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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Petroleum science and technology Ročník 40; číslo 17; s. 2141 - 2162
Hlavní autoři: Fang, Xinxin, Feng, Hong, Wang, Hao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 12.05.2022
Taylor & Francis Ltd
Témata:
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
ISSN:1091-6466, 1532-2459
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1091-6466
1532-2459
DOI:10.1080/10916466.2022.2036759