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.
Exploitation
Babatunde Adebayo; Blessing Olamide Taiwo; BUSUYI THOMAS AFENI; Aderoju Oluwadolapo Raymond; Joshua Oluwaseyi Faluyi
Abstract
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. ...
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The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations.
M. Capik; B. Batmunkh
Abstract
Modelling wear of drill bits can increase the efficiency in the drilling operations. Related to the subject, it is aimed to investigate the wear mechanism of drill bits. Wear in drill bits is influenced by many factors related to the drilling and rock properties. The type and intensity of wear are dependent ...
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Modelling wear of drill bits can increase the efficiency in the drilling operations. Related to the subject, it is aimed to investigate the wear mechanism of drill bits. Wear in drill bits is influenced by many factors related to the drilling and rock properties. The type and intensity of wear are dependent on several complicated factors that are required to be considered in anticipating the rate of wear in the field and laboratory conditions. The laboratory tests have been performed in order to specify the relationships between the bit wear rate and the physico-mechanical properties, drillability, abrasive properties, and brittleness of rocks. Statistical analysis has been used to obtain equations for estimating the bit wear rate based on the rock properties. In this work, an ensemble technique is used to estimate the confidence interval and the prediction intervals for the regression models. This paper summaries the rock properties and bit wear mechanism, and argues the options to determine the bit wear rate. The test models indicate that the rock properties can give an idea of bit wear. They also show a good correlation between the bit wear rates. Also some models are developed to represent the wear quantification, and an approach is suggested in order to estimate the bit wear rate. The results obtained from studying the developed models provide a good agreement with the performance evaluation of an efficient drilling, which provide an indirect evaluation of drill bit wear rate during a drilling process, which can help to reduce the specific energy consumption and lower costs for the exchange of drill bits.
