The advent of deep learning marked a milestone in the real-life applicability of machine learning tools, as now very complex problems can be solved with unprecedented accuracy. Deep neural networks generally require little explicit prior knowledge and are distinctively efficient in extracting complicated patterns. These capabilities turn them into feasible candidates for replacing and/or...
Natural gas hydrate has huge reserves and is one of the most potential carbon energy resources. In the process of natural gas hydrate production, the phase state changes in the formation. Until now, the gas-liquid two-phase flow mechanism is not well understood for gas hydrate formation. The permeability of gas and water determines the flow capacity of fluids in hydrate formation and directly...
Traditional flow-based two-phase upscaling entails the computation of upscaled relative permeability functions for each coarse block or interface. It can be very time-consuming especially for large models with a large quantity of coarse gird blocks or for cases that requires simulation runs over multiple geological realizations (as commonly used in uncertainty quantification or optimization)....
There are inherent resolution and field-of-view trade-offs in X-Ray micro-computed tomography imaging, which limit the characterization, analysis and model development of porous systems with multi-scale heterogeneities. In this work, we overcome these tradeoffs by utilising a deep convolution neural network to create enhanced, high-resolution data over large spatial scales from low-resolution...
In this talk, we present an effective micro-macro model for reactive flow and transport in evolving porous media exhibiting two competing mineral phases. As such, our approach comprises flow and transport equations on the macroscopic scale including effective hydrodynamic parameters calculated from representative unit cells. Conversely, the macroscopic solutes’ concentrations alter the unit...
The need of flow and transport characterization in underground fractured media is critical in many engineering applications, like fossil fuel extraction and water resources analysis. However, there is a lack of full knowledge (geometrical and hydrogeological) of these fracture systems and, therefore, statistical representations of the fractured media are given. In this context, we perform flow...
In a geological carbon storage project, management of reservoir pressure buildup is essential for long-term safe carbon storage. A reservoir pressure buildup caused by CO2 injection may lead to serious safety issues such as induced seismicity, caprock damage, and leakage of brine and CO2. Brine extraction is a practical solution to mitigate the reservoir pressure buildup. In heterogeneous...
The study of particle transport in porous media is a research field of great interest as it is involved in a wide variety of applications [1]. The random nature of porous media systems makes it difficult to analytically correlate the impact and the synergy of the their geometrical parameters. Since these features make these systems a suitable candidate for machine learning (ML) approaches, in...
Artificial neural networks (ANNs) are well known for its strong learning ability and have been widely used in the petroleum industry, such as history matching, production optimization and productivity forecast. However, ANNs are also a typical kind of “black box” models for their weakness in the model interpretability, causing their results less reliable than those from other physics based...
Numerical simulation is an essential tool for understanding subsurface flow in porous media problems, yet it often suffers from computational challenges due to these problems' highly non-linear governing equations, their multi-physics nature, and the need for high spatial resolutions to capture multi-scale heterogeneity. The inherent parameter uncertainties in subsurface porous media...
Recent advances in multiscale imaging techniques for the analysis of complex pore structures and compositions have revolutionized our ability to characterize various porous media systems. Segmentation of images obtained from different image techniques such as X-ray computed microtomography (μCT) and scanning electron microscopy (SEM) is the first step to quantitatively describe various...
Heterogeneity of microstructures in clastic rocks is relevant to a wealth of subsurface properties (e.g., porosity, permeability, fracture orientations) and, yet, is challenging to effectively characterize because of the stochastic distribution of grain deposition, diagenesis, and texture deformation. At the pore to core scale, heterogeneity lies in the spatial variation of pore throat...
A simulation tool capable of speeding up the calculation for natural convection in porous media is of sizeable practical interest for engineers, in particular, to effectively perform sensitivity analyses, uncertainty quantification, and optimization of $\mathrm{CO_2}$ sequestration and geothermal harvesting. We present a non-intrusive reduced order model (ROM) using the nested proper...
Geostatistical inversion problems in geologic CO2 sequestration (GCS) often involve matching observational data using a physical model that takes a large number of parameters. It is known that solving an inversion problem in a high-dimensional space with complex structure is usually a very time consuming process. In this work, a dimensionality reduction technique, variational autoencoder...
Porous materials are widely used in industrial applications (e.g., catalysis and separations) and diffusion of liquids within these materials can often control performance. Self-diffusion coefficients are typically obtained from molecular dynamics (MD) simulations in which the forces and trajectories of particles are calculated via Newtonian physics for millions of time steps. While MD...
Many recent studies have demonstrated the superior predictive interpretability and physics consistency by anchoring deep feedforward neural networks (DNN) with physics laws. As this type of network is fully connected, it potentially suffers low training efficiency when predicting complex problems such as multiphase flow in porous media.
In this study, we propose a learning framework to...
There are literally a few million boreholes in the continental US (both onshore and offshore) that include abandoned wells, production wells, and wells for underground hydrocarbon storage. Some are vulnerable to potentially catastrophic loss of seal integrity, largely owing to progressive damage of the annular cement sheath. The Deepwater Horizon oil spill and the Aliso Canyon natural gas leak...
Flow and reactive transport in fractured and porous media are fundamental to understanding coupled multiphysics processes critical to various geoscience and environmental applications such as geologic carbon storage, subsurface energy recovery, and environmental biogeochemical processes. Although fluid dynamics simulations provide fundamental solutions to flow and reactive transport processes,...
Multiphysics reactive transport models are nowadays widely employed in subsurface porous media and are able to account for several fully coupled physical and biogeochemical processes. However, the increasing level of detail of such models comes at the price of an increased complexity which often leads to long runtimes. As a result, explorative and probabilistic analysis that require numerous...
With the recent progress in reinforcement learning (RL) research, we investigate whether it would be suitable to use RL in solving optimal well control problem with uncertain reservoir models. In principle, RL algorithms are capable of learning optimal action policies — a map from states to actions — to maximize a numerical reward signal. In the RL formulation of porous media flow control...
