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Gravity is measured and used by Lantmäteriet to calculate a model of the geoid to get accurate reference heights for positioning. Lantmäteriet are continuously measuring new gravity and height data across Sweden to both complement, replace and to add new data points. This is mainly done by measurements in the field at benchmark points. One of the major reasons for continued measurements on e.g. benchmark points is that the measuring always moves forward which makes the measurements more accurate. More accurate data leads to a more accurate calculation of the geoid due to the more accurate gravity values. A more accurate geoid gives the possibility of more precise positioning across Sweden, due to the more precise height values. Lantmäteriet is in the process of updating their entire database of gravity data. They are also measuring at locations where there are none or sparse with measurements. As a stage in the renewing of their database and other systems the Geodesy department wishes to get an introduction to the ArcGIS environment. By customizations of several ArcGIS functions, Lantmäteriet’s work with the extensive data will get easier and perhaps faster. Customized tools will help make e. g. adding and removing data points easier, as well as making cross validation and several other functions only a click of a button away.

Reservoir Simulation is a very powerful tool used in the Oil and Gas industry to perform and provide various functions including but not limited to predicting reservoir performance, conduct sensitivity analysis to quantify uncertainty, production optimization and overall reservoir management. Compared to explored reservoirs in the past, current day reservoirs are more complex in extent and structure. As a result, reservoir simulators and algorithms used to represent dynamic systems of flow in porous media have invariably got just as complex. In order to provide the best solutions for analyzing reservoir performance, there is a need to continuously develop reservoir simulators and reservoir simulation algorithms that best represent the performance of the reservoir without compromising efficiency and accuracy. There exists several commercial reservoir simulation packages in the market that have been proven to be extremely resourceful with functionality that covers a wide range of interests in reservoir simulation yet there is the constant need to provide better and more efficient methods and algorithms to study and manage our reservoirs. This thesis aims at bridging the gap in the framework for developing these algorithms. To this end, this project has both an educational and research component. Educational because it leads to a strong understanding of the topic of reservoir simulation for students which can be daunting especially for those who require a more direct experience to fully comprehend the subject matter. It is research focused because it will serve as the foundation for developing a framework for integrating custom built external simulators and algorithms with the workflow of the model builder of our reservoir simulation package of choice i.e. Petrel with the Ocean programming environment in a seamless manner for simulating large scale multi-physics problems of flow in highly heterogeneous flow of porous media. Of particular interest are the areas of model order reduction and production optimization. In-house algorithms are being developed for these areas of interest and with the completion of this project. We hope to have developed a framework whereby we can take our algorithms specifically developed for areas of interest and add them to the workflow of the Petrel Model Builder. Currently, we have taken one of our in-house simulators i.e. a two dimensional, oil-water five-spot water flood pattern as a starting point and have been able to integrate it successfully into the “Define Simulation Case” process of Petrel as an additional choice for simulation by an end user. In the future, we will expand this simulator with updates to improve its performance, efficiency and extend its capabilities to incorporate areas of research interest.

Myndigheten Sveriges Geologiska Undersökning (SGU) har ett nationellt ansvar för att övervaka Sveriges grundvattennivåer. Eftersom det inte är möjligt att få ett heltäckande mätstationssystem måste grundvattennivån beräknas på vissa platser. Därför är det intressant att undersöka sambandet mellan grundvattennivån och utvald geografisk information, så kallade miljövariabler. På sikt kan maskininlärning komma att användas inom SGU för att beräkna grundvattennivån och då kan en förstudie vara till stor hjälp. Examensarbetets syfte är att genomföra en sådan förstudie genom att undersöka vilka miljövariabler som har störst betydelse för grundvattennivån och kartlägga modellosäkerheter vid grundvattenprediktering. Förstudien genomförs på sju områden inom SGUs grundvattennät där mätstationerna finns i grupper likt kluster. I förstudien används övervakad maskininlärning som i detta examensarbete innebär att medianvärden på grundvattennivån och miljövariablerna används för att träna modellerna. Med hjälp av statistisk data från modellerna kan prestandan utvärderas och justeringar göras. Algoritmen som används heter Random Forest som skapar ett klassifikations- och regressionsträd, vilket lär modellen att utifrån given indata fatta beslut som liknar männiksans beslutfattande. Modellerna ställs upp i ArcGIS Pros verktyg Forest-based Classification and Regression. På grund av områdenas geografiska spridning sätts flera separata modeller upp. Resultatet visar att det är möjligt att prediktera grundvattennivån men betydelsen av de olika miljövariablerna varierar mellan de sju undersökta områdena. Orsaken till detta lär vara geografiska skillnader. Oftast har den absoluta höjden och markens lutningsriktning mycket stor betydelse. Höjd- och avståndsskillnad till låg och hög genomsläpplig jord har större betydelse än vad höjd- och avståndsskillnad har till medelhög genomsläpplig jord. Höjd- och avståndsskillnad har större betydelse till större vattendrag än till mindre vattendrag. Modellernas r2-värde är något låga men inom rimliga gränser för att vara hydrologiska modeller. Standardfelen är oftast inom rimliga gränser. Osäkerheten har visats genom ett     90 %-igt konfidensintervall. Osäkerheterna ökar med ökat avstånd till mätstationerna och är som högst vid hög altitud. Orsaken lär vara för få ingående observationer och för få observationer på hög höjd. Nära mätstationer, bebyggelse och i dalgångar är osäkerheterna i de flesta fallen inom rimliga gränser. / The Swedish authority Geological Survey of Sweden (SGU) has a national responsibility to oversee the groundwater levels. A national network of measurement stations has been established to facilitate this. The density of measurement stations varies considerably. Since it will never be feasible to cover the entire country with measurement stations, the groundwater levels need to be computed in areas that are not in the near vicinity of a measurement station. For that reason, it is of interest to investigate the correlation between the groundwater levels and selected geographical information, so called environmental variables. In the future, SGU may use machine learning to compute the groundwater levels. The focus of this master's thesis is to study the importance of the environmental variables and model uncertainties in order to determine if this is a feasible option for implementation on a national basis. The study uses data from seven areas of the Groundwater network of SGU, where the measuring stations are in clusters. The pilot study uses a supervised machine learning method which in this case means that the median groundwater levels and the environmental variables train the models. By evaluating the model's statistical data output the performance can gradually be improved. The algorithm used is called “Random Forest” and uses a classification and regression tree to learn how to make decisions throughout a network of nodes, branches and leaves due to the input data. The models are set up by the prediction tool “Forest-based Classification and Regression” in ArcGIS Pro. Because the areas are geographically spread out, eight unique models are set up. The results show that it’s possible to predict groundwater levels by using this method but that the importance of the environmental variables varies between the different areas used in this study. The cause of this may be due to geographical and topographical differences. Most often, the absolute level over mean sea level and slope direction are the most important variables. Planar and height distance differences to low and high permeable soils have medium high importance while the distance differences to medium high permeable soils have lower importance. Planar and height distance differences are more important to lakes and large watercourses than to small watercourses and ditches.  The model’s r2-values are slightly low in theory but within reasonable limits to be a hydrological model. The Standard Errors Estimate (SSE) are also in most cases within reasonable limits. The uncertainty is displayed by a 90 % confidence interval. The uncertainties increase with increased distance to measuring stations and become greatest at high altitude. The cause of this may be due to having too few observations, especially in areas with high altitude. The uncertainties are smaller close to the stations and in valleys. / SGUs grundvattennät

Rising world petroleum prices and global warming are contributing to interest in renewable energy sources, including energy produced from agricultural crops and waste sources of biomass. A network of small mobile pyrolysis units may be the most cost effective system to convert biomass from agricultural feedstocks to bio-crude oil. Mobile pyrolysis units could be moved to the feedstock production fields thereby greatly simplifying feedstock logistics. In the North Central (NC) region of the U.S., possible feedstocks are corn stover, energy sorghum, and switchgrass. A grid-based Geographic Information System (GIS) program was developed to identify optimum locations for mobile pyrolysis units based on feedstock availability in the NC region. Model builder was used to automate the GIS analysis. Network analysis was used to find the best route to move the mobile pyrolysis units to new locations and to identify the closest refinery to transport the bio-crude oil. To produce bioenergy from feedstocks, the removal of biomass from agricultural fields will impact the hydrology and sediment transport in rural watersheds. Therefore, the hydrologic effects of removing corn stover from corn production fields in Illinois (IL) were evaluated using the Soil Water Assessment Tool (SWAT). The SWAT model was calibrated and validated for streamflow and sediment yields in the Spoon River basin in IL using observed data from the USGS. The modeling results indicated that as residue removal rates increased, evapotranspiration (ET) and sediment yields increased, while streamflows decreased. Biochar is a carbon-based byproduct of pyrolysis. To ensure that the mobile pyrolysis system is economically and environmental sustainable, the biochar must be land applied to the feedstock production fields as a soil amendment. An assessment of hydrologic changes due to the land application of biochar was made using the SWAT model in the Spoon River basin and changes in soil properties due to incorporation of biochar into the soil obtained from laboratory experiments by Cook et al. (2012). Model simulations indicated that a biochar application rate of 128 Mg/ha decreased water yield, and sediment yield in surface runoff and increased soil moisture and ET.

Bushings are a necessary component of the transformers in the power grid. A bushing is used to control the electric field's strength and shape. It is also an insulator for high-voltage conductors. The bushing enables a conductor to be safely brought through a grounded barrier. In this report, several methods for creating a 2D axi-symmetrical bushing core geometry in COMSOL Multiphysics were developed. The geometry includes the conductor, hollow area inside the conductor, the RIP, the mold and aluminum foils. First, the base-geometry was constructed, which includes all geometry parts except the foils. Afterward, two different approaches were used to construct the foils. The first approach was to automatically build a requested number of foils. The second approach was to create the foils based on data from excel-sheets. The developed method should be able to create both full foils and partial foils. A total of four foil methods were developed. The first method used COMSOL's Model Builder to create a requested number of foils uniformly distributed within the base-geometry. The second method used COMSOL's Application Builder to create a requested number of foils based on mathematical expressions. The third method reads data from an excel sheet to create the foils in COMSOL. Method four is an improved version of method three that can create partial foils as well as the base-geometry. Foil methods II, III, and IV, created every foil as a separate geometrical object. As a result, an associated method that deletes the foils were also developed for each of these methods. A conclusion that the fourth method was the most realistic method of creating a bushing core could be draw due to, among other factors, it is the only method that can build partial foils.