., considering cumulative oil production as an objective function provides a good fit for Corey’s model (R-squared= 99.56%) and single and multi-objective optimization lead to the best fits, respectively, with the accuracies of 99.57 and 99.16% for piecewise model during the imbibition process. Moreover, the drainage history matching results showed that the piecewise model exhibits the best performance employing the single objective and multi-objective optimization algorithms with the accuracies of 99.32 and 99.85%, respectively. Overall, the piecewise model can be utilized to address the history matching problem using multi-objective optimization and estimate the flow functions curves. Hence, it is proposed as an efficient method for estimating fluid relative permeability and capillary pressure functions based on experimental and simulation data.

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References

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Journal of Petroleum Research
Volume 34, 1403-2 - Serial Number 135
Special Issues on Water-based EOR
July and August 2024
Pages 72-90

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