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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)Oil, 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.
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ÖgeArtificial neural network tool development for flue gas sequestration in depleted shale oil reservoirs(Graduate School, 2023-01-27)The 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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ÖgeTracking pressure, hydraulic and thermal fronts in porous media(Graduate School, 2022-01-21)Geothermal energy is the heat energy stored in the subsurface. It is a clean, renewable, and sustainable energy source. Therefore, geothermal energy is a popular energy resource in the world. There are two types of utilization of geothermal energy which are direct use and indirect use. Geothermal energy is used directly for space heating, greenhouse heating, tourism, etc. However, heat energy is converted to another type of energy for indirect utilization. The main purpose of indirect utilization is electricity production. Geothermal power plants are used to convert heat energy to electricity. There are three types of geothermal power plants which are dry steam power plants, flash steam power plants, and binary power plants. For sustainable management of a geothermal resource, future performance predictions must be made. This requires good reservoir engineering practices and good reservoir characterization. One of the ways of characterizing the reservoir is by way of using tracers. Generally, tracers are made up of material that does not exist in the geothermal reservoir. Almost all of the geothermal fields in Turkey contain some amount of carbon dioxide. The carbon dioxide is usually dissolved in the geothermal water in various mass fractions. Depending on the amount, carbon dioxide can have a significant effect on production performance. Because of reinjection operations (where water with either little or no carbon dioxide is reinjected), the amount of carbon dioxide in the reservoir decreases. Depending on the reinjection amount, the produced carbon dioxide from wells also decreases once reinjected water reaches the production wells. This provides the opportunity to treat the carbon dioxide data as tracer data. Analyzing the decline of carbon dioxide at the production wells would provide a better characterization of the reservoir. Hence a model is necessary to model the decline of the carbon dioxide level. When reinjection operations are carried out, usually there are three fronts involved: the pressure front, hydraulic front, and thermal front. In this study, a model is developed to analyze how the fronts propagate in the reservoir. In the mathematical model, mass balance on the water, mass balance on carbon dioxide, and overall energy balance are applied to model pressure, temperature, and mass fraction of carbon dioxide in the geothermal reservoir. The model developed is a numerical model where the reservoir is split into grid blocks and mass and energy equations are solved simultaneously. To track pressure, thermal, and hydraulic fronts, the geothermal reservoir is divided into 175 homogenous grid blocks. These grid blocks are hydraulically connected with each other. In this study, the effects of injection operation and some petrophysical properties on the displaced pressure, thermal, and hydraulic fronts are studied. It is important to note that there are several assumptions. First, the geothermal reservoir is assumed to be a liquid dominated geothermal reservoir. Second, it is assumed that there is a 1D linear flow. Furthermore, it is important to note that injection is operated with a constant mass flow rate. Finally, the impact of carbon dioxide diffusion is ignored. Analytical equations of the breakthrough time of both thermal and hydraulic fronts are provided. Comparison of numerical and analytical solutions of these fronts are also provided.
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ÖgeNew analytical model for underground storage of natural gas with carbon dioxide as cushion gas and for sequestration of carbon dioxide(Graduate School, 2023-10-18)Natural gas is a strategically important, valuable fuel used in heating, industry and transportation. Natural gas is the smallest member of hydrocarbon paraffins. While some countries produce and export surplus natural gas, some countries are dependent on import of natural gas. Turkey is a country in need of imports for natural gas. For this reason, some of the imported natural gas is used, while the unused portion is stored for use when needed. One of the natural gas storage methods is to store natural gas underground. Depleted natural gas and oil reservoirs and salt domes can be used for underground storage. Storage of natural gas is very important for countries due to seasonally changing gas demand, fluctuations in gas prices and strategic reasons. Although the stored gas is generally methane, not all of the stored natural gas can be produced due to the pressure difference between the reservoir and the surface. Some of the stored natural gas is left in the reservoir as base gas to create pressure support. This leads to economic loss. Using carbon dioxide instead of methane as cushion gas provides significant economic, environmental, and operational benefits. In this study, the effects of using carbon dioxide as cushion gas were investigated. The physical properties of carbon dioxide and methane such as density, compressibility and compressibility factor were investigated. Although the denser of the two gases with different densities in the tank sinks to the bottom and the other one is at the top of the tank, the area between the two gases where these two gases form a homogeneous solution is called the mixing zone of these two gases. Since there will be a region consisting of a mixture of these two gases as a transition zone in a reservoir containing carbon dioxide and methane, the compressibility factor of the mixture region containing different percentages of carbon dioxide and methane was calculated using Peng Robinson Equation of State. Since looking at the physical properties, the compressibility of carbon dioxide at temperatures between 60-120 bar and 50-70 °C is higher than that of methane, it is concluded that using the same amount of carbon dioxide as cushion gas by volume gives significantly beneficial results in terms of pressure optimization and increases the amount of methane produced. Since carbon dioxide is cheaper than methane, its use as cushion gas may give satisfactory results both economically and environmentally in natural gas storage reservoirs. It is seen that the use of carbon dioxide as an enhanced gas recovery method as cushion gas in methane storage and production is more efficient in terms of reservoir management and economy. In this study, how the pressure changes during methane production in gas reservoirs containing carbon dioxide and methane as cushion gas for different production scenarios is observed by the use of the new material balance equation presented by Tureyen et al., (2023). Thermodynamically, how methane and carbon dioxide affect the reservoir properties and how they change with different initial reservoir pressures, molar percentages of methane and carbon dioxide, temperatures, and production scenarios are investigated. As a result, it has been observed that the use of carbon dioxide as cushion gas in the temperature and pressure range of 50-70 °C and 60-120 bar increases the methane storage and production efficiency, which is, the working gas capacity. Considering the compressibility behavior of methane and carbon dioxide, it has been observed that the mixing zone containing the same volumetric ratio of methane and carbon dioxide shows a compressibility factor behavior closer to methane. For this reason, a new analytical equation was introduced by taking the mixing zone into account. CO2 is injected into a reservoir containing methane initially, followingly only methane is produced from the reservoir and average reservoir pressure change is observed during the injection and production stage with analytical models where one of the analytical models does not include the mixing zone into consideration and the other one does. CMG (Computer Modelling Group) is used to verify the results. It is seen that the analytical model which includes a mixing zone gives better results than the analytical model assumes no mixing zone in the reservoir. Finally, assuming that carbon dioxide will be located in the lower part of the reservoir and methane in the upper part of the reservoir due to the density difference, it is important to observe how the transition zone of methane and carbon dioxide changes with methane production. Since only methane production is targeted, it is important to follow the transition zone height in order to prevent carbon dioxide production. For this reason, the change in the height of the transition zone between carbon dioxide and methane with methane production in the reservoir containing 50% carbon dioxide and 50% methane for different reservoir shapes such as cylindrical, trapezoidal and hemispherical was investigated.
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ÖgeSimulation of carbon dioxide as a cushion gas in underground gas storage reservoirs(Graduate School, 2023-10-18)Fossil fuels are a part of the primary source of energy, and today the majority of industrialized and developing nations use oil, coal, and natural gas as their primary fossil fuels. Natural gas, one of these fossil fuels, is a versatile, efficient, clean-burning fuel that is utilized in a range of applications. The gas industry encompasses various sub-sectors that contribute to the overall expansion and maintenance of a reliable gas supply. One of these vital components is underground gas storage, which plays a crucial role in ensuring consistency in gas supply. Underground gas storage involves the practice of storing natural gas in reservoirs that have significant capacities. This strategic approach allows for the management of high import volumes during periods of low demand, as well as the provision of an adequate supply of natural gas during periods of high demand. The primary purpose of underground gas storage is to balance the fluctuating demand and supply dynamics of the gas market. By storing natural gas during times when demand is low, such as during the summer season or periods of reduced industrial activity, the excess supply can be stored underground in reservoirs. This practice helps to avoid the wastage of gas resources and ensures that the gas supply is readily available when demand increases. Moreover, underground gas storage facilities contribute to the overall energy security of a region or country. By maintaining a sufficient inventory of stored natural gas, countries can reduce their dependence on external sources of gas supply during times of geopolitical uncertainties or disruptions in gas imports. This enhances energy resilience and provides a buffer against potential supply disruptions, thus ensuring the uninterrupted functioning of industries, power generation facilities, and residential heating systems. Overall, underground gas storage is a critical sub-sector within the gas industry. It provides a means to balance supply and demand, manage seasonal variations, and enhance energy security. By investing in the expansion and maintenance of underground gas storage facilities, countries can increase customers' access to a reliable gas supply and strengthen their overall energy infrastructure. In the process of storing and withdrawing gas from an underground storage reservoir, certain considerations need to be addressed to ensure smooth operation. When it comes to withdrawing gas, it is crucial to maintain the average reservoir pressure above a certain value to ensure the fluent extraction of the stored gas. This is where the concept of cushion gas or base gas comes into play. The cushion gas refers to the amount of gas that needs to stay in place to maintain the required pressure levels. Traditionally, natural gas has been used as cushion gas due to its compatibility with the stored gas and the reservoir conditions. However, as alternative storage methods and gas management strategies have been explored, other gases such as carbon dioxide (CO2) have gained attention as potential cushion gases. Carbon dioxide offers several advantages as a cushion gas. Firstly, it can be readily available as a byproduct of industrial processes, making it an attractive option for utilization. Additionally, carbon dioxide can exhibit favorable thermodynamic properties, allowing it to function effectively in maintaining the reservoir pressure within the desired range. The selection and amount of the cushion gas depends on various factors, including the specific reservoir characteristics, gas storage requirements, and environmental considerations. Each gas has its own unique properties, and the choice of cushion gas should be made based on a comprehensive assessment of these factors. By employing an appropriate cushion gas, the gas storage facility can ensure that the average reservoir pressure remains above the minimum level required for efficient gas extraction. This allows for a reliable and consistent supply of gas during periods of high demand, contributing to the overall stability and effectiveness of the gas storage and retrieval process. The main objective of this study is to investigate the feasibility of utilizing carbon dioxide (CO2) as a cushion gas in an underground storage reservoir. In addition, the behavior of the mixing zone is also investigated. The simulation process is conducted using the Generalized Equation of State Model Compositional Reservoir Simulator (GEM), a software developed by the Computer Modelling Group. In this study, several scenarios are modeled using the simulation program. Each scenario represents a specific combination of reservoir conditions, including reservoir temperature, average reservoir pressure, and the compositions of carbon dioxide and methane within the reservoir. The simulation aims to provide a comprehensive understanding of how the reservoir behaves under various conditions when carbon dioxide is used as a cushion gas. By inputting the specific reservoir properties and gas compositions into the GEM simulator, the researchers can assess the performance of the reservoir in each scenario. The simulation results include data depending on factors such as reservoir pressure, reservoir temperature, and the behavior of the carbon dioxide and methane within the reservoir. These results will help to evaluate the suitability of using carbon dioxide as a cushion gas and determine the potential benefits or limitations of such a storage approach. Overall, this study contributes to the field of underground reservoir storage by investigating the use of carbon dioxide as a cushion gas, providing valuable insights into the dynamics of such a system and its potential implications for carbon dioxide storage and management strategies. Due to the different compressibility behavior of carbon dioxide at certain temperatures and pressure conditions, it can be both an advantageous and disadvantageous gas when it is used as a base gas. The results showed that carbon dioxide usage as a cushion gas at reservoir temperatures of 313.15 K, 323.15 K, 333.15 K, and 343.15 K and pressure ranges below 120 bar is quite beneficial as it provides pressure support because of its higher compressibility values than that of methane at these reservoir conditions. However, carbon dioxide loses its advantage when the initial reservoir pressure is increased to 180 bar since its compressibility is lower than the compressibility of methane at higher reservoir pressures. Moreover, the concentration of methane and carbon dioxide has a huge impact on the average reservoir pressure decline rate. Furthermore, results illustrate that the mixing zone length formed between working and cushion gas tends to extend with time. The mixing zone is assumed to be the part in which tracer concentration is between 0.1 and 0.9 and the length of the mixing zone for early, mid, and late time is 270 m, 458 m, and 567 m respectively. It was also observed that mixing zone length is proportional to the square root of dimensionless time.