Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network
With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data se...
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| Veröffentlicht in: | Petroleum science and technology Jg. 40; H. 17; S. 2141 - 2162 |
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Taylor & Francis
12.05.2022
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| Abstract | With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data sensitive to lithology by introducing Bayesian classifier and Self Adaptive Double Chain Quantum Genetic Algorithm (S-DC-QGA) which could optimize BP neural network prediction model. In the first step, the Bayesian network classifier achieved the classification and recognition of lithology, improved the correlation of model sample data, and enhanced the accuracy and speed of lithology classification. In the second step, S-DC-QGA characterized as the global optimization is used to optimize the number of hidden layer nodes, weights and thresholds of the BP neural network. Results showed that Bayesian lithologies classification enhanced the correlation of samples, moreover optimized BP neural network solved overfitting, random initial weights of BP itself and oscillation of fitting and generalization ability with slight change of network parameters, improving the prediction accuracy of rock drillability parameters and the generalization ability of the model. The findings of this study can help for better drilling design and improvement of mechanical drilling speed. |
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| AbstractList | With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data sensitive to lithology by introducing Bayesian classifier and Self Adaptive Double Chain Quantum Genetic Algorithm (S-DC-QGA) which could optimize BP neural network prediction model. In the first step, the Bayesian network classifier achieved the classification and recognition of lithology, improved the correlation of model sample data, and enhanced the accuracy and speed of lithology classification. In the second step, S-DC-QGA characterized as the global optimization is used to optimize the number of hidden layer nodes, weights and thresholds of the BP neural network. Results showed that Bayesian lithologies classification enhanced the correlation of samples, moreover optimized BP neural network solved overfitting, random initial weights of BP itself and oscillation of fitting and generalization ability with slight change of network parameters, improving the prediction accuracy of rock drillability parameters and the generalization ability of the model. The findings of this study can help for better drilling design and improvement of mechanical drilling speed. |
| Author | Feng, Hong Fang, Xinxin Wang, Hao |
| Author_xml | – sequence: 1 givenname: Xinxin orcidid: 0000-0001-8654-708X surname: Fang fullname: Fang, Xinxin organization: China Coal Technology & Engineering Group Xian Institute – sequence: 2 givenname: Hong surname: Feng fullname: Feng, Hong organization: China Coal Technology & Engineering Group Xian Institute – sequence: 3 givenname: Hao surname: Wang fullname: Wang, Hao organization: Research Institute of Petroleum Exploration & Development |
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| SubjectTerms | Back propagation networks Bayesian Bayesian analysis BP neural network Classification Classifiers Data logging Drilling genetic algorithm Genetic algorithms Global optimization Lithology lithology classification Mathematical models Neural networks Parameters Prediction models rock drillability Shale gas |
| Title | Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network |
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