Well-Logging Prediction Based on Hybrid Neural Network Model

被引:24
作者
Wu, Lei [1 ]
Dong, Zhenzhen [1 ]
Li, Weirong [1 ]
Jing, Cheng [1 ]
Qu, Bochao [1 ]
机构
[1] Xian Shiyou Univ, Petr Engn Dept, Xian 710065, Peoples R China
关键词
well-logging; convolutional neural network; long short-term memory; particle swarm optimization; hybrid model; deep learning;
D O I
10.3390/en14248583
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.
引用
收藏
页数:19
相关论文
共 37 条
[1]  
Albawi S, 2017, I C ENG TECHNOL
[2]  
Alkinani H.H., 2019, SPE MIDDLE E OIL GAS, DOI 10.2118/195072-ms
[3]  
Darling T., 2005, WELL LOGGING FORMATI, P326
[4]  
Eberhart RC, 2001, IEEE C EVOL COMPUTAT, P81, DOI 10.1109/CEC.2001.934374
[5]  
Ellis D.V., 2007, Well Logging for Earth Scientists
[6]  
Friedman J.H., 2017, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, DOI DOI 10.1007/978-0-387-84858-7
[7]  
Graves A., 2012, LONG SHORT TERM MEMO, P37
[8]   Recent advances in convolutional neural networks [J].
Gu, Jiuxiang ;
Wang, Zhenhua ;
Kuen, Jason ;
Ma, Lianyang ;
Shahroudy, Amir ;
Shuai, Bing ;
Liu, Ting ;
Wang, Xingxing ;
Wang, Gang ;
Cai, Jianfei ;
Chen, Tsuhan .
PATTERN RECOGNITION, 2018, 77 :354-377
[9]   Reducing the dimensionality of data with neural networks [J].
Hinton, G. E. ;
Salakhutdinov, R. R. .
SCIENCE, 2006, 313 (5786) :504-507
[10]  
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]