Acknowledgements. We would like to thank Total E&P UK in Aberdeen for funding this research. We also thank Fabian Stamm for providing the wonderful synthetic geomodel used in this paper. We are grateful for the constructive reviews from Ashton Krajnovich and an anonymous reviewer for helping us improve this manuscript.Peer reviewedPublisher PD

A. Schaaf

C. E. Bond

F. Wellmann

M. de la Varga

English

Schaaf, Alexander

de la Varga Hormazabal, Miguel

Wellmann, Jan Florian

Bond, Clare E.

Publikationsserver der RWTH Aachen University

Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1

Structural geomodeling is a key technology for the visualization and
quantification of subsurface systems. Given the limited data and the resulting
necessity for geological interpretation to construct these geomodels,
uncertainty is pervasive and traditionally unquantified. Probabilistic
geomodeling allows for the simulation of uncertainties by automatically
constructing geomodel ensembles from perturbed input data sampled from
probability distributions. But random sampling of input parameters can lead to
construction of geomodels that are unrealistic, either due to modeling artifacts
or by not matching known information about the regional geology of the modeled
system. We present a method to incorporate geological information in the
form of known geomodel topology into stochastic simulations to constrain
resulting probabilistic geomodel ensembles using the open-source geomodeling
software GemPy. Simulated geomodel realizations are checked against topology
information using an approximate Bayesian computation approach to avoid the
specification of a likelihood function. We demonstrate how we can infer the
posterior distributions of the model parameters using topology information in two
experiments: (1) a synthetic geomodel using a rejection sampling scheme
(ABC-REJ) to demonstrate the approach and (2) a geomodel of a subset of the
Gullfaks field in the North Sea comparing both rejection sampling and a
sequential Monte Carlo sampler (ABC-SMC). Possible improvements to processing
speed of up to 10.1 times are discussed, focusing on the use of more advanced
sampling techniques to avoid the simulation of unfeasible geomodels in the first
place. Results demonstrate the feasibility of using topology graphs as a summary
statistic to restrict the generation of geomodel ensembles with known
geological information and to obtain improved ensembles of probable geomodels
which respect the known topology information and exhibit reduced uncertainty
using stochastic simulation methods.</p

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Acknowledgements. We would like to thank Total E&P UK in Aberdeen for funding this research. We also thank Fabian Stamm for providing the wonderful synthetic geomodel used in this paper. We are grateful for the constructive reviews from Ashton Krajnovich and an anonymous reviewer for helping us improve this manuscript.Peer reviewe

de la Varga, Miguel

Wellmann, Florian

Aberdeen University Research

Constraining stochastic 3-D structural geological models with topology information using Approximate Bayesian Computation in GemPy 2.1

https://gmd.copernicus.org/articles/14/3899/2021/gmd-14-3899-2021.pdf

Constraining stochastic 3-D structural geological models with topology information using Approximate Bayesian Computation in GemPy 2.1

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Aberdeen University Research