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A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

Bin Li*

China United Coalbed Methane Corporation, Ltd., Beijing, 100011, China

* Corresponding Author: Bin Li. Email: email

(This article belongs to the Special Issue: Solid, Fluid, and Thermal Dynamics in the Development of Unconventional Resources )

Fluid Dynamics & Materials Processing 2023, 19(10), 2729-2748. https://doi.org/10.32604/fdmp.2023.029649

Abstract

In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence plots (PDP) indicate that liquid consumption intensity and the proportion of quartz sand are the two most sensitive factors affecting the model’s performance.

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APA Style
Li, B. (2023). A productivity prediction method based on artificial neural networks and particle swarm optimization for shale-gas horizontal wells. Fluid Dynamics & Materials Processing, 19(10), 2729-2748. https://doi.org/10.32604/fdmp.2023.029649
Vancouver Style
Li B. A productivity prediction method based on artificial neural networks and particle swarm optimization for shale-gas horizontal wells. Fluid Dyn Mater Proc. 2023;19(10):2729-2748 https://doi.org/10.32604/fdmp.2023.029649
IEEE Style
B. Li, "A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells," Fluid Dyn. Mater. Proc., vol. 19, no. 10, pp. 2729-2748. 2023. https://doi.org/10.32604/fdmp.2023.029649



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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