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2 GRC Transactions, Vol. 35, 2011 Multi-Criteria Suitability Modelling for Geothermal Exploration Wells Siting A Case Study of the Silali Geothermal Prospect, North Rift Kenya L. W. Wamalwa Geothermal Development Company Ltd., Kenya wwafula@gdc.co.ke Keywords Geothermal exploration, multi-criteria suitability modeling, weighted linear combination, Boolean integration model, environmental suitability analysis ABSTRACT Models are used in many different ways, from simulations of how the world works, to evaluations of planning scenarios, to the creation of indicators of suitability or vulnerability. In geothermal exploration, GIS based multi criteria suitability modelling was applied in the Silali geothermal prospect and it involved assigning weights to locations relative to each other based on given geoscientific criteria to find favourable locations for new geothermal exploration wells in the prospect. Geothermal well site selection is a spatial problem that involves the evaluation of data samples collected over a prospect area by different geo-scientific teams such as geophysicist, geochemists, geologists, reservoir engineers, environmentalists and geo-information team using varied spatial sampling methods and techniques. This multi-disciplinary approach of geothermal exploration therefore necessitates multi criteria analysis. Weighted linear combination (WLC) model and Boolean integration modes were utilized towards eventual suitability modelling in GIS. This approach has produced priority maps identifying favourable areas for drilling geothermal exploration wells. The results of the weighted linear combination analysis and the Boolean integration model were combined with those of an environmental suitability analysis for final selection of well sites. 1. Introduction 1.1 Background A geothermal exploration program is usually carried out on a step-by-step basis, passing from regional to sub-regional surveys before proceeding to more detailed studies (Goldstein, 1988; Wright et al., 1989). During each stage of the process, the least interesting areas are gradually eliminated so that efforts are concentrated on the remaining, more promising ones (Dickson and Fanelli, 2004). The management of a geothermal exploration program consists of integrating and interpreting the results of geological, geochemical, geophysical and other surveys so as to identify the location and extent of the areas recommended for further study or exploratory drilling. Identifying such areas of high geothermal potential can prove a daunting task, but the entire procedure can be made less cumbersome if it is done in a stepwise manner. Identifying well sites on the basis of the integrated results of different surveys and studies is a complex process involving decision-making, and is therefore subject to human error. Geographical Information Systems (GIS) (Bonham-Carter, 1994) can help to minimize these errors by identifying the drilling sites through a combination of various digital thematic maps. The GIS tool is a computerized approach to geothermal exploration, capable of integrating and interpreting geo scientific and environmental data and indicating the most appropriate sites for further investigation, including exploratory drilling, and where the impact on the environment will be kept to a minimum. In this study the results of three main models were utilized, two based on exploration data and the other on an environmental suitability analysis. In the first model, the geological, geochemical, geophysical and other data collected in the Silali geothermal prospect were integrated and an appropriate area was defined. In the environmental suitability analysis the GIS data layers were constructed from information on the topography, vegetation cover and density, land use, and surface water drainage relative to this specific area. By superimposing environmental thematic maps of specified criteria the most appropriate area on the basis of environmental parameters was selected. The results of the exploration and environmental suitability integration models were then overlain so as to define the most appropriate areas for siting geothermal wells. The GIS ArcMap 9.3 was utilized as a decision-support system tool in the site selection process. 1.2 Location and Description of Study Area Silali is the largest and most spectacular volcanoes of the Kenya rift. It is situated on the border of the Baringo and Turkana districts, 50km north of Lake Baringo bounded by approximately and N Latitude, and E Longitude. 1053

3 The caldera is a broad, low angle shield, with basal diameter 30 km x 25 km slightly elongate in a N-S direction. The volcanic shield covers an area of about 900 km 2 and rises to 760 m above the rift floor. The summit of the volcano is at 1528 masl and is occupied by a caldera that measures 7.5 km by 5 km and is bounded by 300 m cliffs. 1.3 Geothermal Energy Exploration Geothermal well site selection is a spatial problem that involves the evaluation of data samples collected over a prospect area by different geo-scientific teams such as geophysicist, geochemists, geologists, reservoir engineers, environmentalists and geo-information team using varied spatial sampling methods and techniques. This multi-disciplinary approach of geothermal exploration therefore necessitates multi criteria analysis and ordered Index overlay or weighted linear combination (WLC) of trend surface results and criterion maps towards eventual suitability modeling in GIS. 2. GIS Multi-Criteria Evaluation 2.1 Thematic Evidence Layers In order to categorize areas most likely to host geothermal resources and sites for exploration wells, different exploration datasets (i.e. geology, geochemistry, surface heat loss and geophysical data layers) are analyzed and combined using GIS. In carrying out the suitability modeling for Silali geothermal prospect, five data sets were used in the multi criteria suitability evaluation. 2.2 Geological Data Layers This included the spatial distribution of eruption centers, faults and fractures. Geologically they have always been considered of great importance in geothermal systems (Hanano, 2000) because they generally have higher permeability. 2.3 Geophysics Data Layers The Geophysical sampling techniques employed involved the use of transient electromagnetic (TEM) and magnetotellurics (MT) equipments. The TEM and MT measurements were employed to image the subsurface for the existence of electrically conductive zones that could be geothermal reservoirs. By processing the spatial sample data, electrical resistivity maps at different depths are obtained. Areas with a resistivity below 10Ωm were selected as potential sites for geothermal exploration (Uchida et al., 2004). The trend surface plots and maps for subsurface resistivity were done using various programs such as WinGlink and Surfer. 2.4 Geochemistry Data Layers This was done by spatial sampling methods over the prospect area. This involved sampling of fumaroles discharge, borehole water and soil gas sampling to determine the concentrations of carbon dioxide and radon radioactivity. From analysis and geo-statistical trend surface maps done in ArcMap for the above geochemical samples information on geo-thermometry, nature of the reservoir and permeability were evaluated. Figure 1. Study area. 2.5 Surface Heat Loss Measurements Surface heat loss sample measurements are used for estimating the amount of heat lost from the earth s surface through conduction or convection. Spatial temperature measurements are made on the area with geothermal potential at the surface. Spatial sample measurements at 0.5m and 1m depths are taken to determine the temperature gradient. The temperature gradient is then computed in the Fourier equation to determine the amount of heat lost through conduction. Heat loss via convection is determined by measuring the flow rates and source temperature of fumaroles and hot springs. These two forms of heat losses are then summed up to obtain the amount of heat lost from the surface of the area with geothermal potential, and hence help in estimating the quantity of the thermal energy present. 1054

4 2.6 Environmental Factors The Silali geothermal prospect is 100% ASAL and placed in Agro-Ecological zones 5 and 6. The local community (Pokots) in the Silali geothermal prospect area is mainly pastoralist, however a few have settled down to farming. Some crop farming is being done in agro-pastoralist livelihood zones found in Churo, Tangulbei and Koloa division. The study area has a mean annual precipitation of mm and is mostly covered by evergreen bush (Dodonaea Croton Evergreeen), deciduous shrub land Chrysopon grassland but their abundance has been greatly reduced by human activities, particularly by overgrazing. In most places, Acacia species such as Acacia reficiens,a. totilis,and A.nubica are the most dominant species. Adenium obesum, Salvadora persica, Balanites orbicularisis, Terminali brownii,aloe vera and palms(brahea eduli) are the predominant shrub. Only about 20% of the land is used for agriculture, the main obstacle being the steepness of the terrain and the lack of water for irrigation. The population of the area according to 1999 national census was 101,000 persons in 15,888 households in the five divisions that are within the prospect namely Kolloa, Tangullbay, Churo,Nginyang and Mondi. were first combined and analyzed in order to define promising areas. The derived map layers (factor maps) from different categories were then weighted, overlaid, combined and analyzed until promising areas had been defined and classified. This levelby-level overlaying of layers continued until all the data layers had been used in the analyses. Figure 2 shows the flow chart for the Index Overlay method utilized in the geothermal favorability analysis. The function classes and functions used in this mode are as outlined in Table Integration of Data Layers Multi criteria evaluation is commonly achieved by one of the two procedures (Eastman, 2001). The first involves Boolean overlay whereby all criteria are reduced to logical statements of suitability and then combined by means of one or more logical operators such as intersection and union. The second is known as weighted linear combination (WLC) wherein continuous criteria (factors) are standardized to a common numeric range and then combined by means of a weighted average. The result is a continuous mapping of suitability that may then be masked by one or more Boolean constraints to accommodate qualitative criteria, and finally threshold to yield a final decision. According to Hopkins (1977) the most prevalent procedure for integrating multi-criteria evaluation and multi-objective evaluation (MOE) in GIS for suitability analysis is using a weighted linear combination approach. 3.1 Weighted Linear Combination Model The Weighted Linear Combination or Index Overlay is a GIS operation whereby the layers with a common area are joined on the basis of their occupation of space (Bonham-Carter, 1994; Clarke, 1999). The overlay function creates composite maps by combining diverse data sets. Each class of map is given a different score, allowing for a more flexible weighting system. Score tables and the map weights can be adjusted to reflect the judgment of an expert in the domain of the application under consideration. At any location, the output score, S, is defined as; S = Σ WiAi Σ Wi Where;Wi is the weight of the i th map, and Ai is the score in the i th map (Bonham-Carter, 1994). The subjective-based analyses were carried out using the Index Overlay model. The data layers in the same category or subject Figure 2. Weighted linear combination model flow diagram Boolean Integration Model Boolean modeling involves the logical combination of binary maps resulting from the application of conditional AND and OR operators. In practice, one cannot assign the same importance to each of the criteria being combined. Each item of evidence needs to be weighted according to its relative significance Union Operation (OR) The Union Tool in ArcInfo creates a new coverage by overlaying two or more polygon coverages. The output coverage contains the combined polygons and the attributes of both coverages. In using this method, those areas selected as suitable areas by any one of the evidence layers are combined to prevent the loss of any prospective area defined by just a single evidence layer inside of the data sets Intersection Operation (AND) The Intersect Tool in ArcInfo calculates the geometric intersection of any number of feature classes and data layers that are indicative of geothermal activity (geology, geochemistry, geophysics). Features that are common to all input data layers were selected using this method (Bonham-Carter, 1994). This implies that the selected area is suitable for the purpose of a study based on all input data layers. In this model these operations can be represented by the following simple equation; where the output source S is expressed as; S = RD [(FL CM) (AZ HS)] Where the and denote AND and OR operations, respectively, and RD, FL, CM, AZ and HS represents the resistivity,

5 faults, caldera ring, acidic hydrothermal alteration zones and hot Springs respectively. The evidence layers and functions used in the Boolean integration model are as shown in the table 3 below. When developing the Boolean logic model based on each evidence layer, the zones in the study area were assigned to one of two different probability classes. The zones with a geothermal resource were assigned a value of 1 and the remainder a value of 0. A new factor map was then generated by overlaying two or more input raster maps. Fig. 3 shows the conceptual model based on the Boolean method that was used to integrate the data from the study area. Figure 3. Boolean integration model flow diagram. 3.3 Environmental Suitability Modeling The environmental suitable areas for well sitting will be determined based on the following; Topology - To avoid or reduce possible surface disturbances, drill pads should be located in almost level (<5% slope) to very gently sloping (6 15% slope) terrain. Surface drainage - Geothermal surface installations, including wells, should not be constructed on terrain subject to flooding Residential areas - Geothermal wells should not be sited near residential and recreational areas or places of historical/ cultural interest. Local communities, in particular, are concerned about noise, possible modifications to the landscape, and to cultural and historical features, as well as changes in the use of public areas Land use - In environmental suitability analysis, we assign different values to the different land uses, with higher values given to areas of greater socio-economic importance. Similarly, agricultural land receives a higher value than pasture land Vegetation cover index - The vegetation cover in the study area was mapped, and the major plant communities defined. The different types of vegetation cover were ranked according to their importance for grazing and an index assigned to each plant from the most important to the least important. The environmental suitability modeling was done using two data sets i.e. the landsat image of the Silali prospect that enabled the classification of the various land uses including ranking of the vegetation cover from the most important to the less important. The residential areas were also identified from the classified image. This was achieved by carrying out a supervised classification of the image. The Digital elevation model of the area was used to determine areas with suitable gradients for drilling as well as areas with suitable drainage away from flood prone areas. The flow diagram for the conceptual model adopted in the environmental suitability analysis is presented in Figure 4. Table 1. Evidence layers and functions used in Weighted Linear Combination. Dataset Evidence Layer Type Evidence Criteria Selection Method Function to Delineate Eruption Centers Point < 500m Buffer area around the eruption center Heat source Geology Faults & Fractures Polyline < 200m Straight-line distance on both sides of the faults and fractures Permeable zone Fumaroles Point < 1000m Buffer area around the fumarole Fluid flow pathways and up flow zone Soil gas (CO 2 concentration) Polygon > 3.5% Kriging trend surface interpolation Permeability Geochemistry Soil gas (Radon gas concentration) Polygon > 2300ppm Kriging trend surface interpolation Permeability Rn/CO 2 Ration Polygon > 4000 Kriging trend surface interpolation Permeability Geophysical Electrical Restivity Polygon < 30 Ωm Kriging trend surface interpolation Reservoir Heat Loss Soil Temperature Polygon > 50 c Kriging trend surface interpolation Heat loss Table 2. Evidence layers and functions used in Boolean integration model. Dataset Evidence Layer Type Evidence Criteria Selection Method Function to Delineate Geology Faults lines Polyline < 200m Straight-line distance on both sides of the faults line Permeable zone Caldera ring Polygon < 500m Buffer area around the caldera ring Fluid flow pathways and up flow zone Geochemistry Alteration zone Polygon <1000m Buffer area extending from the edges of the polygon Permeability Hot springs Point <2000m Buffer area around the hot spring Heat source Geophysical Electrical Restivity Polygon < 10 Ωm Natural neighbor interpolation Reservoir 1056

6 Figure 4. Environmental suitability modeling flow diagram. Table 3. Weighted linear combination factor classes and there weights. Geological Evidence Layer Geochemical Evidence Layer Geophysical Evidence Layer Heat Loss Evidence Layer Factor class (Distance in m) Weight Factor class Factor class Factor class Weight Weight (Rn/CO 2 ) (Resistivity,Ωm) (Temp C) Weight <200 9 > 21 9 < 10 9 > >600 1 <10 1 >60 1 > Results and Discussion 4.1 Weighted Linear Combination Model The geological, geochemistry, geophysics and heat loss data sets were utilized in the models using the criteria outlined above and the trend surfaces generated from the different datasets reclassified and assigned a uniform scale of 1-9.The most suitable areas were assigned 9 and the least suitable areas assigned the value 1 The class factors utilized in the weighted linear combination model and there weights are shown in Table 3. The suitable areas as delineated by the weighted linear combination model are shown in the Figure Boolean Integration Model The Boolean integration model was run by generating buffers around the evidence layers as indicated in table 2. A union was done between the results of the caldera wall buffer and the distance from faults buffer to delineate area with high permeability. The results of the distance from hot springs buffer and the distance from hot altered ground buffer were also combined by doing a union to delineate the areas with a heat source. The areas with high permeability and heat source were then combined by an intersection to delineate areas with both high permeability and a heat source. This was finally combined with areas with low resistivity to generate the suitable areas for exploration drilling based on the Boolean integration model as shown in Figure Environmental Suitable Area Land cover land use supervised classification of a landsat image of the study area was done with residential areas as one of the classes. The study area was sparsely populated due to the pastrolistic nature of the residence in the study area. A digital elevation model of the area was also used to determine the best slopes for exploration drilling. Areas with heights between 1,100-1, meters above sea level had the steepest slopes and were eliminated as suitable areas for siting wells. Areas with heights between 900-1,100 meters above sea level had slopes with gradients less than 15% and were delineated as the suitable areas for exploration well sitting. The results of the classification indicated that there were no settlements inside the caldera and its environs instead the area was covered by pasture and shrubs as indicated in Figure 7. Suitable areas for well siting according to the environmental data were areas where the drilling will not affect the pastures and these are areas with shrubs, bare rock and young lava. Figure 5. Weighted linear combination suitable areas map. 1057

7 Wamalwa Table 4. Proposed geothermal exploration wells. ID Eastings Northings ' 14.28" 1 08' 36.86" ' 52.73" 1 09' 12.19" ' 13.37" 1 09' 30.23" Elevation Conclusion and Recommendations 5.1 Conclusion Spatial associations between geothermal exploration and environmental evidence layers were analyzed using GIS as a decision-making tool to determine the appropriate sites for exploratory wells in the Silali geothermal prospect in the north rift Kenya. Digital data layers and maps were used in a GIS environment to select these sites. In exploration data modeling, two data analysis and integration models were used: the Boolean integration model and the weighted linear combination models. The same input data layers were utilized in both models. In every step in the analysis, the area selected by the weighted linear combination model was consistent to the ones selected by the Boolean model. Two different data layers were used for the environmental suitability analyses. By superimposing these specific criteria layers, suitable drill sites on the basis of environmental factors were derived. A zone covering 39% of the study area was defined as being suitable for siting exploratory wells based on an environmental suitability analysis. After considering the geological factors 9% of the study area was suitable for well siting. Three well sites were selected in the south east side of the caldera ring in the Silali geothermal field. Figure 6. Boolean integration model suitable areas map. Figure 7. Environmental suitable areas. 5.2 Recommendations 4.4 Environmentally Suitable Area Recommendations as per the results of this research include; The results of the three models were then combined to give the most suitable areas for geothermal exploration wells as indicated in Figure 8. The three suitable layers were then intersected in ArcGIS spatial analyst to give the areas where all the factors are evident. The area was reduced to the area inside the caldera ring on the south eastern side. Three geothermal exploration wells i.e. 101,102 and 103 were proposed for drilling as shown in the Figure 9. The coordinates of the exploration wells proposed and there elevations are as shown in table 4 in both UTM Arc 1960 datum and in geographic coordinates systems. Integration of more geo-scientific data into the specific models e.g. the integration of seismic data that enables the interpretation of the geothermal system of an area up to 25 kilometers below the surface. Integration of more environmental data e.g. the social economic data in the research to narrow down to geothermal exploration well sites in the future. That more infill geo-scientific data be obtained for further analysis in the models and especially on the eastern side of the caldera where this research proves there is the resource. 1058

8 References 1. Bonham-Carter, G.F., (1994), Geographical Information Systems for Geoscientists: Modeling with GIS. Computer Methods in the Geosciences, vol. 13. Pergamon, New York, NY, USA, 398 pp. 2. Clarke, K.C., (1999), Getting StartedWith Geographic Information Systems. second ed. Prentice Hall, Upper Saddle River, NJ, USA, 338 pp. 3. Eastman, R. J., (2001), Guide to GIS and Image processing, Vol.2. Clark University, USA Eastman, R. J., (2001), Idrisi 32, Release 2. Tutorial. Clark University, USA Eastman, J. R., and Jiang, H., (1996), Fuzzy measures in multi criteria evaluation. proceedings, 2nd. International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Studies, May 21-23, Fort Collins, Colorado, Figure 8. Integration of results map. 6. ENEL, (1983), Geothermal Power Development Studies in Iran. General Report on Sabalan Zone. Ente Nazionale perl Energia Elettrica (Italy) report to the Ministry of Energy, Islamic Republic of Iran, Tehran, 220 pp. 7. ESRI, (2004), ArcGIS 9.0, Using ArcGIS-3D Analyst. Environmental Systems Research Institute, Redlands, CA, USA,382 pp. 8. ESRI, (2005). Using ArcMap 9.1. Environmental Systems Research Institute. Redlands, CA, USA, 598 pp. 9. Goldstein, N.E., (1988), Sub regional and detailed exploration for geothermal hydrothermal resources. Geothermal Science Technology 1, Hanano, M., (2000), Two different roles of fractures in geothermal development. In: Proceedings of the World Geothermal Congress, Kyushu Tohoku, Japan, 28 May 10 June, pp Hopkins, L. D., (1977), Methods for generating land suitability maps: a comparative evaluation. J. Am. Inst. Plan., 43(4), Figure 9. Proposed exploration wells. 6. Acknowledgement My sincere gratitude goes to the Geothermal Development Company Limited management for the permission to use the exploration data for Silali. Scientists from the resource development company for the technical input and Members of the GIS section-gdc for their contribution and support. 12. Uchida, T., Song, Y., etal., (2004), 3D magnetotelluric interpretation in Pohang low-enthalpy geothermal area, Korea. In: Proceedings of the 17th IAGAWG1.2Workshop on Electromagnetic Induction in the Earth, Hyderabad, India, October 18 23, pp. 1/6 6/ KenGen, Menengai volcano: Investigations for its geothermal potential. Unpublished report of Geothermal Resource Assessment project 14. Peter Omenda, k. Opondo, etal (2000), Ranking of Geotherma prospects in the Kenyan Rift pp Yousefi. H, Ehara. S, and Noorollahi. Y., (2007), Geothermal Potential Site Selection using GIS in Iran, 32nd workshop on Geothermal reservoir engineering, January 22-24, 2007, Stanford, CA, USA, Access site September

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