Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

• Published in: Journal of petroleum exploration and production technology Vol. 11; no. 7; pp. 3103 - 3127
• Main Authors: Ng, Cuthbert Shang Wui, Jahanbani Ghahfarokhi, Ashkan, Nait Amar, Menad
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Case studies; Earth and Environmental Science; Earth Sciences; Energy Systems; Geology; Heuristic methods; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Machine learning; Monitoring/Environmental Analysis; Neural networks; Offshore Engineering; Optimization; Original Paper-Production Engineering; Ova; Particle swarm optimization; Reservoirs; Simulators; Three dimensional models; Training; Two dimensional models; Data-driven proxy modeling; Waterflooding optimization; Artificial neural network; Nature-inspired algorithms; Machine learning
• ISSN: 2190-0558; 2190-0566
• DOI: 10.1007/s13202-021-01199-x

With the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R 2 being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.