Open Access

ARTICLE

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:

(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

Received 01 March 2023; Accepted 05 May 2023; Issue published 25 June 2023

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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Keywords

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