Title:

Resource Capacity Estimation Using Lognormal Distributions of Power Density Derived from Producing Fields and Area Derived from Resource Conceptual Models; Advantages, Pitfalls and Remedies

Authors:

William CUMMING

Key Words:

capacity, resource assessment, power density

Conference:

Stanford Geothermal Workshop

Year:

2016

Session:

General

Language:

English

Paper Number:

Cumming

File Size:

673 KB

View File:

Abstract:

The power density method for estimating potential power generation capacity of geothermal exploration prospects is recommended because it is relatively simple, case histories have been graphically compiled to support parameter estimates, and its pitfalls are more easily appreciated than is the case for many alternatives. In this implementation, a lognormal capacity distribution in MWe is obtained by multiplying two lognormal probability distributions, one for interpreted resource area in km2 and one for power density in MWe/km2. The probability distribution for the interpreted area of the resource is estimated by constructing a range of geothermal resource conceptual models that are representative of the P10, P50 and P90 confidence intervals commonly used in resource assessment, and that consider both data and conceptual uncertainty. The conceptual models integrate available geoscience data in an manner consistent with hydro-thermodynamics. A distribution for power density can be estimated from the Wilmarth and Stimac graphical compilation of worldwide geothermal field power density versus resource temperature, zoned by conceptual setting. This approach shares many of the problems of other geothermal resource assessment methods but its simplicity often makes recognizing pitfalls more likely and mitigating them more tractable. For example, one issue with this implementation is its potentially inconsistent area estimates. Because updated conceptual models are not published for most geothermal fields, the power densities are based on areas estimated from the perimeter of the drilled production zones, not from conceptual models. Comparisons of conceptually relevant details from roughly analogous fields can provide a consistency check on power density and on decision tree cases used to provide weighted economics of realistic developments. This approach is implemented in an Excel workbook that has been widely used for training courses and economic decision-making in the geothermal industry. It is available from the author as freeware subject to users’ reference to this paper in initial publications of projects that utilize it.


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