Multi-criteria decision support for geothermal resources exploration based on remote sensing, GIS and geophysical techniques along the Gulf of Suez coastal area, Egypt
Introduction
Geothermal energy is one of the important energy resources that can contribute electricity production in an efficient way (Morgan et al., 1983), and support Egypt's energy demands in a clean way (Boulos and Wood, 1990). The geothermal energy represents the urgent renewable energy expanding electricity generation capacity and covering energy requirements for development plans (Hosney, 2000). Impacts of geothermal resources on development actions attract concern for researchers and local governments (Swanberg, 1983; Abdel Zaher et al., 2011a; Lashin, 2013, 2015; Mohamed et al., 2015; Chandrasekharam et al., 2016; Atef et al., 2016; Abdel Zaher et al., 2018a, 2018b). A large amount of quantitative data and processing techniques are required to evaluate locations of geothermal resources and their socio-economic implications (Astolfi et al., 2011; Abdel Zaher et al., 2014; Atef et al., 2016). Worldwide studies considered the spatial assessment of geothermal potential to expand management policy (Coolbaugh et al., 2005; Noorollahi et al., 2007, 2008; Yousefi et al., 2010). Since the last decades, the policy-makers focus on increasing energy resources to support its economic development (Yousefi et al., 2010). The geothermal energy resources in Egypt can be controlled by structural and tectonic system or by depositional system of the different stratigraphic units (Morgan et al., 1983). Several hot-springs are in the Red Sea and its Gulfs rift which represent structurally controlled source (Abdel Zaher et al., 2011b). In addition, many hot-springs exist in the Western desert which represent depositional controlled source (Abdel Zaher et al., 2018a). Several new data sources indicated that temperatures more than 150 °C, may be in the coastal zone reservoir along Red Sea and Gulf of Suez at a depth of 4 km and deeper (Abdel Zaher et al., 2014; Atef et al., 2016). Temperatures of Red Sea floor increase sharply toward central axis generating regional heat flow anomaly along the Red Sea and Gulf of Suez margins. The high heat flow anomaly indicates the high potential for geothermal energy along the Red Sea and Gulf of Suez coastal zones (Boulos and Wood, 1990; Lashin, 2007; Lashin and Al Arifi, 2010; Abdel Zaher et al., 2018a). Hence, the spatial prediction of geothermal potential is particularly significant due to the socio-economic impacts and for management of sustainability along the Gulf of Suez coastal area (GOSCA).
Remote sensing (RS) techniques were extremely used for the evaluation of geothermal resources (Coolbaugh et al., 2005; Noorollahi et al., 2007, 2008; Calvin et al., 2015; Nishar et al., 2016). Digital image processing was utilized to extract drainage pattern, to estimate surface heat losses, and to identify zones fitting the criteria of hot-spring sites using the analysis of thermal bands (Donegan and Flynn, 2004; Haselwimmer et al., 2011; Kaiser and Ahmed, 2013). Enhanced Thematic Mapper (ETM + 7), Operational Land Imager (OLI-8) and TIRS Landsat satellite images were selected recently to estimate land surface temperature (Elfinurfadri et al., 2017; Cristóbal et al., 2018; Hang and Rahman, 2018; El Bouazouli et al., 2019). The Landsat satellite images were coupled with magneto-telluric (MT) measurements to assess a surface indicator of geothermal energy (Yuliang and Yongming, 2008). The integrated RS and GIS approach has been adopted in several researches as the most influential technology for the geospatial assessment and handling of geothermal resources (Coolbaugh et al., 2007; Kratt et al., 2009; Sadeghi and Khalajmasoumi, 2015). However, few researchers around the world supports spatial prediction of geothermal favorability combining datasets such as satellite image processing, and geophysical methods such as well logs, self-potential, geo-electrical, gravity, magnetic, and thermal methods (Prol-Ledesma, 2000; Carranza et al., 2008; Abdel Zaher et al., 2014; Hinz et al., 2015; Abdel Zaher et al., 2018a; Shirani et al., 2019). None of the studies has been applied an integrated methodology based on well logs, seismic profiles, gravity and magnetic data, earthquake activities, and satellite images to develop a conceptual model that delineates geothermal potential sites through the Bayesian statistical analysis.
Applications of geophysical methods are commonly used for the exploration of geothermal resources (Hersir and Björnsson, 1991; Lund, 2003; Abdel Zaher et al., 2014; Shirani et al., 2019). The geophysical methods are applied widely to support information about temperature and fluid content of the rocks, aquifer locations, structural setting controlling aquifers, and general properties of the geothermal system (Keary and Brooks, 1992; Abdel Zaher et al., 2011a; Hsieh et al., 2014; Kana et al., 2015). In this study, several direct and indirect geophysical methods are selected to explore the geothermal reservoirs. The direct methods are considered the most successful and include well logs, and thermal methods (Hersir and Björnsson, 1991; Lund, 2003; Abdel-Fattah et al., 2020). The indirect methods comprise magnetic proper ties, density and seismic reflection which are usually used to explore the structural setting and physical characteristics of the host rock (Bodvarsson, 1970; Georgsson, 2009; Abdel-Fattah et al., 2015, 2018). Seismic reflection tool is selected in this study as a powerful and essential pre-requisite to assess the possibility of geothermal sites and to reduce the risk associated with expensive drilling wells. None of previously studies has been used seismic geothermal exploration.
GIS is a powerful technique to incorporate multi-datasets and evaluate any wealthy environment exposing to natural resources (Ondreka et al., 2007; Noorollahi et al., 2015; Abuzied and Alrefaee, 2017; Abdel Zaher et al., 2018b). Several studies have been achieved by different methods to assess geothermal energy using GIS (Coolbaugh et al., 2007; Kratt et al., 2009; Abdel Zaher et al., 2011a; Noorollahi et al., 2015). These studies vary from rule-based systems to empirical process-based systems (Coolbaugh and Shevenell, 2004; Moghaddam et al., 2013; Sadeghi and Khalajmasoumi, 2015; Yalcin and Gul, 2017). Several GIS models have been applied for evaluating geothermal potentiality in different areas around the world (Coolbaugh and Shevenell, 2004; Moghaddam et al., 2013; Moghaddam et al., 2014; Sadeghi and Khalajmasoumi, 2015; Abdel Zaher et al., 2018b). Hence, the GIS has been used in this study to integrate multi-datasets and delineate geothermal potential zones. The bivariate statistical analysis is one of GIS statistical models where the distribution analysis defines potential zones from different data sources, and hence suggests information on the relation between natural resources and their causative factors (Abuzied, 2016; Abuzied and Pradhan, 2020). The bivariate statistical analysis includes several methods such as information value (IV) method (Van Westen et al., 1997), logistic regression (Saha et al., 2005), weights-of-evidence (WoE) analysis (Jebur et al., 2014) and frequency ratio (FR) method (Abuzied and Alrefaee, 2018). The bivariate statistical analysis is the logical analytical model where the defined potential zones contribute to adopt the numerical weights and ranks (Abuzied and Alrefaee, 2018). Therefore, the IV and WoE statistical analyses were selected in this study to respectively assign ranks for factor contributed geothermal potential and weights for their classes.
Section snippets
Study area characteristics
The Gulf of Suez coastal area (GOSCA) lies between the western corner of Sinai Peninsula and the northeastern side of Nile River (Fig. 1). Gulf of Suez represents the northern branch of the great East African Rift System. The study area extends between latitudes 27° 50´ 25˝ to 29° 54´ 55˝ N and longitudes 32° 0´0˝ to 33° 50´ 54˝ E with a total area 51,200 km2 (Fig. 1). The topography of the study area varies from gentle terrains to steep mountains, sloping gradually towards coastal zones. The
Data and methods
In order to define favorable sites of geothermal energy resources, a holistic integrated approach was taken considering geological, geomorphological, and hydrological characteristics of the GOSCA (Fig. 3). The modeling workflow was explained by the flow chart (Fig. 3) which displays the several datasets driving from the following sources:
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Different geological maps of Egypt (Conoco, 1987; EGSMA, 1994) were selected to determine different lithological units (Fig. 2) and define the training classes
Results
The holistic integrated approach applied in this study, adds the commonsensical model expecting the new geothermal sites in the Gulf of Suez coastal area. The geophysical analysis supports the development of the GPM to create geothermal potential map (Fig. 17). The spatial mapping of geothermal potential sites was accomplished by integrating the evidence layers using weight of evidence and information value statistical analysis. The geothermal potential map classified the study area into
Discussion
The tectonic evolution of Red Sea and its Gulfs contributes mainly in the construction of geothermal reservoirs in the study area. The spatial distribution of geothermal sites was evaluated through a time of several processes to predict carefully the availability of different sites for geothermal resource. Different GPM factors reveal a strong contribution in the possibility of geothermal resources along Gulf of Suez shoreline (Table 1).
Conclusion
The study suggests the holistic integrated approach to develop the commonsensical model expecting the new geothermal sites in the Gulf of Suez coastal area. The geothermal potential map was created by integrating several evidence layers based on WoE and IV statistical analysis. The geothermal potential map reveals the most promising sites for geothermal reservoir. The favorable sites are distributed in areas where the surficial outcropped lithologies are highly structured and permeable. The
Declaration of Competing Interest
None.
Acknowledgements
The authors greatly thank the Gulf of Suez Petroleum Company (GUPCO), the Egyptian General Petroleum Corporation (EGPC), the Belayim Petroleum Company (PETROBEL), and the British Petroleum Company (BPC) for providing BHT well data and seismic profiles along the study area. The authors want to express their appreciation to Prof. Christopher Bromley, Prof. Younes Noorollahi, and Dr. Mohamed Abdel Zaher for their fruitful recommendations and constructive criticism on an earlier draft of the
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