Geological Carbon Sequestration (GCS) has emerged as a promising strategy for mitigating global warming, yet its effectiveness heavily depends on accurately characterizing subsurface flow dynamics. The inherent geological uncertainty, stemming from limited observations and reservoir heterogeneity, poses significant challenges to predictive modeling. Existing methods for inverse modeling and uncertainty quantification are computationally intensive and lack generalizability, restricting their practical utility. Here, we introduce a Conditional Neural Field Latent Diffusion (CoNFiLD-geo) model, a generative framework for efficient and uncertainty-aware forward and inverse modeling of GCS processes. CoNFiLD-geo synergistically combines conditional neural field encoding with Bayesian conditional latent-space diffusion models, enabling zero-shot conditional generation of geomodels and reservoir responses across complex geometries and grid structures. The model is pretrained unconditionally in a self-supervised manner, followed by a Bayesian posterior sampling process, allowing for data assimilation for unseen/unobserved states without task-specific retraining. Comprehensive validation across synthetic and real-world GCS scenarios demonstrates CoNFiLD-geo's superior efficiency, generalization, scalability, and robustness. By enabling effective data assimilation, uncertainty quantification, and reliable forward modeling, CoNFiLD-geo significantly advances intelligent decision-making in geo-energy systems, supporting the transition toward a sustainable, net-zero carbon future.
This technical note describes the FluidFlower concept, a new laboratory infrastructure for geological carbon storage research. The highly controlled and adjustable system produces a strikingly visual physical ground truth of studied processes for model validation, comparison, and forecasting, including detailed physical studies of the behavior and storage mechanisms of carbon dioxide and its derivative forms in relevant geological settings for subsurface carbon storage. The design, instrumentation, structural aspects and methodology are described. Furthermore, we share engineering insights on …
Geological carbon capture and sequestration (CCS), where CO$_2$ is stored in subsurface formations, is a promising and scalable approach for reducing global emissions. However, if done incorrectly, it may lead to earthquakes and leakage of CO$_2$ back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To …
Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage
Faculty of Civil Engineering；Geosciences, TU Delft, Stevinweg 1, 2628 CN Delft, Netherlands；Centrum Wiskunde \& Informatica, Science Park 123, 1098 XG Amsterdam, Netherlands；Simulation Group, Imperial College London, London, United Kingdom；Department of Energy Resources Engineering, Stanford University, CA, USA
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fr…
This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO2 plumes during injection and post-injection periods of geologic CO2 sequestration (GCS) operations. Based on a Fourier Neuron Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO2…
CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration o…
Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 plumes is weak in amplitude and therefore difficult to detect seismically. This situation is compounded by the fact that the surveys are often coarsely sampled and not replicated to reduce costs. As a result, images obtained for different vintages (baseline and monitor surveys) often contain artifacts that may be attributed wrongly to time-lapse changes. To address these issues, we propose to invert the baseline and monitor surveys jointly. By using the j…
In addition to reducing anthropogenic emissions of CO$_2$, it is increasingly clear we also need to remove CO$_2$ from the atmosphere in order to avoid some of the worst case scenarios for climate change. Geologic sequestration of CO$_2$ is among the most attractive approaches because of the large global capacity and long-time scales for storage. One mechanism of geologic storage is through carbon mineralization. Some mafic and ultramafic rocks contain minerals that will react with CO$_2$ in a carbonation reaction and convert it to carbonated minerals. This is effectively a permanent CO$_2$ st…
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic im…
Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage
Department of Earth and Planetary Sciences, Jackson School of Geosciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地球科学学院地球与行星科学系）；Bureau of Economic Geology, The University of Texas at Austin（德克萨斯大学奥斯汀分校经济地质局）；Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校希尔德布兰德石油与地质系统工程系）
Serpentinization and carbonation of ultramafic formations is a ubiquitous phenomenon, which deeply influences the biogeochemical cycles of water, hydrogen, carbon... while supporting the particular biosphere around the oceanic hydrothermal vents. Carbonation of peridotites and other mafic and ultramafic rocks is also a hot topic in the current energy landscape as the engineered sequestration of mineral CO2 in these formations could help reduce the atmospheric emissions and cope with climate change. In this study, we present two reactive percolation experiments performed on a natural serpentini…
The partial differential equations describing immiscible, but soluble, carbon dioxide (CO2) displacement of brine are developed including local mass-transfer effects. Scaling relationships for characteristic time among laboratory and representative storage formation conditions are found upon assumption that free-phase CO2 transport during injection is dominated by convection. The implication is that an hour in the FluidFlower (large-scale visual model) scales to hundreds of years of elapsed time in the storage formation. The scaling criteria permit extrapolation of the effects of changes in pa…
Mineralization of carbon dioxide is often seen as an attractive alternative to classical Carbon Capture and Storage (CCS) technologies, allowing the sequestration of \ce{CO2} as a solid mineral with no risk of aquifer contamination or leakage back to the atmosphere. While olivine and pyroxenes are known to easily and quickly react with dissolved \ce{CO2}, fresh peridotites are quite rare and ultramafic rocks usually contain significant amounts of serpentine, which presents a lower reactivity. The purpose of this study was then to analyze the reactivity of two natural rocks: a partially serpent…
Depleted oil fields in the Gulf of Suez (Egypt) can serve as geothermal reservoirs for power generation using a CO2-Plume Geothermal (CPG) system, while geologically sequestering CO2. This entails the injection of a substantial amount of CO2 into the highly permeable brine-saturated Nubian Sandstone. Numerical models of two-phase flow processes are indispensable for predicting the CO2-plume migration at a representative geological scale. Such models require reliable constitutive relationships, including relative permeability and capillary pressure curves. In this study, quasi-static pore-netwo…
Stochastic Modeling and Upscaling of Hydrodynamic Transport in Geological Fractures
Università di Bologna（博洛尼亚大学）；Stanford University（斯坦福大学）；University of Rennes（雷恩大学）；CNRS（法国国家科学研究中心）；Institut Universitaire de France (IUF)（法兰西学院）；Spanish National Research Council (IDAEA-CSIC)（西班牙国家研究委员会）
