Hybrid Strategies for Interpretability of Rate of Penetration Prediction: Automated Machine Learning and SHAP Interpretation

Abstract

ABSTRACT: Accurate prediction of rate of penetration (ROP) during petroleum drilling is crucial to optimize and guide field operations. However, due to the complex nonlinear relationship between drilling parameters and ROP, traditional empirical models often struggle to accurately predict ROP. This study introduces an automated machine learning (AutoML) for ROP prediction and utilizes SHAP (SHapley Additive exPlanations) to interpret the prediction results. The workflow framework based on this collaborative prediction strategy enables automated processing of data and automatic stacking ensemble of multiple machine learning models. It adaptively selects the optimal model after comprehensive validation without human intervention, thereby significantly reducing the time spent on model selection and hyperparameter optimization for ROP prediction. The results indicate that the weighted ensemble model, which has been stacked level-3 and 5-fold cross-validation, achieves the best prediction accuracy: RMSE= 1.86, MSE= 3.47. SHAP provides a global explanation for the model's prediction results, making the results of the automated prediction workflow more convincing and interpretable. This study provides automated machine learning workflow ideas for accurate prediction of ROP so that researchers can focus more on the business scenario itself without excessive machine knowledge and frequent manual intervention. 1. INTRODUCTION In the field of petroleum drilling, the rate of penetration (ROP) is a crucial indicator that reflects the speed at which the drill bit penetrates and breaks through the rock formation. It plays a pivotal role in measuring drilling efficiency. Accurate prediction of ROP is essential for optimizing drilling parameters during the drilling process, which can effectively improve efficiency and reduce costs (Li et al., 2022; H. Zhang et al., 2021; Kuang et al., 2021). With the development of oil drilling technology and modern data science and technology, the prediction methods for ROP have undergone distinct stages of development: empirical or physical models, prediction models that combine physical and data-driven approaches, and machine learning models (Boukredera et al., 2023; Ahmed et al., 2019). In the realm of equation-based prediction using conventional methods, several physics-based models such as the B-Y ROP equation (Bourgoyne & Young, 1974), MSE equation(Caicedo et al., 2005), and Motahhari equation(Motahhari et al., 2010) have certain limitations. These models may not consider all the factors comprehensively, making it challenging to adapt to complex downhole scenarios. Hegde et al.(2017) compared three physics-based traditional models with data-driven models using a combination of physics and data-driven modeling approaches. In terms of using machine learning to predict ROP, various machine learning algorithms such as random forest(RF), support vector machine(SVM), and neural network(NN) have been employed(Moran et al., 2010; Ashrafi et al., 2019; Brenjkar & Biniaz Delijani, 2022; Tunkiel et al., 2022; C. Zhang et al., 2023; Wan et al., 2023). The results indicate that machine learning outperforms traditional models (Soares & Gray, 2019); Bizhani and Kuru(2022) explored the application of Bayesian neural networks in ROP prediction, focusing on the concept of model prediction uncertainty. Duru(2022) optimized five machine learning algorithms: linear regression, decision tree, support vector machine, random forest and multilayer perceptron by genetic algorithm and the results showed enhanced prediction performance of models. Gan et al.(2023) proposed a novel ROP modeling approach called hybrid bat algorithm optimized - restricted Boltzmann machine - back propagation neural network. Qu et al. (2023) improved the backpropagation neural network (BP) and utilized it for ROP prediction methods.