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.
Exploration
Mahdi Bajolvand; Ahmad Ramezanzadeh; Amin Hekmatnejad; Mohammad Mehrad; Shadfar Davoodi; Mohammad Teimuri
Abstract
Shear Wave Slowness Log (DTSM) is one of the most important petrophysical logs applicable for studying reservoirs, especially geomechanical studying of the oil and gas fields. However, lack of this parameter in wellbore logging can import great sources of uncertainty into geomechanical studies. This ...
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Shear Wave Slowness Log (DTSM) is one of the most important petrophysical logs applicable for studying reservoirs, especially geomechanical studying of the oil and gas fields. However, lack of this parameter in wellbore logging can import great sources of uncertainty into geomechanical studies. This study aims to provide solutions for decreasing the uncertainty of geomechanical models with estimation of the DTSM log using the high accurate deep machine learning models. The main idea is using data from offset fields for extending the range of training data and improving the estimation ability and generalizability of machine learning models. For this purpose, petrophysical data from 8 wells of 4 Iranian oil fields were collected. In the first stage, data preprocessing was performed for reducing the effects of wrong data, missing value, noises, and outliers. Then, machine learning (regression learning-based and deep neural network-based) and analytical models implemented for estimating DTSM. The results indicated that the Gated Recurrent Unit (GRU) model with the values of 1.9 and 2.14 for RMSE and 0.99 for R-square had the most exact answers, for training and test data, respectively. Meanwhile, evaluation of the accuracy of the models on the validation well data indicated that GRU model with the values of 2.43 and 0.93 had been the most accurate model for RMSE and R-square, respectively. Accordingly, using a multi-field comprehensive data bank and applying machine learning methods are strongly recommended to estimate the DTSM, for the cases where limited offset data is available.
M. Alipour Shahsavari; P. Afzal; A. Hekmatnejad
Abstract
The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated ...
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The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated in UDMA. Factor analysis, Concentration-Number (C-N) fractal model and Local Linear Model Tree (LOLIMOT) algorithm used for this purpose. Factor analysis utilized in recognition of the correlation between elements and their classification. This classified data used for training the LOLIMOT algorithm based on relevant elements. The results of the LOLIMOT algorithm represent anomalies in areas with no lithogeochemical samples. Although, the C-N log-log plot for target elements were generated based on stream sediment and lithogeochemical samples which could be delineated mineral potential maps of the target elements. Results obtained by the LOLIMOT and fractal modeling show that the SW and the Eastern parts of the area are proper for further exploration of Cu, Pb, and Zn.
