Abstract. Geological uncertainty quantification is critical to subsurface modeling and
prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be
continuously updated with new data. We provide an automated method for
uncertainty quantification and the updating of geological models using borehole
data for subsurface developments within a Bayesian framework. Our
methodologies are developed with the Bayesian evidential learning protocol
for uncertainty quantification. Under such a framework, newly acquired
borehole data directly and jointly update geological models (structure,
lithology, petrophysics, and fluids), globally and spatially, without
time-consuming model rebuilding. To address the above matters, an ensemble of
prior geological models is first constructed by Monte Carlo simulation from
prior distribution. Once the prior model is tested by means of a falsification
process, a sequential direct forecasting is designed to perform the joint
uncertainty quantification. The direct forecasting is a statistical learning
method that learns from a series of bijective operations to establish
“Bayes–linear-Gauss” statistical relationships between model and data
variables. Such statistical relationships, once conditioned to actual
borehole measurements, allow for fast-computation posterior geological
models. The proposed framework is completely automated in an open-source
project. We demonstrate its application by applying it to a generic gas
reservoir dataset. The posterior results show significant uncertainty
reduction in both spatial geological model and gas volume prediction and
cannot be falsified by new borehole observations. Furthermore, our automated
framework completes the entire uncertainty quantification process
efficiently for such large models.
Standardization of Uncertainty Quantification of Sub-surface Geological Models through a Field’s Life Cycle
