This research is dedicated to make a foreseeing of the future state of the Kizildere Geothermal Field in order to suggest acceptable solutions to the current problems. The non-isothermal mechanism of the geothermal field is simulated for the pressure and temperature variables. For this purpose, a finite element model (696 four-nodal elements with 750 nodes) of the field is formulated by considering the geological conditions and the present wells already drilled in the area. Then the model is calibrated to the field for the natural state by using appropriate physical properties, boundary and initial conditions. Comparison of the simulated and the observed pressures and temperatures has emphasized a very successful calibration study. After the calibration, response of the field to the production and injection for the period of 1984-2006 has been simulated by applying a history matching study. History matching runs have yielded very good correlations between the observed and the computed values of the pressure and temperature variables. The calibrated and history matched model has been applied to the field to simulate the future performance of the field for different production and injection scenarios. In the first scenario the field is simulated for the next 10-year production period keeping the on-going production conditions. Then, the influence of the production of two new wells has been investigated in two different scenarios. In the forth scenario, the effect of injection from one of the production wells has been simulated.

Protective cultivation is widely used nowadays in order to increase crop yield by creating the optimum conditions such as temperature, humidity and CO2 content, irrespective of outside conditions. Since plant production doubles for every 10 degrees increase in temperature to a certain limit, this makes temperature a very important factor for optimum plant growth. In order to keep the greenhouse temperature constant during changes in outside conditions, heating and often cooling are required. Heating of a greenhouse can be done using different systems and design procedures. The applicability of different types of greenhouses is studied at the field local conditions, &Ccedil / avundur-&Ccedil / ankiri, Turkey. Required heating load was calculated that is due to infiltration and conduction through the greenhouse cover at a single design point, which is the minimum outside temperature. Two types of heating systems, soil heating system and bare tube system, were considered. Analysis of results showed that, &Ccedil / avundur geothermal field with 54 &deg / C fluid temperature is suitable for greenhouse heating. Although the existing well &Ccedil / -1 is capable of producing 47 l/s, the flow rate of geothermal fluid for greenhouse heating was limited by 35 l/s due to existing thermal facilities in the area. Among different glazing materials, plastic film covered greenhouses with double poly was found to be the most suitable in terms of heat load calculations. The maximum number of greenhouses (the area of each green house is 216 m2) that can be heated by &Ccedil / avundur Geothermal field was found to be 138 by considering soil heating with double poly glazing material. Annual heat load factor of geothermal energy for greenhouse heating in &Ccedil / avundur area was found to be as high as 96% depending on indoor design temperature and base load.

ABSTRACT CHARACTERIZATION OF KIZILCAHAMAM GEOTHERMAL FIELD BY TRACER TESTING Tevfik Kaya M.S, Petroleum and Natural Gas Engineering Department Supervisor : Assoc. Prof. Dr. Serhat Akin Co-Supervisor : Prof. Dr. Mahmut Parlaktuna September 2005, 107 Pages Kizilcahamam Geothermal Field which is 70 km far from Ankara, has been utilized for Geothermal District Heating System, 25 MWt, 2500 residences capacity, greenhouses heating, thermal facilities since 1994. The average production rate is 350m3/h during the heating season , 150m3/h during the summer season for hot water and yearly average reinjection rate is 114m3/h from the field. The long term projections has been studied concerning on expected pressure decline by matching 10 years field history data which contain dynamic level and temperature data. The pressure decline is 140 kPa in the field between 1999 and 2005 with the existing reinjection rate, if the existing rates do not change, the additional pressure decline which is 120 kPa will be occurred up to 2011. In order to get more information from the field, the fluorescein as tracer has been injected in to the MTA-1, and the samples were collected from the MTA-2, Fethi Bey, IHL-1 and IHL-3 for 3 months. The fluorescein concentration has been detected by using fluorimeter, and tracer concentration time plots were analyzed. The fluoreiscein was detected in short breakthrough time in MTA-2 and Fethi Bey which are close to reinjection well, breakthrough time is longer in IHL-1 and IHL-3. The interpretation of tracer test shows that there is communication between all wells. Tracer concentration time plots were compared with different mathematical models, the best match was obtained with multi-fractured model. These results show that Kizilcahamam field is not homogeneous field. It is expected that increasing the reinjection rate will decrease the pressure decline in the field.

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In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree-based models (e.g., XGBoost and Random Forest). We found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model. / Master of Science / Machine learning and artificial intelligence have transformed many research fields and industries. In this thesis, we investigate the applicability of machine learning and data-driven approaches in the field of geothermal energy exploration. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models, we show that machine learning can provide viable alternative solutions for geothermal energy mapping. First, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree based models (e.g., XGBoost and Random Forest). We find that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. Accuracy measures show that machine learning models are at par with physics-based models and can even outperform the thermal conductivity model. Second, we incorporate the thermal conductivity theory with machine learning and propose an innovative clustering-regression approach in the emerging area of physics-guided machine learning that results in a smaller error than black-box machine learning methods.

Master of Arts / This thesis is an exploratory study that aims to identify whether it is adequate to apply the current approach of considering policies related to geothermal energy under the general umbrella of renewable policies or we need to use a system-sector based approach specifically for geothermal energy systems. I have identified a total of twenty-three different policy types related to geothermal energy systems in U.S. states. To understand how geothermal related policies diffuse from one U.S. state to another, and, therefore, better design policies to promote the use of geothermal energy in U.S. states, we need to perform several diffusion studies. This process is time consuming and expensive. Thus, focusing on the most promising geothermal related policies, at least as a start, is crucial for future studies focusing on the diffusion of geothermal related policies between U.S. states. Therefore, this thesis focuses on the preliminary step of selecting a limited set of geothermal related policies for future policy diffusion studies. The main conclusions and answers provided in this thesis provide a strong support to the hypothesis that a system-sector based approach is needed when studying policies related to geothermal energy in U.S. states. I explicitly report that each of the three main geothermal systems is impacted by different set of policy categories and types. I also discuss that not all policies have the same impact on all sectors in which the geothermal energy is applied; in other words, the utilization of geothermal energy in the different sectors is promoted by different policies in distinguished ways. Moreover, the discussion in this thesis highlights the shortcomings of the common approach usually used in diffusion v studies of renewable energy policies. This approach considers all renewable energies as a general category, neglecting any potential impacts due to the unique characteristics of each renewable source. I show that, for example, the most popular policy types considered in policy diffusion studies for renewable energies are not the most significant ones for the different geothermal systems. I also highlight the fact that other policy types that are generally overlooked in policy diffusion studies of the generalized renewable energies are more significant for geothermal energy systems. These results indeed support my hypothesis regarding the importance of system-sector based approach when investigating geothermal energy policies.

The large proportion of energy used in the built environment has made improving energy efficiency in buildings, in particular their heating, ventilation, and air conditioning (HVAC) systems, a policy objective for reducing energy consumption and CO2 emissions nationally and internationally. Ground source heat pump (GSHP) systems, due to their high coefficient of performance (COP) and low CO2 emissions are consequently, receiving increasing attention. This work is concerned with the modelling of borehole heat exchangers (BHEs), the commonest form of ground heat exchangers found in GSHP systems. Their careful design is critical to both the short timescale and long timescale performance of geothermal heat pump systems. Unlike conventional components of HVAC systems, BHEs cannot be designed on the basis of peak load data but require 3 application of dynamic thermal models that are able to take account of the heat transfer inside the borehole as well as the surrounding ground. The finite volume method has been applied to develop a dynamic three-dimensional (3D) model for a single BHE and BHE arrays. The multi-block boundary fitted structured mesh used in this model allows the complex geometries around the pipes in BHEs and the surrounding ground around the borehole to be represented exactly. The transport of the fluid circulating along the pipe loop has been simulated explicitly in this model. The ground underneath the borehole has also been represented in this model. Validation of the 3D model has been carried out by reference to analytical models of borehole thermal resistance and fluid transport in pipes, as well as experimental data. In this work, the 3D numerical model has been applied to investigate the three-dimensional characteristics of heat transfer in and around a BHE at both short and long timescales. By implementing a two-dimensional (2D) model using the same numerical method and comparing the simulation results from the 3D and 2D models, the most significant three-dimensional effects have been identified and quantified. The findings have highlighted some of the limitations of 2D models, and based on the findings, methods to improve the accuracy of a 2D model have been suggested and validated. Furthermore, the 3D and 2D finite volume models have been applied to simulate an integrated GSHP system and their effects on overall system performance predictions have been investigated. The 3D numerical model has also been applied to examine thermal interactions within BHE arrays and to evaluate the assumptions of the line source model and their implications in the analysis of thermal response test data.

Geothermal Energy is one of the common talks at present. It has the potential to run long term and can provide base-load energy, at the same time it helps to reduce the greenhouse gas emissions. It is found almost everywhere on earth. The resources of geothermal energy range from shallow ground to hot water or hot rock, which can be found few kilometers below the surface and even deeper to magma where the temperature is extremely high. Since its discovery from the ancient times, many technologies have been developed to understand or use geothermal energy properly.  This report is based on literature survey of geothermal energy compared to other energy sources in terms of construction, supply energy and the advantage-disadvantage of the system. From this study it has been found that geothermal power plant does not need external fuel to operate, that’s why the price of geothermal energy does not go up like oil and gas, in USA the cost of geothermal electricity ranges from $0.06 to $0.10 per kilowatt-hour and besides it is one of the most clean, reliable and renewable energy source, which is environment friendly and cheaper than other energy sources.

Since the ancient years, human beings were using holes and caves to protect themselves from weather conditions making it the first known form of exploiting ground’s heat, known as Geothermal Energy. Nowadays, geothermal energy is mainly used for electricity production, space heating and cooling, Ground Coupled Heat Pump (GCHP) applications, and many other purposes depending on the morphology of the ground and its temperature. This study presents results of investigations into the evaluation of the thermal properties of the ground in Cyprus. The main objectives were i) to determine the thermal characteristics of the ground in Cyprus, ii) investigate how they affect the sizing and positioning of Ground Heat Exchangers (GHE) and iii) present the results for various ground depths, including a temperature map of the island, as a guide for engineers and specifiers of GCHPs. It was concluded that there is a potential for the efficient exploitation of the thermal properties of the ground in Cyprus for geothermal applications leading to significant savings in power and money as well. Six new boreholes were drilled and two existing ones were used for the investigation and determination of i) the temperature of the ground at various depths, ii) its thermal conductivity, iii) its specific heat and iv) its density. The thermal conductivity was determined by carrying out experiments using the line source method and was found to vary in the range between 1.35 and 2.1 W/mK. It was also observed that the thermal conductivity is strongly affected by the degree of saturation of the ground. The temperature of the undisturbed ground in the 8 borehole locations was recorded monthly for a period of 1 year. The investigations showed that the surface zone reaches a depth of 0.25 m and the shallow zone 7 to 8 m. The undisturbed ground temperature in the deep zone was measured to be in the range of 18.3 °C to 23.6 °C and is strongly dependent on the soil type. Since the ground temperature is a vital parameter in ground thermal applications, the temperature of the ground in locations that no information is available was predicted using Artificial Neural Networks and the temperature map of the island at depths of 20 m, 50 m and 100 m was generated. Data obtained at the location of each borehole were used for the training of the network. Data for the sizing of GHEs based on the ground properties of Cyprus were presented in an easily accessible form so that they can be used as a guide for preliminary system sizing calculations. With the aid of Computational Fluid Dynamics (CFD) software the capacity of the GHEs in each location and the optimum distance between them was estimated. Additionally, the long term temperature variation of the ground was investigated. For the first time since a limited study in the 1970’s, a research focusing on the determination and presentation of the thermal properties of the ground in Cyprus has been carried out. Additionally, the use of Artificial Neural Networks (ANNs) is an innovative approach for the prediction of data at locations where no information is available. The publication of this information not only contributes to knowledge locally but also internationally as it enables comparison with other countries with similar climatic conditions to be carried out.

An assessment of the thermal resource in the Straits of Florida was performed to estimate the Ocean Thermal Energy Conversion (OTEC) potential. Direct measurements of the temperature profile across the Florida Straits were taken from nearshore Southeast Florida to the Exclusive Economic Zone boundary along four evenly spaced transects perpendicular to Florida's Southeast coast, spanning 160 km. Along the southern transects in summer, nearshore cold and warm water resources meet or exceed the average 20ÀC temperature difference required for OTEC. In winter, the nearshore average DT of 17.76ÀC can produce 59-75% design net power and 70-86% in spring with DT averaging 18.25ÀC. Offshore along the southern transects, a high steady DT from 18.5- 24ÀC creates an annual average net power of 120-125MW. Along the northern transects, the nearshore resource does not exist, but a consistent OTEC resource is present offshore, providing 70-80% design net power in winter, and 100-158% in spring and summer. / by Anna E. Leland. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.