Biocomposites based on bentonite and lecithin: An experimental approach supported by molecular dynamics

Li, Qiang; Berraud-Pache, Romain; Yang, Jian; Souprayen, Christelle; Jaber, Maguy

doi:10.1016/j.clay.2022.106751

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…There are many factors affecting the initial productivity. Most researchers studying the main controlling factors primarily focus on geological factors and dynamic development Energies 2023, 16, 4489 2 of 17 factors [7][8][9][10][11]. Wenli Ma et al proposed the Pearson maximum information coefficient correlation synthesis analysis method to identify 13 main control factors for the initial shale gas productivity [12].…”

Section: Introductionmentioning

confidence: 99%

Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm

Zhao

Wang

2023

Energies

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Conventional numerical solutions and empirical formulae for predicting the initial productivity of oil wells in low-permeability reservoirs are limited to specific reservoirs and relatively simple scenarios. Moreover, the few influencing factors are less considered and the application model is more ideal. A productivity prediction method based on machine learning algorithms is established to improve the lack of application performance and incomplete coverage of traditional mathematical modelling for productivity prediction. A comprehensive analysis was conducted on the JY extra-low-permeability oilfield, considering its geological structure and various factors that may impact its extraction and production. The study collected 13 factors that influence the initial productivity of 181 wells. The Spearman correlation coefficient, ReliefF feature selection algorithm, and random forest selection algorithm were used in combination to rank the importance of these factors. The screening of seven main controlling factors was completed. The particle swarm optimization–extreme learning machine algorithm was adopted to construct the initial-productivity model. The primary control factors and the known initial productivity of 127 wells were used to train the model, which was then used to verify the initial productivity of the remaining 54 wells. In the particle swarm optimization–extreme learning machine (PSO-ELM) algorithm model, the root-mean-square error (RMSE) is 0.035 and the correlation factor (R2) is 0.905. Therefore, the PSO-ELM algorithm has a high accuracy and a fast computing speed in predicting the initial productivity. This approach will provide new insights into the development of initial-productivity predictions and contribute to the efficient production of low-permeability reservoirs.

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