In this work, we present a kinetic simulation model for gas hydrates in porous media using the Operator-Based Linearization (OBL) technique. The OBL approach introduces algebraic operators that represent the physical terms in the mass and energy balance equations. Operators are calculated only in supporting points comprising the discretized parameter space, and operator values and partial derivatives for linear system assembly are readily obtained through (multi-)linear interpolation. Taking advantage of this setup, the implementation of advanced thermodynamic models for hydrate formation and dissociation under kinetic assumptions is simplified. We test the assumptions for thermodynamic modelling by analysing the Gibbs energy surfaces of the fluid and hydrate phases and demonstrate that, in the limit, the thermodynamic equilibrium for both kinetic and equilibrium reaction models is equivalent. We compare the simulation results with the published experimental results for CH₄-hydrates and extend the assessment to a CO₂-hydrate formation experiment in a semi-batch, constant-pressure configuration. The model reproduces the main pressure–temperature transients and hydrate evolution for both CH₄- and CO₂-systems. We demonstrate applicability at core scale for hydrate formation and, at field scale, for gas production from CH₄-hydrates by thermal stimulation and depressurization. The interaction of thermal-compositional phenomena (phase changes, adiabatic expansion, kinetic rates, and reaction enthalpy) gives rise to highly nonlinear physics that an appropriate OBL discretization resolves. Overall, the patterns of hydrate formation and dissociation are highly sensitive to the kinetic-rate inputs; hence, the appropriate choice of the reaction model remains a key consideration from both physical and numerical perspectives.

Underground hydrogen storage in depleted gas fields is a potential solution for large-scale, seasonal storage of hydrogen, in support of the decarbonization of energy systems and other industrial activities. Its viability depends on the performance of the storage operations, which is influenced by the interaction between reservoir geology and operational strategies. However, general guidelines for development planning that account for geological uncertainty are still lacking. In addition, existing site screening criteria remain limited in that they do not account for how operational decisions can alter the suitability of a reservoir geology for hydrogen storage. Here, we employ a numerical model of flow and transport to evaluate a set of operational strategies in varying geological scenarios for depleted methane gas reservoirs of the Bunter Sandstone, an important formation in the North Sea. We investigate the following strategies for their impact on performance and interaction with geological features that are common in the Bunter sandstone: depletion level, injected hydrogen mass, cushion gas, well perforation, idle period, production rates, and methane reinjection. We found that depletion level, injected mass, and well perforation interact strongly with geology and are critical for site selection. The methane reinjection strategy provides pressure support that increases hydrogen production, though at the cost of purity in the long-term. Furthermore, cushion gas strategies show significant optimization potential but limited interaction with geology, whereas the duration of the idle period and target rates have low optimization potential. Based on these findings, we propose a site selection and development planning framework for underground hydrogen storage in depleted gas fields. The site selection phase introduces a novel screening criterion, the gravity–purity number, which integrates geological and operational considerations. The development phase provides criteria and guidelines for planning operational strategies, and establishes a hierarchy based on their optimization potential.

Phase equilibrium calculations play an important role in a wide variety of applications in chemical and petroleum engineering. In this work, we focus on CO₂-hydrocarbon mixtures, with applications ranging from enhanced oil recovery processes to CO₂ storage. In compositional reservoir simulation, both robustness and efficiency are of utmost importance. The conventional approach for multiphase equilibrium consists of a sequence of phase stability and flash calculations. At each level of the stepwise process, stability testing is performed starting from several initial guesses; therefore, reducing the number of stability calls and using judiciously the information from stability to initialize a phase split are key points in developing an efficient stability-flash algorithm. Two new initialization strategies for multiphase flash calculations are proposed. The first one (improved stepwise initialization) follows the conventional procedure, but uses additional initial guesses. In the second one (improved multiple initialization), a three-phase split is initiated if at least three minima of the tangent plane distance (TPD) function are detected by stability analysis of feed composition. Both proposed methods are using all information from phase stability testing at each stage. Unlike in previous formulations, compositions at all minima of the TPD function, including trivial and positive TPDs are used to generate initial equilibrium constants. Highly robust routines are used, based on successive substitution iterations (SSI) in early iteration stages, followed by Newton iterations with modified Cholesky factorization and line search, in both stability and flash calculations. The proposed methods are tested and compared with the conventional procedure for several benchmark mixtures from the literature, containing hydrocarbon components and CO₂. Phase diagrams are constructed in the P-Z plane, focusing on the number of stationary points of the TPD functions found in each step of the multiphase stability-flash algorithm and on how they must be efficiently used in initialization. For all the test mixtures, in the proposed stability-flash strategy, the number of calls of the stability and flash routines and the number of iterations in flash calculations are significantly reduced as compared to previous approaches, recommending the new approach as a useful tool in compositional simulation.

Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how machine learning methods can enhance data assimilation for GCS with a framework that integrates score-based diffusion models with machine learning-enhanced localization in channelized reservoirs during CO2 injection. We employ a machine learning-enhanced localization framework that uses large ensembles (Ns=5000) with permeabilities generated by the diffusion model and states computed by simple ML algorithms to improve covariance estimation for the Ensemble Smoother with Multiple Data Assimilation (ESMDA). We apply ML algorithms to a prior ensemble of channelized permeability fields, generated with the geostatistical model FLUVSIM. Our approach is applied on a CO2 injection scenario simulated using the Delft Advanced Research Terra Simulator (DARTS). Our ML-based localization maintains significantly more ensemble variance than when localization is not applied, while achieving comparable data-matching quality. This framework has practical implications for GCS projects, helping improve the reliability of uncertainty quantification for risk assessment.

The authors regret that during a post-publication review of the numerical model, we identified that rock thermal conductivity and rock heat capacity were not transferred properly for the ensemble run and were set to zero for the energy conservation equation. This resulted in earlier thermal breakthroughs because conductive heat transfer between the reservoir and the surrounding rock matrix was not accounted for. After correcting the model and rerunning the simulations, we found that the correction affects only the reported numerical values. The overall trends, relative comparisons between scenarios, interpretations, and scientific conclusions presented in the paper remain unchanged. The inclusion of rock thermal conductivity primarily delays thermal breakthrough and therefore we extend the simulation time from 30 years to 80 years in base case to capture long-term thermal behavior. The sensitivity analysis indicates that the ranking of parameter importance remains consistent, whereas the magnitude of the importance scores increase after introducing the rock thermal conductivity and rock heat capacity.