Well-Logging Prediction Based on Hybrid Neural Network Model
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/24/8583 |
| _version_ | 1797504984378507264 |
|---|---|
| author | Lei Wu Zhenzhen Dong Weirong Li Cheng Jing Bochao Qu |
| author_facet | Lei Wu Zhenzhen Dong Weirong Li Cheng Jing Bochao Qu |
| author_sort | Lei Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:12:11Z |
| format | Article |
| id | doaj.art-76ebe3c2e65945cf86768d563d8759c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:12:11Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-76ebe3c2e65945cf86768d563d8759c52023-11-23T08:09:24ZengMDPI AGEnergies1996-10732021-12-011424858310.3390/en14248583Well-Logging Prediction Based on Hybrid Neural Network ModelLei Wu0Zhenzhen Dong1Weirong Li2Cheng Jing3Bochao Qu4Petroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, ChinaPetroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, ChinaPetroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, ChinaPetroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, ChinaPetroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, ChinaWell-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.https://www.mdpi.com/1996-1073/14/24/8583well-loggingconvolutional neural networklong short-term memoryparticle swarm optimizationhybrid modeldeep learning |
| spellingShingle | Lei Wu Zhenzhen Dong Weirong Li Cheng Jing Bochao Qu Well-Logging Prediction Based on Hybrid Neural Network Model Energies well-logging convolutional neural network long short-term memory particle swarm optimization hybrid model deep learning |
| title | Well-Logging Prediction Based on Hybrid Neural Network Model |
| title_full | Well-Logging Prediction Based on Hybrid Neural Network Model |
| title_fullStr | Well-Logging Prediction Based on Hybrid Neural Network Model |
| title_full_unstemmed | Well-Logging Prediction Based on Hybrid Neural Network Model |
| title_short | Well-Logging Prediction Based on Hybrid Neural Network Model |
| title_sort | well logging prediction based on hybrid neural network model |
| topic | well-logging convolutional neural network long short-term memory particle swarm optimization hybrid model deep learning |
| url | https://www.mdpi.com/1996-1073/14/24/8583 |
| work_keys_str_mv | AT leiwu wellloggingpredictionbasedonhybridneuralnetworkmodel AT zhenzhendong wellloggingpredictionbasedonhybridneuralnetworkmodel AT weirongli wellloggingpredictionbasedonhybridneuralnetworkmodel AT chengjing wellloggingpredictionbasedonhybridneuralnetworkmodel AT bochaoqu wellloggingpredictionbasedonhybridneuralnetworkmodel |