Florian Doster

Geological CO ₂ storage is transitioning from demonstration to industrial-scale deployment, and faults within and around storage complexes exert a first-order control on its safety and performance. Faults can compartmentalise reservoirs through across-fault sealing, provide pathways for vertical migration of CO ₂ and brine across caprocks, and host induced seismicity when reactivated by pore-pressure, thermal and geochemical changes during and after injection. These behaviours are not independent in the sense that the same fault can seal hydraulically while remaining critically stressed, or slip without becoming transmissive, and its state can evolve over engineering to geological timescales. We synthesise structural geology, fault-seal analysis, geomechanics, geochemistry, modelling and the operational record from projects including Sleipner, In Salah, Decatur, Quest, Snøhvit, Aquistore and Gorgon. No measurable fault-related leakage from a storage complex has been documented at any saline-aquifer or depleted-gas-field project. Microseismicity on unmapped sub-seismic and basement structures is recurrent, and in 2026 two events above M _L 4.0 near Quest became the first felt events at a storage project, provisionally attributed to injection and without effect on containment. Because the pressure and stress perturbation extends far beyond the CO ₂ plume, the faults that host seismicity commonly lie beneath or outside the storage complex and loading there can peak after injection stops. We review across-fault sealing proxies and their CO ₂ -specific uncertainty, fault-fracture network controls on leakage, induced-seismicity mechanisms, natural analogues, regulatory frameworks and post-injection behaviour, and identify five priority data classes and the coupled workflows needed to convert them into defensible risk envelopes.

Understanding CO ₂ nanobubble formation in water‐saturated sandstone is critical for understanding fluid behavior in CO ₂ storage systems. Here, CO ₂ exsolution from an aqueous phase in a sandstone was investigated using small‐angle neutron scattering at 50°C during cyclic depressurization from 12 to 0.7 MPa. Nanoscale heterogeneities consistent with CO ₂ clusters and nanobubbles (5–200 nm) were resolved during pressure reduction. Although bulk phase diagrams predict exsolution at ∼8 MPa at 50°C, detectable exsolution emerged only at 2.4 MPa, indicating strong confinement and surface effects. A progressive loss of signatures associated with nanobubbles <∼15 nm suggests preferential disappearance of nanobubbles, consistent with curvature‐driven coarsening (e.g., Ostwald ripening). Repeated cycling revealed partial qualitative reversibility, implying nucleation and saturation‐history (hysteresis) effects relevant to operational pressure transients in CO ₂ storage. Our findings improve assessment of CO ₂ mobility, trapping, and leakage risk under dynamic pressure in CO ₂ storage systems.

Simulating the fluid flow along fault zones at different scales is essential for predicting the CO ₂ leakage and containment during injection and storage. However, this can be challenging, especially in the early stages of a storage project when knowledge of the reservoir and caprock is limited and the cost of obtaining the relevant data is high. This study develops a tool for fast screening of fault leakage at the site screening stage. The tool uses a vertically integrated reservoir model coupled with a newly developed upscaled fault leakage function based on source/sink relations. The fault is conceptualized as an increased vertical permeability through the caprock due to the presence of a fracture network in the damage zone and a reduced horizontal permeability in the reservoir due to fault throw and presence of a low‐permeability fault core. The proposed tool is validated against numerical simulations demonstrating strong agreement in predicting leakage rates under varying reservoir conditions. The model's capabilities are further tested through simulation cases, including a field‐scale application in the Malay Basin. These cases revealed key insights into the roles of fault permeability and fault capillary entry pressure in controlling leakage and highlighted the importance of accurately characterizing these properties to mitigate risks. The computationally efficient model presented in this study is a valuable tool for quantifying uncertainties in key fault parameters, and other constitutive relations that affect the behavior of the storage reservoir and potential fault leakage.

Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-based solutions, most of them built upon convolutional neural networks (CNNs), have been recently designed to tackle this problem. However, these solutions were limited to approximating one field over the domain (e.g. velocity field), not accounting for the coupled evolution of multiple interacting fields, including concentration, porosity and flow rates. In this manuscript, we present a novel deep learning approach that incorporates both temporal and spatial information to predict the future states of the dissolution process at a fixed time-step horizon, given a sequence of input states. The overall performance, in terms of speed and prediction accuracy, is demonstrated on a numerical simulation dataset, comparing its prediction results against state-of-the-art approaches, also achieving a speedup around 104 over traditional numerical simulators.

Fluid flow in low-permeability caprocks is largely controlled by fractures. However, these fractures are often oversimplified as planar structures. Furthermore, existing studies provide limited insights into the comparisons between naturally occurring shear fractures and artificially induced tensile fractures. As such, it is yet to be quantified how flow models induced fractures adequately capture flow in natural subsurface fractures, which are difficult to obtain. Here, we examine three natural shear fractures and two induced tensile fractures from the same low-permeability lithology. Using high-resolution synchrotron X-ray computed micro-tomography, we extracted three-dimensional fracture void geometries to analyse aperture distributions, surface roughness, and spatial correlation patterns. We then compared measured fracture transmissivities against theoretical predictions from parallel-plate models (cubic law) and direct numerical simulations (DNS) of flow to evaluate consistency and uncertainties. The results reveal that despite important differences in heterogeneity, induced and natural fractures can generally yield similar flow in caprocks. Our comparisons further indicate that the choice of flow estimation method can introduce more uncertainty than the fracture opening mode. These findings highlight the importance of anchoring modelling approaches when predicting permeability in fractured caprock. They also demonstrate the usability of laboratory-induced tensile fractures to study subsurface flow.

Structural uncertainties and unresolved features in fault zones hinder the assessment of leakage risks in subsurface storage. Understanding multi‐scale uncertainties in fracture network conductivity is crucial for mitigating risks and reliably modeling upscaled fault leakage rates. Conventional models, such as the Cubic Law, which is based on mechanical aperture measurements, often neglect fracture roughness, leading to model misspecifications and inaccurate conductivity estimates. Here, we develop a physics‐informed, AI‐driven correction of these model misspecifications by automatically integrating roughness effects and small‐scale structural uncertainties. Using Bayesian inference combined with data‐driven and geometric corrections, we reconstruct local hydraulic aperture fields that reliably estimate fracture conductivities. By leveraging interactions across scales, we improve upon traditional empirical corrections and provide a framework for propagating uncertainties from individual fractures to network scales. Our approach thereby supports robust calibration of conductivity ranges for fault leakage sensitivity analyses, offering a scalable solution for subsurface risk assessment.

Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-based solutions, most of them built upon convolutional neural networks (CNNs), have been recently designed to tackle this problem. However, these solutions were limited to approximating one field over the domain (e.g. velocity field). In this manuscript, we present a novel deep learning approach that incorporates both temporal and spatial information to predict the future states of the dissolution process at a fixed time-step horizon, given a sequence of input states. The overall performance, in terms of speed and prediction accuracy, is demonstrated on a numerical simulation dataset, comparing its prediction results against state-of-the-art approaches, also achieving a speedup around 10⁴ over traditional numerical simulators.

Different operations dealing with the subsurface, such as subsurface CO2 disposal, hazardous waste disposal, geothermal energy extraction, underground hydrogen storage, etc., can change the fluid/flow system underground. The injection of fluids with thermodynamic and chemical properties different from those of the reservoir fluid can trigger a series of chemical reactions, which may affect the fluid and/or rock properties. Depending on the system under study, these changes may be advantageous or unfavorable. Reactive transport modeling is a choice for investigating how these changes can alter the system. In this study, a reactive transport solver is developed in the MATLAB Reservoir Simulation Toolbox using the sequential fully-implicit approach. The developed reactive transport solver is illustrated using reactions and geometries using reactions and geometries relevant for assessing the sealing capacity of a fractured caprock of a deep saline aquifer used for underground CO2 disposal, and the limitations and advantages of the approach are stated. Moreover, the results of the simulation for two fracture models, the discrete fracture matrix and embedded discrete fracture matrix models, are compared. The simulations demonstrate that hydrogen ion concentration or pH is the primary parameter affecting the extent of dissolution, while the other aqueous species concentrations are less influential. It is also shown that at higher flow rates, dissolution substantially occurs in the vicinity of the main fracture, along the flow direction, while at lower flow rates, because the injected fluid becomes fully buffered closer to the inlet, dissolution only occurs in the vicinity of the inlet over the course of the simulation. Applying the discrete fracture matrix and embedded discrete fracture matrix models to one of the scenarios demonstrates that both yield equivalent results. Document Type: Original article Cited as: Moslehi, S., Fazeli, H., Doster, F., Kord, S. Development of a reactive transport solver in MATLAB Reservoir Simulation Toolbox using the fully-implicit sequential iterative approach. Advances in Geo-Energy Research, 2025, 16(2): 114-130. https://doi.org/10.46690/ager.2025.05.04

The Malay Basin is a mature hydrocarbon province currently being re‐assessed for CO₂ storage. Selecting an appropriate storage site requires a comprehensive understanding of the structural and stratigraphic history of the basin. However, previous studies have been limited to observations from either regional 2D seismic lines or individual 3D seismic volumes. In this study, we access and utilise a basin‐wide (ca. 36,000 km²) 3D seismic and well database to describe the structural and stratigraphic features of the basin, particularly those within the uppermost ca. 4 km (Oligocene to Recent) and gain new insights into the basin's evolution. E–W transtensional rift basins first developed due to sinistral shear across an NW‐SE strike‐slip zone. The NW‐SE basin morphology seen today was generated during the late Oligocene–early Miocene, during which time dextral motion across marginal hinge zones created en‐echelon antithetic, extensional faults and pull‐apart basins, especially well preserved along the western margin of the basin. Collisional forces to the southeast during the early to middle Miocene resulted in the shallowing of the basin, intermittent connection to the South China Sea and a cyclic depositional pattern. Around 8 Ma (late Miocene), a significant uplift of the basin resulted in a major unconformity with up to 4.2 km of erosion and exhumation in the southeast. In the centre and northwest of the basin, the inversion of deeper E–W rifts resulted in the folding of Miocene sequences and the formation of large anticlines parallel to the rift‐bounding faults. The Pliocene to Pleistocene history is more tectonically quiescent, but some extensional faulting continued to affect the northwest part of the basin. Larger glacio‐eustatic sea‐level fluctuations during this time resulted in major changes in sedimentation and erosion on the Sunda Shelf, including the formation of a middle‐Pliocene unconformity. These structural events have created a variety of hydrocarbon traps across the basin of different ages, including transpressional anticlines, rollover anticlines and tilted fault blocks. Each of these has discrete and distinct trap elements with important implications for their CO₂ storage potential.