A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning
文献类型：期刊论文
| 作者 | Li, Huayang4,5; Tan, Qiang4,5; Deng, Jingen4,5; Dong, Baohong4,5; Li, Bojia4,5; Guo, Jinlong2; Zhang, Shuiliang1; Bai, Weizheng3 |
| 刊名 | PROCESSES
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 11期号:9页码:31 |
| 关键词 | pore pressure prediction machine learning KNN Extra Trees Random Forest LightGBM overpressure well logs empirical models |
| DOI | 10.3390/pr11092603 |
| 英文摘要 | In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density-sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir's pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block. |
| 资助项目 | National Natural Science Foundation of China[52174040] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001074460300001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/39440]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tan, Qiang; Deng, Jingen |
| 作者单位 | 1.CNOOC Tianjin Branch, Tianjin 300459, Peoples R China 2.Joint Logist Support Force, Shanghai Quartermaster & Energy Qual Supervis Stn, Quartermaster & Energy Qual Supervis Stn, Shanghai 200137, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.China Univ Petr, Sch Petr Engn, Beijing 102200, Peoples R China 5.China Univ Petr, State Key Lab Petr Resource & Prospecting, Beijing 102249, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Huayang,Tan, Qiang,Deng, Jingen,et al. A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning[J]. PROCESSES,2023,11(9):31. |
| APA | Li, Huayang.,Tan, Qiang.,Deng, Jingen.,Dong, Baohong.,Li, Bojia.,...&Bai, Weizheng.(2023).A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning.PROCESSES,11(9),31. |
| MLA | Li, Huayang,et al."A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning".PROCESSES 11.9(2023):31. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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