ROP and TOB optimization using machine learning classification algorithms

Oyedere, Mayowa; Gray, K. E.

doi:10.1016/j.jngse.2020.103230

Cited by 29 publications

(4 citation statements)

References 12 publications

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“…We can observe the following: (1) the proposed PCA−Informer model significantly improves the inference performance (number of accurate predictions) on the dataset, and as the prediction range continues to increase, the prediction error rises steadily, indicating that the PCA−Informer model is successful in predicting the rate of penetration. (2) The performance of the PCA−Informer model is significantly better than that of RNN and LSTM, with an average RMSE reduction of 35 and 29.9%, respectively, compared to these methods. (3) Compared to the single Informer model, the RMSE of our method is reduced by 11.8%.…”

Section: Results Discussion and Analysismentioning

confidence: 94%

“…The rate of penetration is one of the most effective evaluation indicators in drilling engineering, directly related to drilling costs and efficiency. , Currently, rate of penetration (ROP) prediction mainly relies on the professional knowledge of field engineers and postdrilling data analysis, and the results are often quite subjective, lacking reliable analytical basis . Accurate prediction of the rate of penetration can better plan drilling operations and shorten the drilling cycle.…”

Section: Introductionmentioning

confidence: 99%

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ROP Prediction Method Based on PCA–Informer Modeling

Wang,

Lou,

Lin

et al. 2024

ACS Omega

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Increasing the rate of penetration (ROP) is an effective means to improve the drilling efficiency. At present, the efficiency and accuracy of intelligent prediction methods for the rate of penetration still need to be improved. To improve the efficiency and accuracy of rate of penetration prediction, this paper proposes a ROP prediction model based on Informer optimized by principal component analysis (PCA). We take the Taipei Basin block oilfield as an example. First, we use principal component analysis to extract data features, transforming the original data into low-dimensional feature data. Second, we use the PCA-optimized data to build an Informer model for predicting ROP. Finally, combined with actual data and using the recurrent neural network (RNN) and long short-term memory (LSTM) as baselines, we perform algorithm performance comparative analysis using root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2). The results show that the average MAE, RMSE, and R 2 of the PCA–Informer model are 9.402, 0.172, and 0.858, respectively. Compared with other methods, it has a larger R 2 and smaller RMSE and MAPE, indicating that this method significantly outperforms existing methods and provides a new solution to improve the rate of penetration in actual drilling operations.

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Section: Results Discussion and Analysismentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

ROP Prediction Method Based on PCA–Informer Modeling

Wang,

Lou,

Lin

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…In 2020, other five models for optimizing the ROP were developed by Oyedere and Gray [29]; these models were developed using logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), SVR, and RF. These models were developed based on five machine learning classification tools to optimize the ROP based on the WOB, DSR, Q, and UCS.…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

et al. 2022

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This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.

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“…The rate of penetration (ROP) represents the drilling footage per unit of pure drilling time. The higher the ROP, the higher the drilling efficiency and speed. , To effectively manage the drilling operation, engineers must consider the ROP in advance. Currently, ROP prediction is mainly performed based on the expertise of the field engineers and analyzing the post-drilling data; hence, it is often subjective and unreliable .…”

Section: Introductionmentioning

confidence: 99%

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

Lou

Cheng

et al. 2022

ACS Omega

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Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

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ROP and TOB optimization using machine learning classification algorithms

Cited by 29 publications

References 12 publications

ROP Prediction Method Based on PCA–Informer Modeling

ROP Prediction Method Based on PCA–Informer Modeling

A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

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