Gregg Nordquist

Geophysical exploration for geothermal resources: an application for combined MT-TDEM

Summary Magnetotelluric sounding combined with co-located time domain electromagnetic sounding (MT-TDEM) is the most important geophysical technique used in the exploration for high temperature (>230C) geothermal resources. Other geophysical methods, especially gravity and galvanic resistivity, may have value in particular geothermal plays. However, MT-TDEM resistivity imaging consistently provides detailed constraints on geothermal conceptual models by resolving the geometry of the thick conductive argillic (smectite clay) alteration that forms over and adjacent to the more resistive propylitic alteration of geothermal reservoirs. MT-TDEM interpretations of the base of the argillic alteration constrain reservoir geometry. Geochemical interpretations of surface thermal features, characterize reservoir temperature and fluid properties. When integrated, these interpretations define conceptual models that effectively support geothermal exploration programs. Where well cuttings are available in drilled fields, smectite measurements can resolve ambiguities in MT-TDEM interpretations directed at geothermal development. The exploration program that led to the most recent discovery of Unocal Geothermal of Indonesia, the Namora-I-Langit Geothermal Field in Sumatra, illustrates a successful application of these techniques.

Role of 1D MT Inversion In a 3D Geothermal Field

An extensive magnetotelluric (MT) survey comprised of 85 sites has been acquired over the Darajat geothermal field in Indonesia to map the geothermal reservoir and the overlying clay cap. The rouged topography and the geometry of the margin of the clay cap makes the resistivity structure 3D at reservoir depth. Although 3D MT inversion is now commonly used in geothermal interpretations 1D and 2D MT inversions are still effective tools for a variety of tasks such as quality assurance. Lower dimensional inversion can also play two critical roles in determining and assessing the resistivity model derived by 3D inversion: 1) by providing a good starting model to reduce the computational cost of the 3D inversion, and 2) by providing a computationally feasible path to stochastic inversion of the data that provides realistic parameter’s standard deviations for use in assessing reliability of the resistivity model. Using a spatially constrained 1D stochastic inversion of the MT data, we investigate the common claim that 1D inversion can provide a pseudo 3D model which closely matches the 3D inversion for the overburden and clay cap layers. The discrepancy between the pseudo 3D and true 3D inverse models increases with depth, however the presence of the core resistive feature of the field is still indicated at approximately the same depth as found in the true 3D model. Analysis of the 1D model parameter probability density functions shows that layer thicknesses are better determined than layer resistivities.

Stochastic inversion of 2D magnetotelluric data using sharp boundary parameterization

Summary Stochastic approaches for inverting geophysical data have many advantages over deterministic inversion methods in terms of finding global solutions and quantifying their associated uncertainty. Since stochastic methods often need to run forward models more times than deterministic algorithms, their applications currently are limited to 1D inverse problems, where forward models can be run very fast. However, due to the rapid growth in computing power, especially parallel computing techniques, and the recent development of new numerical simulation methods, stochastic inversion of 2D geophysical data has become feasible. In this study, we explore the use of Markov chain Monte Carlo based Bayesian models for inverting 2D magnetotelluric (MT) data. To minimize the number of parameters, we adopt sharp boundary parameterization, in which we consider the locations and the resistivity of regions formed by the interfaces as unknowns. We use a parallel, adaptive finite-element algorithm to forward simulate frequency-domain MT responses of 2D conductivity structure. Synthetic case studies show the developed stochastic model is effective for estimating the interface locations and resistivity; most importantly, it provides detailed uncertainty information on each unknown parameter.