Reservoir lithology identification method based on multi-scale time-frequency-space feature combination
WANG Zong-Ren1,2,3(), WEN Chang4,5(), XIE Kai1,2,3, SHENG Guan-Qun6, HE Jian-Biao7
1. School of Electronic Information,Yangtze University,Jingzhou 434023,China 2. Key Laboratory of Oil and Gas Resources and Exploration Technology, Ministry of Education,Jingzhou 434023,China 3. National Experimental Teaching and Demonstration Center of Electrical Engineering and Electronics,Yangtze University,Jingzhou 434023,China 4. Western Research Institute of Yangtze University,Xinjiang 834000,China 5. School of Computer Science,Yangtze University,Jingzhou 434023,China 6. School of Computer and Information,China Three Gorges University,Yichang 443002,China 7. School of Computer Science,Central South University,Changsha 410083,China
Conventional methods for reservoir lithology identification suffer low precision and efficiency since reservoir lithologies have various types and complex compositions and alternate frequently.This study proposed a reservoir lithology identification method based on multi-scale time-frequency-space feature combination.Based on the original logging characteristics,this method introduced the multi-scale frequency-domain components from the complementary ensemble empirical mode decomposition (CEEMD) to improve the longitudinal resolution of log curves.Moreover,a multi-scale convolutional neural network-bidirectional gated recurrent unit-attention mechanism (CNN-BiGRU-AT) model was constructed to extract the spatio-temporal features of log data containing multi-scale frequency-domain components.In this way,the joint learning of time-frequency-space features of log data was realized.Finally,the model output was optimized using the attention mechanism to reduce the propagation of error information.To verify the reliability of this method,an experimental analysis was conducted using the data from five wells that have relatively complete data.As revealed by the analysis results,the identification accuracy of training and verification sets containing multi-scale frequency-domain components was increased by 9.50% and 8.66%,respectively in the comparative experiments of different data combinations.The method proposed in this study yielded sample identification accuracy of 94.11%.Compared with support vector machine (SVM),backpropagation (BP) neural network,convolutional neural network (CNN),bidirectional gated recurrent unit (BiGRU),and CNN-BiGRU fusion models, the identification accuracy of this method increased by 16.21%,14.54%,11.69%,5.05%,and 3.38%,respectively.
Fu G M, Yan J Y, Zhang K, et al. Current status and progress of lithology identification technology[J]. Progress in Geophysics, 2017, 32(1):26-40.
[2]
Adeniran A A, Adebayo A R, Salami H O, et al. A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs[J]. Applied Computing and Geosciences, 2019, 1:100004.
doi: 10.1016/j.acags.2019.100004
Yuan Z W, Duan Z J, Zhang G Y, et al. Interpretation of logging lithology in Carbonate reservoirs based on Markov Chain probability model[J]. Xinjiang Petroleum Geology, 2017, 38(1):96-102.
Cheng D W, Yuan X J, Zhou C M, et al. Logging-lithology identifi cation methods and their application:A case study on Chang 7 Member in central-western Ordos Basin,NW China[J]. China Petroleum Exploration, 2016, 21(5):117-126.
Wang Z H, Zhu X M, Sun Z C, et al. Igneous lithology identification and lithofacies classification in the basin using logging data:Taking Junggar Basin as an example[J]. Earth Science Frontiers, 2015, 22(3):254-268.
[6]
Bai X L, Zhang S N, Huang Q Y, et al. Origin of dolomite in the Middle Ordovician peritidal platform carbonates in the northern Ordos Basin,western China[J]. Petroleum Science, 2016, 13(3):434-449.
doi: 10.1007/s12182-016-0114-5
[7]
Bressan T S, Souza M, Girelli T J, et al. Evaluation of machine learning methods for lithology classification using geophysical data[J]. Computers & Geosciences, 2020, 139:104475.
doi: 10.1016/j.cageo.2020.104475
[8]
Corina A N, Hovda S. Automatic lithology prediction from well logging using kernel density estimation[J]. Journal of Petroleum Science and Engineering, 2018, 170:664-674.
doi: 10.1016/j.petrol.2018.06.012
Cai Z Y, Lu B L, Xiong S Q, et al. Lithology identification based on Bayesian probability using adaptive kernel density[J]. Geophysical and Geochemical Exploration, 2020, 44(4):919-927.
Su F, Ma L, Luo R Z, et al. Research and application of logging lithology identification based on improve multi-class twin support vector machine[J]. Progress in Geophysics, 2020, 35(1):174-180.
Yang L Q, Chen W, Cha P. Prediction and application of reservoir porosity by convolutional neural network[J]. Progress in Geophysics, 2019, 34(4):1548-1555.
Zhou H, Zhang C L, Zhang X, et al. Lithology identification method of carbonate reservoir based on capsule network[J]. Natural Gas Geoscience, 2021, 32(5):685-694.
Wang Y C, Liu L T, Xu H Z. The identification of gravity anomaly body based on the convolutional neural network[J]. Geophysical and Geochemical Exploration, 2020, 44(2):394-400.
Liang L F, Liu X J, Zhang H B, et al. A study of the effect of hyperparameters GRU-CNN hybrid deep learning EI inversion[J]. Geophysical and Geochemical Exploration, 2021, 45(1):133-139.
