LEE- Petrol ve Doğal Gaz Mühendisliği Lisansüstü Programı
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Sustainable Development Goal "Goal 9: Industry, Innovation and Infrastructure" ile LEE- Petrol ve Doğal Gaz Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeArtificial neural network tool development for flue gas sequestration in depleted shale oil reservoirs(Graduate School, 2023-01-27) Bilim, Yasin Burak ; Kulga, Burak ; 505191511 ; Petroleum and Natural Gas EngineeringThe energy needs of societies are increasing day by day. Oil and natural gas use have become more common with developing technology to meet this demand. However, this consumption also brings some concerns. The negative effects of fossil fuels on the environment, especially greenhouse gas (CO2, CO, N2O, H2S) emissions, have become a cause for worry. Today, the concentration of carbon dioxide in the atmosphere is increasing. Sequestration of flue gas released from factories and power plants is one of the methods used to reduce the rate of greenhouse gases in the atmosphere. In this study, an artificial neural network (ANN) tool that predicts the sequestration of flue gas has been developed. With the developed tool, production estimation can be made for hydraulically fractured shale oil reservoirs with horizontal wells in addition to the flue gas injection time and pressure. The reservoir model used in this study has some key features, such as the stimulated reservoir volume approach, the double porosity double permeability phenomenon and Langmuir adsorption isotherm formulation. After creating the desired reservoir model, minimum and maximum value ranges were taken from the literature for reservoir characteristics and operational parameters. According to these intervals, 10,000 different models with normal distribution were generated randomly and simulated with the CMG GEM simulator. Randomly generated scenario variables and obtained results from the simulation were used in artificial neural network training as input and output values. An artificial neural network, a machine learning method, consists of input, hidden, and output layers. Each layer contains artificial neurons, interconnected like neurons in the biological nervous system, forming the artificial neural network structure. A weight is assigned to the data entering the input layer and processed using an activation function, and output is acquired as a result of the calculations. This study used the hyperbolic tangent function as the activation function. In addition, functional links are used to improve the relationship between input and output. Python and MATLAB programming environments were used to develop the artificial neural network tool. As the training algorithm, the RMSProp function in the Keras library was used in the Python model, and the trainscg and learngdm functions were used in the MATLAB model. In order to minimize the differences arising from the different algorithms used by the models, the ANN structure and the data set used are the same. Accordingly, three hidden layers and 70, 90 and 40 neurons were used in these layers, respectively, in both models. In addition, the data set containing 39 input and 73 output parameters were divided into 80% training, 10% validation and 10% test set. The tools obtained at the end of the training were tested using test sets, and the error rates in the estimations of variables such as shale oil production curves, flue gas injection pressure and injection stopping time were calculated. These error rates were found to be 5.47% for the Python model and 2.54% for the MATLAB model.
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ÖgePrediction of flow rates from different entries using PLT p-T measurements in a horizontal well by machine learning methods(Lisansüstü Eğitim Enstitüsü, 2022-12-13) Çevik, Muharrem Hilmi ; Çınar, Murat ; 505191508 ; Petroleum and Natural Gas EngineeringOil, which is a source of energy and raw materials, has been maintaining its importance for almost a century, and although humanity has sought alternatives, it has not been able to find a source of energy and raw materials that can replace oil and natural gas to date. The origin of the oil production process is more than a century old. Oil, which was first produced with vertical wells, can now be produced from deviated and horizontal wells with the development of technology. Today, horizontal wells have become quite common. Production logging is basically a logging method used to determine how much fluids (water, oil, gas) flow through which intervals. It is possible to measure parameters such as flow profile, pressure, temperature, ID of the well thanks to traditional production logging tools. Production logging tools, originally developed for vertical wells, have lost their function with the spread of deviated and horizontal wells. Because the flow profile in deviated and horizontal wells is quite complex compared to vertical wells. Data obtained with traditional tools are often unhealthy or incomplete data. New devices have been developed to eliminate this disadvantage. Thanks to these devices, velocity and hold-up distribution across the cross-section can be obtained. However, incomplete-inaccurate data due to mechanical problems and high costs are their disadvantages. The spread of horizontal wells allows oil and natural gas production at high flow rates. It is quite obvious that this situation is very advantageous from an economic point of view, but it also brought with it a number of technical problems. Due to the high flow rate, the pressure drop due to friction increases along the well; thus, creating an unbalanced flow profile. Eventually, water and gas coning may occur. Fortunately, ICDs (inflow control device), a product of advanced sounding technology, help solve these problems. Machine learning is the ability to learn and improve a model using data input. It provides a better understanding of the data set by making inferences from the existing data set and allows predictions to be produced using input data. Another feature of machine learning is that it is fast. It can explain analyzes that can take days in a very short time using the right model. Machine learning provides a wide range of uses that can affect many areas such as image process, classification and linear regressions. A substantial amount of data is also obtained in the oil and gas sector. This indicates that the applications of ML in the oil and gas sector will increase. In high flow wells, the roughness value becomes important as the pressure drop due to friction is high. Also, the fact that the roughness is an unknown parameter, moreover, its variation with time makes it difficult to determine. In this study, a constant flow rate interval is determined. Then, the pressure difference due to inflow-outflow and acceleration is neglected. Since the well model is created by considering the pressure difference due to gravitation, only a pressure difference due to friction is detected in this constant flow rate interval. Then, by applying the reverse solution, the relative roughness is calculated. Defining the flow profile correctly is critical for for each member of subsurface team. Although modern production logging tools are helpful in understanding the flow profile, it is prone to mechanical problems and moreover, logging with optimum conveyance requires long operation times and singinificant logging budget. Therefore, some researchers have used solely pressure and temperature data to estimate flow and determine the contribution of perforations. In addition to empirical and analytical approaches, ML applications have started to emerge recently. Considering both the rapid development of ML and its successful applications, the applicability of ML techniques to the oil and gas industry is promising. In this study, the flow rate of six production zones was estimated by using PLT pressure and temperature data obtained from two different measurements (high-choke-low-choke). The aim of this study is to produce a simple, practical and cost effective solution to the limitations of traditional Array PLT caused by mechanical problems and not working correctly in low flow regions. In this context, a well model was created and the contribution of the perforations of this model was adjusted according to the high-choke PLT (high flow) data. Various skin factors are assigned to perforations to make this adjustment. According to this model, synthetic data was produced and flow rate estimates were made for both measurements using machine learning techniques. Machine learning decision tree regression, linear regression, ridge regression and random forest regression were used for flow estimation with high-choke pressure data. 70% of the synthetic data is used for training and 30% for testing. Decision tree regression and random forest regression based on test score are the two methods that give the best results. The high-choke pressure data and the flow estimation are quite well and almost perfectly matched. Flow estimation with high-choke temperature data also gives a good result. As for flow estimation with low-choke pressure and temperature data, the test scores of both methods are above %90, but the estimates are far from low-choke flow data. However, the learning model of both ML algorithms is promising for estimating flow rate.