A reformulation of the USGS volumetric "heat in place" method is presented. More specifically, expressions for "recoverable heat" are derived by considering specific power cycles, i.e., single-flash and binary. The latter approach eliminates the ambiguities associated with specifications of a reference temperature and the utilization efficiency. Since the use of an arbitrarily low reference temperature such as the ambient temperature yields too optimistic an estimate for the recoverable heat, the abandonment temperature instead of the ambient (or condenser) temperature should be assumed as the reference temperature in order to obtain realistic estimates of recoverable heat. The standard USGS method will, however, yield a non-zero available work for temperatures between the ambient (or condenser) and abandonment temperatures; in this case, the only way to conform the USGS method to reality is to require conversion (utilization) efficiency to also tend to zero. The probable range for the thermal recovery factor is considered, and it is argued that in the early exploration stage prior to deep drilling and well testing, the proper lower limit for the recovery factor is zero. An illustrative example of the standard USGS method vs. the present reformulation of the USGS method for resource estimation is presented using previously published data from the Sumikawa geothermal field. A Monte Carlo procedure was employed to predict the megawatt capacity at Sumikawa for three cases: (1) the reformulated USGS method, (2) the effect of a zero minimum recovery factor, and (3) the standard USGS method. A total of 100,000 Monte Carlo simulations were used in each case to compute the cumulative probability distribution. Comparing Cases 1 and 3, it is apparent that the original USGS method predicts a much larger capacity than the new formulation. The main effect of a zero minimum recovery factor (Case 2) is to yield a considerably smaller capacity at the 90% confidence level compared to Case 1. In a certain sense, a zero minimum recovery factor encodes a lack of knowledge regarding the productivity of as yet undrilled wells. (C) 2015 Elsevier Ltd. All rights reserved.