Rock Mechanics
Mahdi Bajolvand; Ahmad Ramezanzadeh; Amin Hekmatnejad; Mohammad Mehrad; Shadfar Davoodi; Mohammad Teimuri; Mohammad Reza Hajsaeedi; Mahya Safari
Abstract
Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by ...
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Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by environmental and operational factors, this study aims to develop an Adaptive Bit Wear Rate Predictor (ABWRP) algorithm for estimating the BWR during drilling operations for new wells. The structure of this algorithm consists of a data transmitter, data processor, deep learning-based bit wear rate estimator, and a bit wear updating module. To develop a model for the BWR estimation module, data from two wells in an oil field in southwest Iran were collected and analyzed, including petrophysical data, drilling data, and bit wear and run records. Both studied wells were drilled using PDC bits with a diameter of 8.5 inches. After preprocessing the data, the key factors affecting the bit wear rate were identified using the Wrapper method, including depth, confined compressive strength, maximum horizontal stress, bit wear percentage, weight on bit, bit rotational speed, and pump flow rate. Subsequently, seven machine learning (ML) and deep learning (DL) algorithms were used to develop the bit wear rate estimation module within the ABWRP algorithm. Among them, the convolutional neural network (CNN) model demonstrated the best performance, with Root Mean Square Error (RMSE) values of 0.0011 and 0.0017 and R-square (R²) values of 0.96 and 0.92 for the training and testing datasets, respectively. Therefore, the CNN model was selected as the most efficient model among the evaluated models. Finally, a simulation-based experiment was designed to evaluate the performance of the ABWRP algorithm. In this experiment, unseen data from one of the studied wells were used as data from a newly drilled well. The results demonstrated that the ABWRP algorithm could estimate final bit wear with a 14% error. Thus, the algorithm developed in this study can play a significant role in the design and planning of new wells, particularly in optimizing drilling parameters while considering bit wear effects.
