Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

Abstract

Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models’ advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction.

A Rate of Penetration (ROP) Prediction Method Based on Improved Dung Beetle Optimization Algorithm and BiLSTM-SA

“…Therefore, accurate prediction of the ROP is particularly important and has become a long-standing focus of research in both academia and industry. In recent years, with the rapid development of the field of artificial intelligence, advanced technologies such as machine learning have been gradually applied to the oil extraction industry, especially in ROP prediction 1,2 .…”

“…(1) Tens of thousands of drilling data were collected, and mixed sampling and small filtering were applied to process the dataset, enhancing the overall data quality and usability. (2) The model integrates ROP prediction physical formulas in machine learning to enhance overall model accuracy. (3) A bidirectional LSTM (BiLSTM) model with good temporal performance was selected and combined with a self-attention mechanism (SA) to enhance BiLSTM feature learning.…”