Geothermal chance for success study in Upper Permian and Triassic carbonate reservoirs

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1 ROCEEDINGS, Thirty-Ninth Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, February 24-26, 2014 SG-TR-202 Geothermal chance for success study in Upper ermian and Triassic carbonate reservoirs Edina Eötvös Loránd University, 1/C ázmány éter sétány, Budapest, 1117, Hungary Keywords: geothermal chance for success, geological probability, geological probability components ABSTRACT My research topic is the presentation of the methodology for determining risk factors of geothermal exploration. Hungary's geothermal endowments are very good. The geothermal gradient is higher than 45 C/km in this country. Both in preneogene basement and within the Neogene basin fill there are lots of clastic sediments, karstified, fractured limestone and dolomite in the substantial part of the country which are usually saturated with water and they have relatively high hydraulic conductivity. I used analogy of hydrocarbons exploration during my work. The basis of this is the determination of the chances for success of prospect research assessment, where geological probability is of great importance. The geological probability is the product of favorable output probabilities of the independent components (source rock, reservoir rocks, caprock, traps, migration). The probability of the existence of a supposed field with its prospective recoverable property can be estimated considering the above factors. During my study, I concluded that geothermal reservoirs research components for success are: heat source (magma intrusions), reservoir rocks, impermeable top and bottom seal, heat flow and transfer fluids. If the value of each component is 0.5, then it means that there is no evidence of the presence of any component or unreliable data value. The values less than 0.5 are unfavorable whereas larger values give positive results. I made a geothermal chance for success study considering a Hungarian geothermal system. I used geological probability determination methods which are used in the hydrocarbon industry. Just like in the case of hydrocarbon exploration, the subsequent random occurence of geological phenomena is necessary for the creation of a new field. That is why it is important to estimate the probabilities of the favorable occurences of the necessary geological processes. This is a subjective estimate which was based on the available data on the study area. 1. INTRODUCTION I present the methodology for determining the geothermal research study for success in this study. Hungary's geothermal potential is very good. The geothermal gradient is higher than average, it is 45 C/km in this country. Basis of temperature distribution map of Dövényi et al (2001) the average temperature is C in 1000 meters deep and C in 2000 meters deep. Based on the studies of Lenkey (1999) and Royden et al (1983) this area is a shallow depth geothermal zone, because the crust is thinner than the average. Consequently the heat flow is about mw/m 2. The rising water which originates from the deep basins can cause positive geothermal anomaly too. Over and above there are lot of clastic sediments, karstified, fractured limestone and dolomite in the substantial part of the country, which are usually saturated with water and they have relatively high hydraulic conductivity according to Liebe (2001). These rocks may be suitable for geothermal reservoir. There are mostly liquid-based geothermal systems in Hungary. The methods of prospect research assessment and probability of geological success have long been thoroughly studied for long time by Rose (1992), Otis and Schneidermann (1997). This study was carried out on the basis of their studies. 2. METHOD The probability of geological success (g) is the product of favorable output probabilities of independent system components. These components are source rock, reservoir rocks, caprock, traps and migration connected to hydrocarbon prospects. In this case the geological probability is defined by the following equation by Otis and Schneidermann (1997). g source reservoir trap dynamics (1) where g, source, reservoir, trap, dynamics are the probabilities of geological success, presence of mature source rock, presence of reservoir rock, presence of trap and play dynamics. They correspond to the following components in the geothermal exploration: heat source, reservoir rock, top and bottom seal which form a closed system and fluid flow. Opposed to the hydrocarbon system, the source rock is not involved in the system instead the heat source is displayed. Based on the first equation the following can be prescribed in the case of a geothermal system. 1

2 g heat reservoir closed system dynamics (2) where g, heat, reservoir, trap, dynamics are the probabilities of geological success, presence of heat source, presence of reservoir rock, presence of closed system and play dynamics. These components depend on some subfactors. In the case of the heat source subfactors are the following: type and heatedness of the heat source. The quality of the reservoir rock is determined by appropriate porosity ranges and types, permeability ranges and types, fracture potential and preservation, diagenic characteristics, heterogeneity, thickness. The lateral continuity and extension are important too. The subfactors of the closed system are the type of the caprock, thickness, lateral continuity and extension, degree of fracturing or faulting. In the case of the play dynamics this is type, presence, quantity and relationship with the reservoir are important. Otis and Schneidermann (1997) determined if any of these probability components is very low, the probability of geological success is very low as well. If the probability value of each component is at least 0.5, it means that there is no evidence of the presence of any component or unreliable data value. The values which are less than 0.5 are unfavorable whereas larger values give positive results. If the value is 1, there are obvious evidences of the presence of components. Just like in the case of hydrocarbons, the subsequent random occurence of geological phenomena is necessary for the birth of a new field. I use subjective and statistic estimates in this study and compare them. In case of the statistic estimation I used two interpolating methods which are the inverse distance weighted (IDW) and kriging. The inverse distance weighted interpolation method will give an estimate value for the sought place from the parameters surrounding wells (weighted arithmetic mean). Kriging sets up a trend from the parameters of the surrounding wells and estimates the value for the sought place. I estimated with these methods the porosity, the thickness of the reservoir, the temperature of a geothermal system. Based on these data the expected values were concluded and the probabilities of the system components were defined. Finally the study area is evaluated. It was based on Otis and Schneidermann (1997), who determined the ranges of the risk factors (Fig 1.). Figure 1: The ranges of the geological risk factors by Otis and Schneidermann (1997) Then they identified a risk assessment from the geological risk factors, which are shown in the following figure (Fig 2.). Figure 2: Risk assessment based on the drilling data of many years by Otis and Schneidermann (1997) 3. STUDY AREA The study area is located in a part of Hungary where the heat flow is higher. A city lies a few kilometers from this area with inhabitants, where they can utilize the produced energy. Some of the drillings are business secrets in this area (the data are owned by MOL) that is the reason why the well s identification and coordinates do not appear in this paper. 11 wells and a 3D seismic 2

3 measurement (Fig 3. a) are available close to the study area. The size of the geothermal reservoir is 6-8 km 2, which was estimated by Z- Map lus (Fig 3. b). Figure 3: Location of wells (a) and the geothermal area (b). The red line means the fracture network. reviously, the temperature based on the temperature distribution map of Dövényi et al is 70 C in 1000 meters and C in 2000 meters deep. The estimated result is 137 C by using inverse distance weighted and 134 C by using kriging interpolation method to calculated temperature measurements data of the surrounding wells. The two interpolation methods gave similar results in this case. The reservoir rocks are dolomite and limestone, which are karstified and fractured. The porosity of these rocks are low. The expected value of the porosity is 0,0161 by using invers distance weighted and 0,01 by using kriging interpolation method, but it was overwritten by the fracture network. This type of the secondary porosity provides water flow in the area. In the greater part of the area the ages of these rocks are Triassic, although sometimes rather Upper ermian. The Eocene conglomerates above the carbonate rocks are fractured too, they form a reservoir unit with the Triassic and Upper ermian rocks. The estimated thickness of the reservoir is about 131 meters by using inverse distance weighted and 120 meters by using kriging interpolation methods. These values do not include the thickness of the Eocene. The thickness of the latter is about meters. The pressure is close or under to the hydrostatic pressure in the reservoir. On the picture below the place of section is shown, which across the geothermal reservoir (Fig. 4. a). There are three wells (5, 6 and 7) which are in the area of the geothermal system, but these wells have not reached the Triassic rocks. Figure 4 (a): lace of the seismic section and the closest wells The picture below (Fig 4. b) shows a northwest-southeast section through the reservoir. The reservoir is on the right side. 3

4 Figure 4 (b): Interpreted seismic session across to the reservoir. The red lines mean faults. Three wells reached the bottom seal below the limestone and dolomite. These seal are clay marl and shale, which have Middle Triassic or Upper ermian ages. The thickness of these beds is about 8-18 meters. Somewhere the lower part of carbonate beds are not fractured, that they can behave as a caprock, because the water cannot really flow due to the low porosity. The top caprock are mainly marl and clay. The ages of these beds are Eocene and Oligocene. Few fault passes through in these rocks, but these beds are much less broken and faults can behave to be closed in this area. There are several evidences of the presence of fluids. Many production tests and water analysis were made in the closer wells. Based on these data, the water has similar composition and its NaCl content is high, g/l. This latter may imply that just few water flowed into the system. 4. RISK ASSESSMENT FOR THE STUDY AREA In the previous chapter I presented the study area, which is evaluated in this chapter. The first component is the heat source. Unequivocal evidences are in the case of the presence of heat source for example the temperature distribution map and the data of the nearest wells. Based on the available temperature data, this can be classified in moderate temperature geothermal resource by Muffler (1979). Temperature distribution follows a normal distribution. In this case the best estimation, namely the expected value is 139,5 for this area, where the lowest estimation is 125 C and the highest estimation is 154 C. The expected value is very similar to the values above, which were calculated with interpolation methods (137 C and 134 C). On the basis of these data the probability of this geology component is 0,8. The second component is the reservoir. It is a very fractured carbonate rock, which has low porosity range. The distribution of porosity follows a normal distribution, too, that the expected value is 0,018, where the lowest estimation of porosity is 0,008 and the highest estimation is 0,0208. This is similar to data of inverse distance weighted interpolating, which was 0,016. There was a secondary fragmentation, which improves these values. The signs of fragmentation appear on the seismic section and the core samples. The evidence of connection of cracks is the same water component and content of NaCl. They provide the flow of water. The value of the reservoir component is 0,75 in this case. The third component is the closed system, whose elements are the top and the bottom seal rocks. There are a lot of data to suggest the existence of the top seal rocks. The presence of bottom seal rocks is supported by fewer data. The beds are mostly impermeable in both cases, that are promotes the formation of a closed system. However, a leading fault can make worse the closing ability of these beds. That is the reason why the probability of the presence of closed system is 0,65. The last component is the dynamics. roduction tests demonstrated the presence of water and it can flow due to the fragmentation. The amount of water is not too large, the minimum value of production was 7 m 3 /day, the maximum values of production was 162 m 3 /day. Based on the available data, there is many evidence of the presence of water, but this amount is not evenly distributed. So the value of this component is 0,69. The geological probability of the area is the product of probability values of the components, so g 0,8 0,75 0,65 0,69 (3) 4

5 The value is 0,2691, which corresponds low risk based on Otis and Schneider (1997) is expected. at the beginning of the exploring of the area. Therefore low risk is expected before the start of the drilling. 5. CONCLUSION The geothermal research components for success exploration were summarized in this study using the analogy of hydrocarbon research. Thereafter the values of temperature, porosity and thickness of the reservoir were estimated using inverse distance weighted and kriging interpolation methods. These data were compared to the values of subjective estimate and sometimes these estimations were similar. The study area can be classified in moderate temperature geothermal resource by Muffler (1978) based on the available temperature data. The value of porosity of the reservoir is improved by fractures. Faults sometimes affected the top seal beds, thereby degrading the quality of this. The evidences of the presence of bottom seal beds are few, but the lower part of the concise, non-fractured carbonate beds may behave as a caprock. The water of this system has high NaCl content, which indicates that there is just a little inflow from the upper beds. Based on these data the probability of all components was estimated and the probability of geological success became 0,2691. This value corresponds to low risk in the prospect research. ACKNOWLEDGE I wish to thank MOL Nyrt for granting acces to the data evaluated in this paper, app Simon Foundation and Tamási Városért Foundation for supporting my participation on this workshop. I also would like to express my gratitude to György ogácsás, Boglárka Klemenik and Imre Szilágyi for their comments and suggestions in finalizing this paper. REFERENCES Dövényi,., Drahos, D. and Lenkey, L.: Magyarország geotermikus energia-potenciáljának feltérképezése a felhasználás növelése érdekében. Hőmérsékleti viszonyok, Jelentés a Környezetvédelmi Alap Célelőirányzat részére, Eötvös Loránd Tudományegyetem, Budapest, Hungary (2001). Lenkey, L.: Geothermics of the annonian basin and its bearing on the tectonics of basin evolution, hd Thesis, Vrije Universiteit, Amsterdam (1999). Liebe,.: Termálvízkészleteink, hasznosításuk és védelmük, Tájékoztató, VITUKI Rt. Hidrológiai Intézet, Budapest, Hungary (2001). Muffler, L.. J. and Cataldi, R.: Methods for regional assessment of geothermal resources, Geothermics, 7, (1978), Otis, R. M., Schneidermann, N.: A rocess for Evaluating Exploration rospects, AAG Bulletin, Vol. 81, (1997), 1-7. Rose,. R.: Chance of success and its use in petroleum exploration, In: Steinmetz, R. ed., The Business of etroleum Exploration, AAG Treatise of etroleum Geology, Handbook of etroleum Exploration, Chapter 7, (1992), Royden, L. H., Horváth, F., Nagymarosy, A. and Stegena, L.: Evolution of the annonian basin system: 2. Subsidence and thermal history, Tectonics 2, (1983),