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    A review of mineral precipitation and effective scale inhibition methods at geothermal power plants in West Anatolia (Turkey)
    (Pergamon-Elsevier Science Ltd, 2019) Haklidir, Fusun S. Tut; Balaban, Tugbanur Ozen
    In water-dominated reservoirs, binary and flash cycle geothermal power systems are widely used for power generation at moderate or moderate-high reservoir temperatures. For effective management of both energy systems, it is critical to understand the effects of controlling pressure in different sections of a power plant as well as temperature changes and the pH levels of geothermal fluids. For advanced (multi-flash or flash + binary systems) systems, although the expectation of energy efficiency is higher than for flash and binary systems, the management of these systems is more complex because of high reservoir temperatures and multi-separation processes. These multi-steam separations result in dramatic pressure and temperature drops for geothermal fluids after each separation stage. Depending on the geothermal reservoir rocks, water-rock interaction can be inferred and possible scale types may be predicted through water-modeling programs. Based on this information, suitable inhibitors may be suggested after short-term field tests prior to start up processes for geothermal power plants. Western Anatolia has important high-medium geothermal systems that are suitable for power production, depending on large graben structures in the Aegean Extensional Zone. Geothermal power plants (Turkey) are generally designed as binary and flash types, based on the reservoir temperatures (in Germencik-Aydin, Alasehir-Manisa regions etc.) in Western Anatolia. The scale types may change with respect to geothermal fields even if they are in close proximity to each other. At the same time, in addition to specific scale types, some minerals are commonly found across each of the geothermal fields. The most important scale types in production wells and lines as well as surface equipment are carbonate minerals such as calcite and aragonite. In addition, silica minerals also tend to form scale, up to 150 degrees C. Scale types in reinjection lines and wells have been commonly identified as celestine, strontium and barite minerals mixed with silica and carbonates in some geothermal power plants. But these scale types also differ within the same reservoir, with well depth. Scale prevention inhibitors and inhibitor systems with different chemical properties are generally used for anti-scaling. Chemical inhibitor performance and mineral precipitation can be monitored both periodically chemical analysis and also using steel control coupons, which are put in at critical pressure drop points along pipelines. Controlling re-injection temperatures and pH levels have had success for anti-scaling. This study is mainly focused on mineral precipitation conditions and the most effective scale inhibition applications in different type geothermal power plants in Western Anatolia.
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    Characterization and Comparison of geothermal fluids geochemistry within the Kizildere Geothermal Field in Turkey: New findings with power capacity expanding studies
    (Pergamon-Elsevier Science Ltd, 2021) Haklidir, Fusun S. Tut; Sengun, Raziye; Aydin, Hakki
    The Kizildere geothermal field is the first known high-temperature geothermal field in Turkey. The field is located in the Buyuk Menderes Graben (BMG), Western Anatolia (Turkey). There are four production sections (reservoir-I, II, III, and IV) with different rock compositions and geochemical characteristics in the production area. Identifying and characterizing these production units may give a good insight into field development plans for a sustainable production. This study aims to characterize production sections of the Kizildere geothermal field using geology, hydrogeochemical properties such as chemistry, stable isotope, and non-condensable gas compositions. The results of gas and water analysis showed that geothermal fluids are of a meteoric origin in all producing sections. It was also found from the analyses that more intensive water-rock interactions take place as the reservoir temperature irises. Based on the different geochemical characteristics of each producing section, we thought that impermeable sediments like mica-schists and chlorite-schists lying between the reservoir units act as a seal. In light of this study, it can be concluded that the Kizildere geothermal reservoir is very heterogeneous due to vertically compartmentalized reservoir sections that require a unique injection-production configuration for a sustainable production lifetime.
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    Prediction of geothermal originated boron contamination by deep learning approach: at Western Anatolia Geothermal Systems in Turkey
    (Springer, 2020) Haklidir, Fusun S. Tut; Haklidir, Mehmet
    Geothermal fluids consist of hot water, steam and gases in water-dominated reservoirs. They contain various dissolved major elements such as sodium, potassium, calcium, silica, bicarbonate, carbonate, chlorine, sulphate and minor elements such as boron, fluorine, lithium, iron, arsenic, mercury and bromine at different concentrations in the liquid phase. The concentration of dissolved solids depends on the temperature, gas content, reservoir geology, permeability, water mixing and fluid source of a geothermal system. Some of these species exhibit a toxic effect at high concentrations and require precaution after the discharging of geothermal water. Boron is one of the important constituents and can be observed as boric acid (H3BO3) or HBO2 in the water phase. The concentration of B changes between 10 and 50 ppm in chloride-type fluids and can occur in greater quantities than these values in organic-rich sedimentary rocks in geothermal fluids. Although boron is considered toxic, it is also one of the crude minerals and can be used in different industries, such as oil and gas chemistry, vehicle technologies, agriculture, ceramics, and adhesive and coating, among others. Machine learning is a method of data analytics for identifying patterns in data and using them to automatically make predictions about new data points. Deep learning is a machine learning subset that uses artificial neural networks with multiple layers. Deep learning can automatically learn representations from data without hand-coded rules or domain knowledge; this is the primary difference between deep learning and traditional machine learning techniques. In this study, a deep neural network model has been developed to predict boron concentrations based on hydrogeochemistry data for different geothermal systems. To compare the prediction performance of our proposed deep neural network model, two well-known regression approaches, linear regression and linear support vector machine (SVM), were performed, and the results have been presented. The performance comparison revealed that our deep neural network (DNN) model achieved better prediction performance than traditional machine learning techniques-linear regression and linear SVM.
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    Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach
    (Springer, 2020) Haklidir, Fusun S. Tut; Haklidir, Mehmet
    Geothermal fluids can be used for purposes such as power production, district heating/cooling, agriculture, and industrial and thermal tourism. Although using geothermal fluids is beneficial, it requires detailed exploration studies of a region. These exploration studies mainly involve geology, geophysics and geochemistry disciplines to understand the location, dimensions, possible capacity and temperature of a reservoir before beginning drilling operations. Because of the high operational costs, the exploration phase of a geothermal project is of great importance to reduce project costs. Evaluation of existing earth sciences data, detailed geology studies, mapping and some geochemical studies, such as using geothermometers, can provide information about a potential geothermal reservoir in a geothermal field. Machine learning is a technology for data analysis which identifies patterns in data and uses them to make predictions about new data points automatically. In this study, a deep learning model is developed to predict geothermal reservoir temperatures based on selected hydrogeochemistry data from different geothermal systems. Two traditional regression approaches, linear regression and linear support vector machine, are performed to compare the prediction performance of our proposed deep learning model. The objective of the study is to obtain the algorithm having the lowest root-mean-square error and mean absolute error. The results show that the deep neural network (DNN) algorithm generated the lowest errors. The DNN model provided the most accurate values close to geothermometer calculations for reservoir temperature. The performance comparison showed that our deep learning model achieved the best prediction performance compared to traditional machine learning techniques.