PROCEEDINGS, Thirty-Sixth Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, January 31 - February 2, 2011 SGP-TR-191 A REEXAMINATION OF USGS VOLUMETRIC “HEAT IN PLACE” METHOD Sabodh K. Garg Science Applications International Corporation 10260 Campus Point Drive San Diego, CA, 92121, USA e-mail: gargs@saic.com Jim Combs Geo Hills Associates LLC 1950 Champion Hills Drive Reno, NV, 89523-3886, USA e-mail: jimjeany@ix.netcom.com ABSTRACT The USGS volumetric estimation method together with Monte Carlo simulations is often used to provide estimates of the probable electrical generation capacity of a geothermal system. The methodology consists of combining probability density functions for uncertain estimates of the temperature, area, thickness, and thermal recovery factor of a geothermal reservoir to obtain the probability distribution function for the stored energy (“heat in place”) and the recoverable heat. The electrical capacity of the potential geothermal reservoir is then computed using a conversion (or utilization) efficiency. In a previous paper, we discussed the importance of choosing the probability distributions of the reservoir parameters based on actual field data. Herein, we examine the specification of the reference temperature and the conversion efficiency. We show that the conversion efficiency depends on both the reference temperature and the power cycle (flash, binary). A proper understanding of the latter relationship is essential for obtaining estimates of reservoir capacity for electrical generation. INTRODUCTION During early stage exploration of geothermal resources associated with an identified hydrothermal convection system, it is necessary to obtain an estimate of the potential electrical energy that might be produced from the delineated geothermal system. In the 1970s, researchers at the United States Geological Survey (USGS) developed a methodology to quantify the uncertainty of estimates of the geothermal resources associated with an identified hydrothermal convection system (e.g., Nathenson, 1975a; 1975b; Nathenson and Muffler, 1975; Muffler and Cataldi, 1978; Brook, et al., 1979). The USGS volumetric estimation methodology consists of combining estimates with uncertainties for the temperature, area, thickness, and thermal recovery factor of a geothermal reservoir into estimates of the stored heat (“heat in place”) and the recoverable energy with uncertainty. An estimate of the recoverable energy together with a conversion efficiency (or utilization factor) is then used to compute the electrical capacity. The parameters required for the computation of electric capacity of the “heat in place” are indicated in Table 1. Table 1. Parameters required for the calculation of the electric generation capacity using the USGS volumetric “heat in place” method. Group 1 Parameters: Reservoir Area (km 2 ) Reservoir Depth (m) Reservoir Thickness (m) Reservoir Temperature (°C) Thermal Recovery Factor (%) Group 2 Parameters: Volumetric Heat Capacity (kJ/m 3 -K) Rejection Temperature (°C) Conversion Efficiency (%) Plant or Project Life (years) Plant Load Factor (%) The parameters in Table 1 can be divided into two groups. Specification of statistical distributions for the parameters in the first group (reservoir area, reservoir depth, reservoir thickness, reservoir temperature, thermal recovery factor) is at best a difficult task and is highly dependent on the stage of exploration of a geothermal system. In a previous paper (Garg and Combs, 2010), we remarked that as far as possible, actual field data should be used when prescribing reservoir parameters. Without data-driven reservoir parameters, use of Monte Carlo simulations is liable to generate unreliable estimates of reservoir megawatt capacity. The second of these groups (Group 2 Parameters) contains parameters whose value does not either vary substantially from case to case (volumetric heat capacity, power plant or project life, power plant load factor) or can be specified sufficiently accurately using available engineering data (rejection temperature, conversion efficiency). In this paper, we consider the specification of rejection temperature (sometimes called reference temperature