测井技术2025，Vol.49Issue(2)：198-208,11.DOI:10.16489/j.issn.1004-1338.2025.02.007
基于贝叶斯优化CNN-LSTM的密度测井曲线重构方法
Density Logging Curve Reconstruction Method Based on Bayesian-Optimized CNN-LSTM
摘要
Abstract
Density logging curves are critical evaluation indicators in oil and gas exploration and development,with their accuracy directly affecting the reliability of reservoir lithology identification,porosity calculation,and fluid property analysis.During logging operations,instrument malfunctions or borehole enlargement often lead to missing or distorted density logging data,compromising reservoir evaluation accuracy.Traditional reconstruction methods,constrained by insufficient model representation capabilities,fail to effectively capture relationships between logging curves under complex geological conditions,resulting in reconstructed curve accuracy that struggles to meet reservoir evaluation requirements.To address this issue,this study proposes a density logging curve reconstruction method based on Bayesian-optimized convolutional neural networks(CNN)combined with long short-term memory networks(LSTM).This approach integrates CNN's advantages in local spatial feature extraction with LSTM's capability in temporal dependency modeling,while introducing Bayesian optimization to automatically search for optimal hyperparameters.Through this integration,the model can fully leverage the strengths of both components when processing logging data under complex geological conditions,thereby enhancing overall performance.Applied to density logging curve reconstruction in three exploration wells in the Sichuan Basin,experimental results demonstrate that compared to standalone CNN or LSTM models,the Bayesian-optimized CNN-LSTM model exhibits superior performance in both accuracy and stability,significantly mitigating the impact of logging curve distortion or data loss on reservoir evaluation.关键词
曲线重构/密度测井/卷积神经网络/长短期记忆网络/贝叶斯优化Key words
curve reconstruction/density logging/convolutional neural network/long short-term memory network/Bayesian optimization引用本文复制引用
李洪玺,陈明江,张显坤,杨孛,赵彬,李贤胜,王欢欢..基于贝叶斯优化CNN-LSTM的密度测井曲线重构方法[J].测井技术,2025,49(2):198-208,11.基金项目
海洋油气勘探国家工程研究中心开放基金课题"古潜山油气藏矿物组分反演及测井智能岩性识别方法研究"(CCL2024RCPS0283KQN) （CCL2024RCPS0283KQN）
川庆钻探工程有限公司地质勘探开发研究院项目"川中-川西地区致密砂岩气藏挖潜测井评价关键技术研究"(CQCJ-2023-08) （CQCJ-2023-08）
