Abstract:
In geothermal exploration, subsurface electrical resistivity models derived from magnetotelluric
(MT) inversions have played a leading role in defining drilling targets during the
early stages of projects. Resistivity models can indicate the depth and extent of a shallow
conductor presumed to be reflective of a clay cap, a low permeable formation that lies on
top of high-temperature geothermal reservoirs. When modeling MT data, gradient-based
deterministic inversion has been the standard. However, for geothermal energy developers
interested in knowing the clay cap boundary location, an undesirable aspect is the
inherent non-uniqueness and the implied uncertainty that is not properly captured when
inferring the conductor geometry from deterministic inversions. Also, when selecting a
single model from a set of plausible models, such as the optimal solution derived from deterministic
inversions, incorrect inference on conductor thickness and resistivity is likely
being made. Consequently, operational decision-making – drilling, resource estimation –
that relies on these could be compromised. In this situation, it is preferable to present
the set of plausible models derived from a stochastic inversion that explicitly represents
the irreducible uncertainty as estimated depth intervals for clay cap boundaries.
The rock resistivity signature in geothermal fields is controlled by variations in multiple
physical parameters like permeability, temperature, fluid salinity, and conductivity
of the rock matrix, which has potentially been hydrothermally altered. So, the correct
interpretation of resistivity models from MT inversions in geothermal fields requires comparison
with direct observation in wells of key features as temperature, lithology, and
clay content. Typically, comparisons between multi datasets are performed qualitatively,
but the derived interpretations could be subjective. Joint inversion/modeling schemes of
multi datasets offers a different perspective. They could provide new insights on rock
properties correlations consistent with multiple sources of information.
This thesis first develops a 1D MT Bayesian inversion that incorporates methylene
blue data from wells (MeB), a clay content indicator, as a constraint. We use this lithological
information to construct structural prior distributions for the MT inversion. The
inversion is formulated in the context of geothermal fields to infer under uncertainty
the shallow conductor boundary reflective of the clay cap. The inversion is tested on a
synthetic example and then applied to the Wairakei-Tauhara geothermal field of New
Zealand. The second part of this thesis develops a multi dataset stochastic modeling of
the inferred clay cap by MT and MeB data along with well lithology and temperature
logs to estimate temperature gradients, clay formation temperatures, and the heat output
of Wairakei-Tauhara.
Our goal in developing these methods is to provide a means to propagate uncertainty
through the inverse problem so that the output is useful to decision-makers. Furthermore,
by bringing in information obtained by direct observations in wells, we strengthen the
connection between model and reality while reducing uncertainty. The methods could improve
conceptual models of shallow geothermal hydrology, provide additional constraints
for geothermal reservoir simulations, and support decision-making processes that define
drilling targets or field management strategies. Further, by integrating multiple data sets,
we generate a holistic image of the upper part of the magmatic-hydrothermal system that
is consistent with the constituent parts each data type is informative of. This helps us
understand the clay alteration distribution in geothermal fields, how they relate to temperature,
and, by quantifying uncertainty, define appropriate limits on those inferences.
Finally, we explore the inferred clay cap’s explicit inclusion to a TOUGH2 geothermal
flow model, adding new constraints to reservoir simulations. The scheme developed serves
as a proof of concept experiment of how prior structural information and geophysical
inferences can be incorporated into reservoir simulations. Its application to more refined
models will allow to construct better permeability models, improve the understanding of
the clay cap and its influence on the geothermal reservoir simulations. Also, we apply
our Bayesian inversion scheme to MT data and well salinity logs collected offshore for
groundwater exploration. We show how the developed inversion scheme can be applied to
other exploration fields where geophysical electromagnetic surveys and drill hole data are
available. This constitutes another example of how the non-uniqueness arising through
trade-offs between parameters in geophysical inversions can be managed by including
informative priors based on well logs.