permeability prediction
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Geofluids ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-15
Jing-Jing Liu ◽  
Jian-Chao Liu

High-precision permeability prediction is of great significance to tight sandstone reservoirs. However, while considerable progress has recently been made in the machine learning based prediction of reservoir permeability, the generalization of this approach is limited by weak interpretability. Hence, an interpretable XGBoost model is proposed herein based on particle swarm optimization to predict the permeability of tight sandstone reservoirs with higher accuracy and robust interpretability. The porosity and permeability of 202 core plugs and 6 logging curves (namely, the gamma-ray (GR) curve, the acoustic curve (AC), the spontaneous potential (SP) curve, the caliper (CAL) curve, the deep lateral resistivity (RILD) curve, and eight lateral resistivity (RFOC) curve) are extracted along with three derived variables (i.e., the shale content, the AC slope, and the GR slope) as data sets. Based on the data preprocessing, global and local interpretations are performed according to the Shapley additive explanations (SHAP) analysis, and the redundant features in the data set are screened to identify the porosity, AC, CAL, and GR slope as the four most important features. The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. The prediction results of the PSO-XGBoost model indicate a superior performance compared with that of the benchmark XGBoost model. In addition, the reliable application of the interpretable PSO-XGBoost model in the prediction of tight sandstone reservoir permeability is examined by comparing the results with those of two traditional mathematical regression models, five machine learning models, and three deep learning models. Thus, the interpretable PSO-XGBoost model is shown to have more advantages in permeability prediction along with the lowest root mean square error, thereby confirming the effectiveness and practicability of this method.

2022 ◽  
R. Miele ◽  
D. Grana ◽  
J.F. Costa ◽  
P.Y. Bürkle ◽  
L.E. Varella ◽  

Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 121915
Alvin K. Mulashani ◽  
Chuanbo Shen ◽  
Baraka M. Nkurlu ◽  
Christopher N. Mkono ◽  
Martin Kawamala

2022 ◽  
Vol 2152 (1) ◽  
pp. 012003
Hongyi Fu

Abstract The use of the mercury intrusion method has been one of the most relevant trends in determining the permeability of porous media in the past decades. In this paper, general knowledge of sandstone reservoir evaluation is delineated including the pore distribution of sandstones and air permeability measurement. Based upon the paradigmatic study conducted by Purcell, a schematic diagram illustrating apparatus used in mercury intrusion is shown and introduced, and the relevant procedure is also outlined. Four significant permeability prediction models are described respectively and compared based on researches focusing on tight rocks. By doing so, this article reveals that the performance of the models is different despite the painstaking analysis and the significance of these studies. The contribution of this present study is providing a general reference of permeability prediction by mercury intrusion method as well as its previous momentous studies, giving a comparison among the given models.

2021 ◽  
pp. 195-216
Simon Katz ◽  
Fred Aminzadeh ◽  
George Chilingar ◽  
M. Lackpour

2021 ◽  
Vol 39 (1) ◽  
José Carlos Xavier da Silva ◽  
Giovanni Chaves Stael ◽  
Silvia Lorena Bejarano Bermudez ◽  
Luis Jacobo Aguilera Aguilera ◽  
Rodrigo Bagueira de Vasconcellos Azeredo

Guosong Chen ◽  
Yuanlin Meng ◽  
Jinlai Huan ◽  
Youchun Wang ◽  
Lihua Xiao ◽  

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