|Table of Contents|

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

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

Issue:
2024年第01期
Page:
127-142
Research Field:
沉积地质与油气勘探
Publishing date:

Info

Title:
Lithology Identification in Optuna-BiGRU Logging Based on Enhanced Multi-head Attention Mechanism
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)
Keywords:
lithology identification;  deep learning;  BiGRU;  enhancing multi-head attention mechanism;  wavelet packet adaptive threshold;  hyperparameter optimization
PACS:
P618.13; P631.8+1
DOI:
10.19814/j.jese.2023.07011
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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Last Update: 2024-01-25