Exploration and optimized siting of geothermal wells using a web-based spatial decision support system
Daniel Waweru Mwaura · DepositOnce · 2018
As the world develops and economies grow, there is a commensurate rise in the demand for energy. Many current approaches of fulfilling the increased demand have led to increased pollution through emission of greenhouse gases. A lot of current research in the energy sector is therefore geared towards green and reliable energy sources. Geothermal energy production is one of the leading sources of green and reliable energy. There has been increased effort in research of finding ways of increasing the production of this energy form. The exploration and development of geothermal resources entails an intricate multidisciplinary decision making process involving diverse conflicting criteria. The nature of the analysis of data as currently carried out, leads to a protracted process before any benefits of the vast resource can be realized. Over the last decade, Geographical Information Systems (GIS) has been pivotal in data analysis and dissemination in an array of disciplines. Due to the nature of the geothermal resource exploration process, there have been numerous efforts aimed at incorporating GIS methods into the process, but the full potential is yet to be realized. This is mainly due to the steep learning curve of traditional GIS systems for many geothermal scientists. This study attempted to find a solution to this problem by evaluating the shortcomings of the traditional methods of geothermal exploration and the visualization methods employed during consequent development. It aimed at taking advantage of Decision Support Systems (DSS) in reducing the disconnect that exists between GIS and geothermal exploration by means of a Spatial Decision Support System (SDSS). The proposed solution aimed at integrating data from various sources in the geothermal scope in a Multi Criteria Decision Analysis (MCDA) model to produce an output that could assist geothermal scientists to locate suitable areas for priority detailed exploration. In most cases, the provision of suitability areas leads to a problem of the priority wells drilling sites. Improving on current methods, the study modeled the geothermal well location problem as a NP-hard problem that could be solved using the combinatorial optimization algorithms. In this aspect two popular algorithms; The Metropolis-Hasting algorithm and theGenetic algorithm were applied and their results were compared. The MCDA and optimization models were combined in a novelWeb-based SDSS for geothermal scientists. The solution was then tested for the Olkaria geothermal field, an active geothermal area in Kenya, East Africa with results that equaled or bettered the highest producing wells in the field. The study therefore revealed that SDSS can provide a solution that improves the time taken from geothermal exploration to development accelerating the pace of provision of alternative energy sources from geothermal resources.