Transdimensional reservoir data inversion for a {2D} geological layered model with horizon slopes and horizontal permeability trends

@inproceedings{herrero_transdimensional_RM2024-1,
 abstract = {We introduce a transdimensional inversion framework to
quantify stratigraphic uncertainties in reservoir layered models
from well data. The goal is to adaptively discover the
suitable number of subsurface model parameters. For
this, we build upon an existing transdimensional sampler
which considers a horizontal layered parameterization
and an uncertain number of layers. Our approach
integrates the dip of horizons to represent thickness
variations, and a horizontal permeability gradient to
reflect possible lateral petrophysical trends in
geological strata. The number N of geological layers in
the model is made variable and defines the model
dimensionality, so that model parameters to infer
correspond to (1) the number of layers N, (2) the
average permeability for each layer, (3) the horizontal
gradients of permeability in each layer, (4) N-1 interface depths and (5) interface slope angles. The inverse problem is set in a Bayesian framework and prior distributions are defined for each of these parameters. We use a reversible jump Markov chain Monte Carlo algorithm to determine both the optimal number of layers and their respective properties. We apply this approach to a simple case aimed at reconstructing a two-dimensional continuous field using two synthetic well logs of permeability, each located on opposite sides of the domain. This shows the ability of the method to retrieve a posterior density close to that of the reference solution, and demonstrates the potential application to real borehole data. Overall, the proposed methodology is a way to integrate geological parameters in a unified modeling framework, and can be extended to seismic inversion, flow data inversion (e.g., for well test interpretation), or other types of geophysical data.},
 author = {Herrero, Julien and Caumon, Guillaume and Bodin, Thomas},
 booktitle = {Proc. 2024 RING Meeting},
 language = {en},
 pages = {11},
 publisher = {ASGA},
 title = {Transdimensional reservoir data inversion for a {2D} geological layered model with horizon slopes and horizontal permeability trends},
 year = {2024}
}