LEE- Petrol ve Doğal Gaz Mühendisliği-Yüksek Lisans
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ÖgeForecasting the performance of shale gas wells using machine learning(Graduate School, 2023)Utilization of real data to develop data-driven models in the petroleum industry has gained momentum in the past decade. The challenges related to modeling unconventional reservoirs has been recognized as the driving force behind this change in approach. Data-driven models help to enhance operations, increase efficiency and save time. In the meantime, several numerical reservoir simulators are used for modeling and forecasting the performance of shale gas wells. However, these models are computationally expensive and the simulators could indirectly face with difficulties in forecasting performance for the unconventional shale reservoirs comparing to conventional ones. This study employs a data analytics approach to investigate and gain understanding into the main driver parameters that influence the gas production performance in unconventional reservoirs (i.e. cumulative gas production after one year). The dataset utilized in this study is acquired from SPE Data Repository and consist of 53 wells (SPE, 2021). The study essentially utilized two primary methods, namely exploratory data analysis (EDA) and predictive data analytics modeling. Through the utilization of exploratory data analysis (EDA), the correlation between each reservoir and operational parameter with the cumulative gas production (Gp) is clearly identified. A number of reservoir and operational parameters display a strictly monotonic relationship with the gas production. Out of all variables, gas saturation was the variable, which demonstrated the strongest correlation. Furthermore, predictive data-analytics models based on statistical and machine learning algorithms were developed to forecast the cumulative gas production after 1 year. Among the five conducted models, extreme gradient boosting machine (XGBoost) proved to be the optimal technique for forecasting gas production, as it yielded the highest Coefficient of Determination (R2) and the lowest Root Mean Square Error (RMSE). Finally, an analysis of variable importance was conducted to determine the key variables, which have the highest predictive power in forecasting gas production performance in unconventional shale reservoirs. The operational parameters such as the number of stages, lateral length and bottom perforation along with reservoir properties such as gas saturation, porosity and thickness are more dominant than the other reservoir and operational parameters. Gas saturation is the most critical parameter, which is considered as the key driver of forecasting the gas production. The findings of this study will be beneficial in the design and development similar forecasting modelling projects.
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ÖgeMachine learning based selection of candidate wells for extended shut-in due to fluctuating oil price(Graduate School, 2024-07-10)Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant drops in the prices, shutting in wells for extended durations such as 6 months or more may be considered for economic purposes. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 170+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. It is aimed to solve this decision-making problem through unsupervised machine learning with the help of the data obtained during production. Average reservoir characteristics at well locations, well performance statistics and well locations were used as potential features that could characterize similarities and differences among wells. After a multivariate data analysis that explored correlations among parameters, clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features. Using the field's reservoir simulation model, scenarios of shutting in different groups of wells were simulated. 3 years of forecasted reservoir performance was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that well performances can be easily characterized in the 3-cluster case as low-, medium- and high-performance wells. Analyzing the forecasting scenarios by considering "NPV per active well" and "NPV from Cash Flow" parameters showed that shutting in all low-, high- and medium-performance wells altogether during the downturns results in better economic outcomes for "NPV per active well". However, shutting in high- and medium- performance wells altogether and operating only low- performance wells during the downturns results in better economic outcomes for "NPV from Cash Flow". The results show that the "NPV from Cash Flow" parameter is most sensitive to the oil price during the high price period, while the "NPV per active well" parameter is most sensitive to the number of wells shut-in during the low oil price period. This study demonstrated the effectiveness of unsupervised machine learning in well classification, particularly for the problem studied. Operating companies may use this approach for selecting wells for extended durations of shut-in in periods of low oil prices.
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ÖgeDevelopment of combustion tube experimental setup for underground coal gasification(Graduate School, 2021)Traditional methods of energy production using coal are mainly based on obtaining coal by surface and underground mining. Later, the obtained coal is burned in thermal power plants and used to produce steam in steam boilers, but it is also used in homes for heating purposes. During surface and underground mining, miners work in very difficult conditions, risking their lives. Unfortunately, there have been many fatal accidents in coal mines in the past. In addition, not using enough filters in the chimneys of thermal power plants and houses, and insufficient control cause various environmental problems. The underground coal gasification process provides relatively cleaner and safer utilization of coal compared to traditional methods. The underground coal gasification process is mainly based on the production of synthesis gas (syngas) formed by gasification of the coal in situ. In the former Soviet Union, important studies were carried out, but these studies were interrupted by the cheaper production of natural gas. Significant field and laboratory work have subsequently been carried out in the United States, Europe, China, and Australia. The only operating plant on an industrial scale is the Angren underground coal gasification plant in Uzbekistan, a remnant of the former Soviet Union. After a suitable coal seam is determined for the underground coal gasification process, the production and injection well pairs are drilled into the determined coal seam. Then if it is not adequate, a connection is created between the wells to ensure gas flow between the wells. Air, oxygen-enriched air, or steam-oxygen mixture can be injected from the injection well to partially oxide coal. During the gasification of coal, several reactions take place. The drying of coal, pyrolysis and gasification processes are the main processes. First, the moisture inside the coal evaporates in the drying phase and is followed by pyrolysis. Second, with the increase in the temperature coal decomposition occurs which is also called pyrolysis. Char forms as a result of pyrolysis and char gasification take place as the final step. These processes occur simultaneously as the oxidant injection continues. As the coal gasifies, a cavity is formed in the gasification region and the cavity grows during gasification. As a result of the gasification of coal, syngas is released. Syngas mainly contains carbon dioxide, carbon monoxide, methane, and hydrogen. The released syngas is produced from the production well and transferred to the facility on the surface. Later, the produced syngas is used for various purposes as electricity generation, methanol, hydrogen, and synthetic fuel production. The underground coal gasification process depends on several different parameters, especially the coal type. Coal properties vary between coals of different ranks. Due to the complex structure of coal, it can exhibit a heterogeneous behavior even within the same seam. For these reasons, the efficiency of underground coal gasification may differ in different coal ranks and different coal fields of the same rank. The depth of the coal seam, the formation, and the geological structures around it also affect operational efficiency. In addition to the formation characteristics, the operation parameters also affect the efficiency of the underground coal gasification process. Producing a maximum amount of syngas with a high heating value by gasification of minimum coal can be described as process efficiency. Injected fluid, injection pressure, connection method between wells, and the method used for production are the operational variables that affect the efficiency of the process. Field and laboratory studies are carried out to improve the understanding of the physical and chemical properties of the underground coal gasification process and to make industrial-scale applications with the information obtained. Field studies initiated with the former Soviet Union later gained momentum in the United States. The effects of variables such as distance between injection and production wells, connection method between wells, different coal ranks, different operating pressures, and the method used for production on underground coal gasification are tried to be better understood with these field and laboratory studies. At the same time, the growth rate and geometry of the cavity that is to be formed during underground gasification are also examined in this field and laboratory studies. In this way, possible subsidences are tried to be prevented. Before initiating the pilot-scale experiments and industrial-scale operations, laboratory studies are required to determine the optimum operation parameters and coal properties. Thus; the underground coal gasification process can be accomplished in an efficient and secure way. In laboratory experiments for underground coal gasification, the coal sample can be taken as a block and block tests can be carried out. However, it is not always possible to obtain block samples. In the case of deeper seams, the samples are obtained through the well as the core. Gasification experiments can also be performed by packing the coal particles in a tube. Previous combustion tube experiments have some drawbacks in terms of their design. First, some of the previous combustion tube experiments for the UCG were executed by providing an adiabatic environment in the reactor. In these experiments, the system is designed such that heat losses are almost zero. The temperature outside the reactor tried to be equal to the temperature inside the reactor by heating the reactor from outside. While this experimental design provides fine temperature behavior throughout the experiment, it does not represent the underground conditions. There is a considerable amount of heat loss through the surrounding formations. Second, previous combustion tube experiments do not have any H2S filter. The experimental design of this study includes an H2S filter. Furthermore, heat losses were tried to minimize; however, the reactor used in this study is not adiabatic.In adiabatic operation of combustion tubes it is not clear if a self sustained combustion front is achieved since the reactor is heated externally. The provided heat could have an impact on the combustion front behavior. It is hard to differentiate the effect of external heating. In this study, a combustion tube experimental setup was developed for underground coal gasification experiments through the adaptation of combustion experiments conducted for in situ combustion of oil petroleum fıelds. Content of the syngas and the progression speed of the combustion front were followed. The amount of produced gas was not measured in this study. Dimensions of the combustion tube to be used for coal gasification experiments were determined by examining the synthesis gas content and the slopes of the temperature profiles in front of and behind the combustion zone. While determining these measurements, the situation where deviations in the composition of the syngas are small, and the slope of the temperature profile in front of and behind the combustion zone does not change was taken as a stable period. During the study, parameters such as injected fluid, coal rank, and operating pressure were also examined. One of the important factors affecting the UCG operation is the content of the injected fluid. In the literature and different field applications, gasification has been carried out using air, oxygen-enriched air, and a vapor-oxygen mixture. In a part of this study, the effect of the amount of oxygen in the injection fluid on the gasification of coal was investigated. According to the results of the experiment, it is observed that the CH4, H2, CO, and CO2 contents of the produced syngas increases with increasing oxygen content in the injected fluid. Hence, the heating value of the syngas increases with increasing oxygen content in the injected fluid. The syngas produced during the gasification of coal contains various corrosive gases, primarily H2S. The main source of released H2S is sulfur in coal. There are different techniques such as caustic scrubbing, metal oxides, and alkaline impregnated activated carbon to trap the released H2S. In this study, a caustic scrubbing column is used to mitigate H2S production. Before the syngas is released into the atmosphere, it enters this column and is purified as much as possible from the H2S. However, the H2S amount at the inlet and the outlet of the scrubber could not be measured since neither in the gas chromotagraph or the gas analyser H2S measurements are available. After the introduction of the scrubber, a substantial decrease in the rotten egg smell indicated qualitatively the decrease in the H2S concentration. In conclusion, a combustion tube experimental setup that was developed for the in-situ combustion of oil experiments was modified for the underground coal gasification experiments. The experimental setup is updated by adding an H2S scrubber and appropriate sand filters. Recommendations are provided for the common problems faced in the experiments such as clogging of the sand filter and condenser and occurrence of the second combustion front. Using a liquid trap with a longer length with a higher volume can aid the condensation of the fluid inside the gas. This can be a solution for the clogging of the sand filter and condenser since the liquid trap also acts as a condenser that keeps the excess amount of liquid. Furthermore, the effect of the O2 content of the injected fluid was also examined. It is experienced that an increase in the O2 content of the injected fluid enhances the methane, hydrogen, and carbon monoxide content of the syngas. The stable behavior can be observed for a 1 m length 7 cm diameter combustion tube with an injection rate of 6 lt/min for the lignite samples used.
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ÖgeCombustion kinetics of asphaltites compared to coals through isoconversional analysis(Graduate School, 2024-07-01)Asphaltites are dark-colored solid petroleum, classified as a group of Bitumens based on the latest classifications; this is disputable. They are formed by alteration during or after migration, generally in oil-bearing zones. They are primarily observed in petroleum provinces. In Turkey, they are extensively observed in the southeastern and some northern parts and are commonly produced by surface mining techniques for electricity generation. On the other hand, gilsonite, a form of asphaltite produced in the US (Uinta Basin of Northeastern Utah), is used as an additive for different purposes. Asphaltites originated from conventional oil. However, they have coal-like properties, such as ash content. It would be worthwhile if kinetic signatures could be compared to coal and petroleum. In this way, a more persistent picture of asphaltite combustion characteristics is clarified. Understanding this helps to decide on information about the reaction scheme, activation energy, and general combustion behavior, and finally, the reactions probably taking place in this procedure of newly trend alternative source of energy. For this purpose, a similar set of experiments is carried out with coal, and using literature, it is compared to petroleum, respectively. This experimental work analyzes the combustion characteristics of asphaltite from the Şırnak province of Turkey through kinetic-cell experiments. Five sets of ramped temperature experiments are conducted using a 2 g asphaltite sample with different heating rates. These experiments measure the temperature inside the cell and the effluent gas composition, primarily CO, CO2, CH4, and O2 concentrations. The results indicate two O2 consumption peaks at different temperatures, corresponding to High-Temperature Oxidation (HTO) and Low-Temperature Oxidation (LTO). Before doing any analysis, some comments compared to coal and asphaltite raw data, such as the initial temperature of LTO and HTO reactions, oxygen consumption, and carbon dioxide production rates. Then, the results are analyzed using isoconversional methods. These methods require a set of experiments conducted at different heating rates and result in a so-called isoconversional fingerprint (Conversion versus activation energy). The objective of the kinetic analysis is obtaining the activation energy without assuming any kinetic model under the isoconversional assumption that the reaction rate at a constant extent of conversion is only a function of temperature. This fingerprint provides information on the reaction scheme. The same analysis was repeated using carbon monoxide and carbon dioxide production data. Carbon monoxide data does not show reliable and sufficiently accurate results. However, taking carbon dioxide data into analysis contributes significantly to figuring out the reaction scheme that might have taken place in the gasification process. Considering that asphaltites are mostly treated as coals in some regions and literature, and the procedures and processes applied to them are almost the same, it is expected to show further similarities with coals. Yet, regarding classifications, crude oil shows a closer relationship to asphaltites. Asphaltites have been proven to exhibit complex behavior regarding activation energy trends, making it hard to comment on their characteristics or reaction schemes. Therefore, a commercial simulator is used to model the gasification experiments based on coal reactions to get a better understanding. Using this commercial simulator determines the behavior of coal in the gasification process and provides a better understanding of the governing reaction. Reservoir and sample characteristics and properties, geology, reactions, heating systems, imposed heating rates, and kinetic parameters are entered as input data into the simulator. The simulation showed that carbon oxidation plays the most crucial role in the gasification process in terms of both activation energy determination and consumption rates. Furthermore, these reactions occur in the most critical phase of the gasification procedure, namely, low and high-temperature oxidations in the intermediate gasification steps. Moreover, almost all the main gasification reactions occur in the last gasification phases. Therefore, it can be concluded that the extent of the gasification of coal and asphaltite tends to behave the same in the previous phases, and the mechanisms and reactions in the final steps of asphaltite gasification are similar to coal.
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ÖgePrognostic reserve estimation for potential reservoirs where geochemical methods proven the hydrocarbon presence by Monte Carlo simulation: Case studies from Turkiye(Graduate School, 2023-01-16)The aim of this thesis is to calculate the prognostic reserve in areas where drilling and production have not been done before. A literature review of more than 100 wells and reservoir rocks around the world shows that the known values for the volumetric method of calculating reserves in sites with the same reservoir rock range only between two minimum and maximum values. Sometimes, even though these figures are very different from each other, they show that sites with the same reservoir rock are worth around 80%. Reservoir rocks collected from the open source literature around the world for the thesis study are divided into two separate classes as siliciclastic and carbonates. Studies show that most of the porosity, reservoir thickness and hydrocarbon saturation values collected for siliciclastic and carbonate rocks are similar values. For this reason, in this study, the same rocks (siliciclastic and carbonate) from the literature were obtained from the literature, using only potential reservoir rock types (siliciclastic and carbonate) and potential reservoir area, without using any laboratory or production data in 7 potential hydrocarbon reservoir areas in Turkey where drilling has not been done before. The underground prognostic reserve volume was calculated using computer simulation with the help of the values collected for As a result of this research, which was carried out by using Monte Carlo Simulation and a comprehensive literature review in 7 different areas in Turkey, 3 very small gas resources, 1 small gas resource and 3 super giant oil resources with 90% probability; 50% probability 1 medium, 2 small, 1 very small gas and 3 super giant oil resources; and finally, it has been determined that 1 large, 2 medium, 1 very small gas resources and 3 super giant oil resources can be found with 10% probability.