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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:

第46卷

期数:

2024年第01期

页码:

127-142

栏目:

沉积地质与油气勘探

出版日期:

2024-01-15

文章信息/Info

Title:

Lithology Identification in Optuna-BiGRU Logging Based on Enhanced Multi-head Attention Mechanism

文章编号:

1672-6561(2024)01-0127-16

作者:

王婷婷¹; 2; 王振豪¹; 李 方¹; 赵万春³; 4*

(1. 东北石油大学 电气信息工程学院,黑龙江 大庆 163318; 2. 东北石油大学 黑龙江省网络化与智能控制重点实验室,黑龙江 大庆 163318; 3. 东北石油大学 非常规油气研究院,黑龙江 大庆 163318; 4. 东北石油大学 陆相页岩油气成藏及高效开发教育部重点实验室,黑龙江 大庆 163318)

Author(s):

WANG Ting-ting¹; 2;  WANG Zhen-hao¹;  LI Fang¹;  ZHAO Wan-chun³; 4*

(1. School of Electrical & Information Engineering, Northeast Petroleum University, Daqing 163318, Heilongjiang, China; 2. Heilongjiang Provincial Key Laboratory of Network and Intelligent Control, Northeast Petroleum University, Daqing 163318, Heilongjiang, China; 3. Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing 163318, Heilongjiang, China; 4. Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development of Ministry of Education, Northeast Petroleum University, Daqing 163318, Heilongjiang, China)

关键词:

岩性识别;  深度学习;  BiGRU;  增强多头注意力机制;  小波包自适应阈值;  超参数优化

Keywords:

lithology identification;  deep learning;  BiGRU;  enhancing multi-head attention mechanism;  wavelet packet adaptive threshold;  hyperparameter optimization

分类号:

P618.13; P631.8⁺1

DOI:

10.19814/j.jese.2023.07011

文献标志码:

A

摘要:

测井岩性识别是油气勘探开发中至关重要的内容。针对现有算法模型在处理测井曲线数据时,无法有效捕获曲线内部深层关联和深度方向关系、拟合能力较弱、难以准确提取关键特征、噪声干扰以及模型超参数调优过程复杂困难等问题,提出了一种通过Optuna超参数优化双向门循环单元(Optuna-BiGRU)结合增强多头注意力机制(EMHA)的测井岩性识别模型——Optuna-BiGRU-EMHA模型。该模型引入残差机制和层归一化以改进多头注意力机制模块,并结合双向门循环单元(BiGRU)解决了处理测井数据时的问题,同时使用Optuna超参数优化框架和小波包自适应阈值方法分别解决了超参数调优和噪声干扰问题。首先通过交会图分析和敏感性箱线图分析选取自然伽马、深感应电阻率、中子-密度孔隙度、平均中子-密度孔隙度和岩性密度5个特征参数的测井数据,通过小波包自适应阈值方法对数据进行去噪,并将测井数据分割成数据块,然后利用Optuna框架优化BiGRU-EMHA模型超参数,最后通过实验对比K-近邻算法(KNN)、随机森林(RF)、极端梯度提升算法(XGBoost)、长短期记忆(LSTM)神经网络、BiGRU、双向长短期记忆(BiLSTM)神经网络、BiGRU-MHA、Optuna-BiGRU-EMHA等8种模型在测井岩性识别中的精度。结果表明:Optuna-BiGRU-EMHA模型识别准确率达到80%,相对于传统机器学习模型和深度学习模型,综合岩性识别准确率分别提高15.94%～23.14%和3.93%～15.94%,该模型为常规测井岩性识别提供了坚实的理论支持。

Abstract:

Lithology identification in well logging plays a crucial role in oil and gas exploration and development. However, existing algorithmic models face challenges in effectively capturing deep-seated correlations and depth-related relationships within log curve data. They also exhibit weak fitting capabilities, struggle with accurate feature extraction, contend with noise interference, and face complexities in fine-tuning model hyperparameters. To address these issues, a model for lithology identification that combines Optuna hyperparameter optimization with a bidirectional gate cycle unit(BiGRU)and enhanced multi-head attention(EMHA)was proposed. The model is called Optuna-BiGRU-EMHA. The approach introduces residual mechanisms and layer normalization to enhance the multi-head attention module, effectively resolving issues encountered when processing well-logging data. Additionally, it leverages the Optuna hyperparameter optimization framework and wavelet packet adaptive threshold method to address hyperparameter fine-tuning and noise interference. The study begins by selecting well-logging data of five characteristic parameters, including natural gamma, deep induction resistivity, neutron-density porosity, average neutron-density porosity and lithology density, through cross-plot and sensitivity analyses. The data is then denoised using the wavelet packet adaptive threshold method and segmented into data blocks. Subsequently, the Optuna framework is employed to optimize the hyperparameters of the BiGRU-EMHA model. Finally, experimental comparisons are conducted to assess the accuracy of eight models in lithology identification based on well logging data. The eight models are KNN, RF, XGBoost, LSTM, BiGRU, BiLSTM, BiGRU-MHA, and Optuna-BiGRU-EMHA. The results show that the Optuna-BiGRU-EMHA model achieves an identification accuracy of 80%. Compared with traditional machine learning and deep learning models, there is a substantial improvement in comprehensive lithology identification accuracy by 15.94% to 23.14%, and 3.93% to 15.94%. The Optuna-BiGRU-EMHA model provides a robust theoretical foundation for conventional well-logging lithology identification in the domain of oil and gas exploration and development.

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相似文献/References:

[1]蔡浩杰,韩海辉,张雨莲,等.基于地形特征融合的卷积神经网络滑坡识别[J].地球科学与环境学报,2022,44(03):568.[doi:10.19814/j.jese.2021.12016]
　CAI Hao-jie,HAN Hai-hui,ZHANG Yu-lian,et al.Convolutional Neural Network Landslide Recognition Based on Terrain Feature Fusion[J].Journal of Earth Sciences and Environment,2022,44(01):568.[doi:10.19814/j.jese.2021.12016]
[2]方 巍,齐媚涵.基于深度学习的高时空分辨率降水临近预报方法[J].地球科学与环境学报,2023,45(03):706.[doi:10.19814/j.jese.2023.01010]
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[3]辛鲁斌,韩 玲*,李良志.基于多源数据融合的滑坡智能识别[J].地球科学与环境学报,2023,45(04):920.
　XIN Lu-bin,HAN Ling*,LI Liang-zhi.Landslide Intelligent Recognition Based on Multi-source Data Fusion[J].Journal of Earth Sciences and Environment,2023,45(01):920.

备注/Memo

备注/Memo:

收稿日期:2023-07-07; 修回日期:2023-09-18投稿网址:http:∥jese.chd.edu.cn/
基金项目:国家自然科学基金项目(52074088,52174022); 东北石油大学省杰青后备人才项目(SJQH202002); 黑龙江省博士后科研启动项目(LBH-Q21086); 黑龙江省揭榜挂帅科技攻关项目(DQYT-2022-JS-758)
作者简介:王婷婷(1982-),女,黑龙江大庆人,教授,博士研究生导师,工学博士,E-mail:wangtingting@nepu.edu.cn。
*通信作者:赵万春(1978-),男,黑龙江大庆人,教授,博士研究生导师,工学博士,E-mail:zhaowanchun@nepu.edu.cn。

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更新日期/Last Update: 2024-01-25