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.
Exploration
Mustafa Yasser Elgindy; Ahmed Zakaria Nooh; Ali Mostafa Wahba
Abstract
Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify ...
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Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify the kick after a large amount of influx has been recorded on the surface. So, we aim to recognize the kick before it enters the wellbore by detecting the abnormal formation pressure once the bit penetrates the rock. This paper proposes a new machine learning model as an alternative solution using field drilling parameters such as true vertical depth, porosity, bulk density, resistivity, rate of penetration, weight on bit, rotation per minute, torque, standpipe pressure, flow rate, flowline temperature, and gas level. The data-driven models were developed using three separate algorithms: K-Nearest Neighbor, Random Forest, and XG Boost. 6022 field data points were split for training, testing, and validation processes. On average, the model using the random forest algorithm showed the highest accuracy in forecasting the formation pressure, with root mean squared error values and coefficient of determination values of 12.868 and 0.9638, respectively. Streamlit Deployment tool was used to create a user interface program to facilitate the prediction process. The program was tested using 196 field data points and recorded a high accuracy of 95%.
Exploration
Mina Shafiabadi; Abolghasem Kamkar Rouhani
Abstract
Considering the effect of fractures in increasing hydrocarbon recovery, the study of reservoir rock fractures is of particular importance. Fractures are one of the most important fluid flow paths in carbonate reservoirs. Image logs provide the ability to detect fractures and other geological features ...
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Considering the effect of fractures in increasing hydrocarbon recovery, the study of reservoir rock fractures is of particular importance. Fractures are one of the most important fluid flow paths in carbonate reservoirs. Image logs provide the ability to detect fractures and other geological features and reservoir layers. In this study, two approaches were used to detect fractures using FMI image log in two wells A and B located in one of oilfields in southwest of Iran. In the first stage, the correction and processing of the FMI raw data were carried out to identify the number and position of fractures, as well as the dip, extension, classification, and density of fractures. In the second step, by considering that the fractures possess the edges in the FMI images, various edge detection filters such as Prewitt, Canny, Roberts, LOG, Zero-cross and Sobel were applied on the image data, and then, their performances for identification of fractures were compared. Finally, the automatic identification of fractures was done by applying the Hough transform algorithm and the results showed that Canny algorithm was the best option to perform Hough transformation. The comparison of the efficiency of the above-mentioned edge detection filters for identification of fractures, and more importantly, the automatic identification of fractures using the Hough transform algorithm can be considered as the novelty of this research work.
