We performed molecular dynamics simulation to elucidate the adsorption behavior of hydrogen (H₂), carbon dioxide (CO₂), and methane (CH₄) on four sub-models of type II kerogens (organic matter) of varying thermal maturities over a wide range of pressures (2.75 to 20 MPa) and temperatures (323 to 423 K). The adsorption capacity was directly correlated with pressure but indirectly correlated with temperature, regardless of the kerogen or gas type. The maximum adsorption capacity was 10.6 mmol/g for the CO₂, 7.5 mmol/g for CH₄, and 3.7 mmol/g for the H₂ in overmature kerogen at 20 MPa and 323 K. In all kerogens, adsorption followed the trend CO₂ > CH₄ > H₂ attributed to the larger molecular size of CO₂, which increased its affinity toward the kerogen. In addition, the adsorption capacity was directly associated with maturity and carbon content. This behavior can be attributed to a specific functional group, i.e., H, O, N, or S, and an increase in the effective pore volume, as both are correlated with organic matter maturity, which is directly proportional to the adsorption capacity. With the increase in carbon content from 40% to 80%, the adsorption capacity increased from 2.4 to 3.0 mmol/g for H₂, 7.7 to 9.5 mmol/g for CO₂, and 4.7 to 6.3 mmol/g for CH₄ at 15 MPa and 323 K. With the increase in micropores, the porosity increased, and thus II-D offered the maximum adsorption capacity and the minimum II-A kerogen. For example, at a fixed pressure (20 MPa) and temperature (373 K), the CO₂ adsorption capacity for type II-A kerogen was 7.3 mmol/g, while type II-D adsorbed 8.9 mmol/g at the same conditions. Kerogen porosity and the respective adsorption capacities of all gases followed the order II-D > II-C > II-B > II-A, suggesting a direct correlation between the adsorption capacity and kerogen porosity. These findings thus serve as a preliminary dataset on the gas adsorption affinity of the organic-rich shale reservoirs and have potential implications for CO₂ and H₂ storage in organic-rich formations.

Summary: One method to access unconventional, heavy-oil resources is to apply in-situ combustion (ISC) to oxidize in place a small fraction of the hydrocarbon, thereby providing heat and pressure that enhances recovery. ISC is also attractive because it provides the opportunity to upgrade oil in situ by increasing the API gravity and decreasing, for instance, sulfur content. Despite a considerable literature on ISC dynamics, the propagation of a combustion front through porous media has never been visualized directly. We use X-ray computed tomography (CT) to monitor ISC movement, displacement-front shape, and thickness in a 1-m-long "combustion" tube. Temperature-profile history, liquid production, and effluent gas data are also obtained. Tests employ an 8.65°API heavy crude oil and representative sand. The general trend of saturation profiles is defined through spatially and temporally varying CT numbers. The role of initial oil and water saturations is examined by packing the combustion tube with either multiple samples with different saturations or by filling it with a uniform sample. Our work quantifies that ISC fronts display instabilities on a fine scale (cm). ISC reactions appear to add to front instability in comparison to inert gas advance. The pressure gradients during ISC appear to influence grain arrangement for loose packing. These grain arrangements cause combustion-front fingering, suggesting that the geomechanical state is relevant to combustion. These new data advance the knowledge base significantly by providing a data set for benchmarking of ISC simulations.

Optimal solvent performance is dependent upon the concentration of CO ₂ in a gas mixture.

A conventional high-pressure/high-temperature experimental apparatus for combined geomechanical and flow-through testing of rocks is not X-ray compatible. Additionally, current X-ray transparent systems for computed tomography (CT) of cm-sized samples are limited to design temperatures below 180 °C. We describe a novel, high-temperature (>400 °C), high-pressure (>2000 psi/>13.8 MPa confining, >10 000 psi/>68.9 MPa vertical load) triaxial core holder suitable for X-ray CT scanning. The new triaxial system permits time-lapse imaging to capture the role of effective stress on fluid distribution and porous medium mechanics. System capabilities are demonstrated using ultimate compressive strength (UCS) tests of Castlegate sandstone. In this case, flooding the porous medium with a radio-opaque gas such as krypton before and after the UCS test improves the discrimination of rock features such as fractures. The results of high-temperature tests are also presented. A Uintah Basin sample of immature oil shale is heated from room temperature to 459 °C under uniaxial compression. The sample contains kerogen that pyrolyzes as temperature rises, releasing hydrocarbons. Imaging reveals the formation of stress bands as well as the evolution and connectivity of the fracture network within the sample as a function of time.

A well production rate is an essential parameter in oil and gas field development. Traditional models have limitations for the well production rate estimation, e.g., numerical simulations are computation-expensive, and empirical models are based on oversimplified assumptions. An artificial neural network (ANN) is an artificial intelligence method commonly used in regression problems. This work aims to apply an ANN model to estimate the oil production rate (OPR), water oil ratio (WOR), and gas oil ratio (GOR). Specifically, data analysis was first performed to select the appropriate well operation parameters for OPR, WOR, and GOR. Different ANN hyperparameters (network, training function, and transfer function) were then evaluated to determine the optimal ANN setting. Transfer function groups were further analyzed to determine the best combination of transfer functions in the hidden layers. In addition, this study adopted the relative root mean square error with the statistical parameters from a stochastic point of view to select the optimal transfer functions. The optimal ANN model's average relative root mean square error reached 6.8% for OPR, 18.0% for WOR, and 1.98% for GOR, which indicated the effectiveness of the optimized ANN model for well production estimation. Furthermore, comparison with the empirical model and the inputs effect through a Monte Carlo simulation illustrated the strength and limitation of the ANN model.