Smart Proxy Modeling of a Fractured Reservoir Model for Production Optimization: Implementation of Metaheuristic Algorithm and Probabilistic Application

• Published in: Natural resources research (New York, N.Y.) Vol. 30; no. 3; pp. 2431 - 2462
• Main Authors: Ng, Cuthbert Shang Wui, Jahanbani Ghahfarokhi, Ashkan, Nait Amar, Menad, Torsæter, Ole
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Back propagation; Back propagation networks; Chemistry and Earth Sciences; Computer applications; Computer Science; Computer simulation; Computing time; Earth and Environmental Science; Earth Sciences; Fossil Fuels (incl. Carbon Capture); Fractured reservoirs; Geography; Heuristic methods; Machine learning; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mineral Resources; Neural networks; Optimization; Original Paper; Particle swarm optimization; Performance prediction; Permeability; Physics; Porosity; Proxies; Reservoir management; Reservoirs; Statistics for Engineering; Sustainable Development; Dual-porosity dual-permeability; Smart proxy modeling; Particle swarm optimization; Reservoir simulation; Backpropagation algorithms
• ISSN: 1520-7439; 1573-8981
• DOI: 10.1007/s11053-021-09844-2

Numerical reservoir simulation has been recognized as one of the most frequently used aids in reservoir management. Despite having high calculability performance, it presents an acute shortcoming, namely the long computational time induced by the complexities of reservoir models. This situation applies aptly in the modeling of fractured reservoirs because these reservoirs are strongly heterogeneous. Therefore, the domains of artificial intelligence and machine learning (ML) were used to alleviate this computational challenge by creating a new class of reservoir modeling, namely smart proxy modeling (SPM). SPM is a ML approach that requires a spatio-temporal database extracted from the numerical simulation to be built. In this study, we demonstrate the procedures of SPM based on a synthetic fractured reservoir model, which is a representation of dual-porosity dual-permeability model. The applied ML technique for SPM is artificial neural network. We then present the application of the smart proxies in production optimization to illustrate its practicality. Apart from applying the backpropagation algorithms, we implemented particle swarm optimization (PSO), which is one of the metaheuristic algorithms, to build the SPM. We also propose an additional procedure in SPM by integrating the probabilistic application to examine the overall performance of the smart proxies. In this work, we inferred that the PSO had a higher chance to improve the reliability of smart proxies with excellent training results and predictive performance compared with the considered backpropagation approaches.