849 research outputs found
4D Seismic History Matching Incorporating Unsupervised Learning
The work discussed and presented in this paper focuses on the history
matching of reservoirs by integrating 4D seismic data into the inversion
process using machine learning techniques. A new integrated scheme for the
reconstruction of petrophysical properties with a modified Ensemble Smoother
with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed.
The permeability field inside the reservoir is parametrised with an
unsupervised learning approach, namely K-means with Singular Value
Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit
(OMP) technique which is very typical for sparsity promoting regularisation
schemes. Moreover, seismic attributes, in particular, acoustic impedance, are
parametrised with the Discrete Cosine Transform (DCT). This novel combination
of techniques from machine learning, sparsity regularisation, seismic imaging
and history matching aims to address the ill-posedness of the inversion of
historical production data efficiently using ES-MDA. In the numerical
experiments provided, I demonstrate that these sparse representations of the
petrophysical properties and the seismic attributes enables to obtain better
production data matches to the true production data and to quantify the
propagating waterfront better compared to more traditional methods that do not
use comparable parametrisation techniques
Surrogate Model for Geological CO2 Storage and Its Use in MCMC-based History Matching
Deep-learning-based surrogate models show great promise for use in geological
carbon storage operations. In this work we target an important application -
the history matching of storage systems characterized by a high degree of
(prior) geological uncertainty. Toward this goal, we extend the recently
introduced recurrent R-U-Net surrogate model to treat geomodel realizations
drawn from a wide range of geological scenarios. These scenarios are defined by
a set of metaparameters, which include the mean and standard deviation of
log-permeability, permeability anisotropy ratio, horizontal correlation length,
etc. An infinite number of realizations can be generated for each set of
metaparameters, so the range of prior uncertainty is large. The surrogate model
is trained with flow simulation results, generated using the open-source
simulator GEOS, for 2000 random realizations. The flow problems involve four
wells, each injecting 1 Mt CO2/year, for 30 years. The trained surrogate model
is shown to provide accurate predictions for new realizations over the full
range of geological scenarios, with median relative error of 1.3% in pressure
and 4.5% in saturation. The surrogate model is incorporated into a Markov chain
Monte Carlo history matching workflow, where the goal is to generate history
matched realizations and posterior estimates of the metaparameters. We show
that, using observed data from monitoring wells in synthetic `true' models,
geological uncertainty is reduced substantially. This leads to posterior 3D
pressure and saturation fields that display much closer agreement with the
true-model responses than do prior predictions
Optimizing Carbon Storage Operations for Long-Term Safety
To combat global warming and mitigate the risks associated with climate
change, carbon capture and storage (CCS) has emerged as a crucial technology.
However, safely sequestering CO2 in geological formations for long-term storage
presents several challenges. In this study, we address these issues by modeling
the decision-making process for carbon storage operations as a partially
observable Markov decision process (POMDP). We solve the POMDP using belief
state planning to optimize injector and monitoring well locations, with the
goal of maximizing stored CO2 while maintaining safety. Empirical results in
simulation demonstrate that our approach is effective in ensuring safe
long-term carbon storage operations. We showcase the flexibility of our
approach by introducing three different monitoring strategies and examining
their impact on decision quality. Additionally, we introduce a neural network
surrogate model for the POMDP decision-making process to handle the complex
dynamics of the multi-phase flow. We also investigate the effects of different
fidelity levels of the surrogate model on decision qualities
Graph Convolutional Networks for Simulating Multi-phase Flow and Transport in Porous Media
Numerical simulation of multi-phase fluid dynamics in porous media is
critical for many subsurface applications. Data-driven surrogate modeling
provides computationally inexpensive alternatives to high-fidelity numerical
simulators. While the commonly used convolutional neural networks (CNNs) are
powerful in approximating partial differential equation solutions, it remains
challenging for CNNs to handle irregular and unstructured simulation meshes.
However, subsurface simulation models often involve unstructured meshes with
complex mesh geometries, which limits the application of CNNs. To address this
challenge, here we construct surrogate models based on Graph Convolutional
Networks (GCNs) to approximate the spatial-temporal solutions of multi-phase
flow and transport processes. We propose a new GCN architecture suited to the
hyperbolic character of the coupled PDE system, to better capture the
saturation dynamics. Results of 2D heterogeneous test cases show that our
surrogates predict the evolutions of the pressure and saturation states with
high accuracy, and the predicted rollouts remain stable for multiple timesteps.
Moreover, the GCN-based models generalize well to irregular domain geometries
and unstructured meshes that are unseen in the training dataset
- …