Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning

Ren, Yangfeng; Lu, Baoping; Zheng, Shuangjin; Kai, Bai; Cheng, Lin; Yan, Hao; Gan, Wang

doi:10.1155/2023/6645604

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In 2022, Zhou et al [19] established a prediction model of the ROP based on support vector regression (SVR), and they proposed an improved algorithm to solve the nonconvex problem of determining the optimal values of the model hyperparameters. In 2023, Ren et al [20] proposed an ROP prediction model based on stacking ensemble learning, and the prediction accuracy rate in specific oilfields reached 92.5%.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model of Machine Learning Method and Empirical Method for Rate of Penetration Prediction Based on Data Similarity

et al. 2023

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The rate of penetration (ROP) is an important indicator affecting the drilling cost and drilling performance. Accurate prediction of the ROP has important guiding significance for increasing the drilling speed and reducing costs. Recently, numerous studies have shown that machine learning techniques are an effective means to accurately predict the ROP. However, in petroleum engineering applications, its robustness and generalization cannot be guaranteed. The traditional empirical model has good robustness and generalization ability. Based on the quantification of data similarity, this paper establishes a hybrid model combining a machine learning method and an empirical method, which combines the high prediction accuracy of the machine learning method with the good robustness and generalization of the empirical method, overcoming the shortcomings of any single model. The AE-ED (the Euclidean Distance between the input data and reconstructed data from the autoencoder model) is defined to measure the data similarity, and according to the data similarity of each new piece of input data, the hybrid model chooses the corresponding single model to calculate. The results show that the hybrid model is better than any single model, and all the evaluation indicators perform better, making it more suitable for the ROP prediction in this field.

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Section: Introductionmentioning

confidence: 99%

Hybrid Model of Machine Learning Method and Empirical Method for Rate of Penetration Prediction Based on Data Similarity

et al. 2023

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Sequential Data Approach for Rate of Penetration Prediction Using Machine Learning Models: A Case Study the Offshore Volve Oil Field, North Sea, Norway

Pakawatthapana,

Khonthapagdee

2024

Lecture Notes in Networks and Systems

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Intelligent prediction method for fracture pressure based on stacking ensemble algorithm

Zhang,

Ren,

Zhang

et al. 2023

Geomech. Geophys. Geo-energ. Geo-resour.

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Fracture pressure is an important reference for wellbore stability analysis and hydraulic fracturing. Considering the low prediction accuracy, significant deviations, and limited applicability of traditional methods for predicting formation fracture pressure, this paper proposes an intelligent prediction method for fracture pressure using conventional well logging data based on the Stacking ensemble algorithm. The base learners of the model include RF, KNN, and LSTM algorithms with low correlation. The meta-learner adopts the XGBoost algorithm. The effectiveness of the model is validated using the fracture pressure data from Dagang Oilfield. The prediction results indicate that the stacking algorithm outperforms individual algorithms. After optimization with genetic algorithm, the R2 of the stacking model is 0.989, RMSE is 0.009%, and MAE is 0.32%. The global sensitivity analysis results show that AC and DEN in the well logging data have higher sensitivity to the fracture pressure. When using intelligent fracture pressure prediction methods, it is essential to ensure the accuracy of AC and DEN data. The work demonstrates the reliability and effectiveness of the method proposed for the intelligent prediction of fracturing pressure using conventional well logging data through Stacking ensemble algorithm to overcome the limitations of traditional methods.

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Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning

Cited by 4 publications

References 14 publications

Hybrid Model of Machine Learning Method and Empirical Method for Rate of Penetration Prediction Based on Data Similarity

Hybrid Model of Machine Learning Method and Empirical Method for Rate of Penetration Prediction Based on Data Similarity

Sequential Data Approach for Rate of Penetration Prediction Using Machine Learning Models: A Case Study the Offshore Volve Oil Field, North Sea, Norway

Intelligent prediction method for fracture pressure based on stacking ensemble algorithm

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