H2, CO2, and CH4 Adsorption Potential of Kerogen as a Function of Pressure, Temperature, and Maturity.

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.

Review of Kerogen's Geomechanical Properties: Experiments and Molecular Simulation.

Measuring the mechanical properties of kerogen, the predominant constituent of organic matter in shale is exceedingly difficult as it constitutes small-scale aggregates interspersed in rocks. Kerogen is characterized by significantly lower stiffness compared to inorganic minerals, thereby the kerogen regions are potential areas for study during, for example, drilling or macroscopic fracture propagation in the course of hydraulic fracturing. For instance, the elastic modulus of kerogen-rich spots is around 10 GPa, while it is about 70 GPa for quartz. Failure of the kerogen nanocantilever beam shows an elastic strain-hardening behavior, indicating a higher energy requirement to propagate a crack. Studies illustrated that the kerogen's mechanical properties are controlled by maceral composition and are positively correlated to the maturity level. This paper provides a comprehensive review of how the mechanical properties of kerogen are elucidated experimentally and contrast the results with the properties delineated from molecular simulation. In addition, we relate kerogen innate attributes, such as maturity and type, to the physical qualities measured and substantiate why accurate knowledge of the mechanical characteristics is pivotal from a hydraulic fracturing perspective.

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters.

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.

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography.

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

The Effect of Clay Content on the Spin-Spin NMR Relaxation Time Measured in Porous Media.

Clays, hydrous aluminous phyllosilicates, have a significant impact on the interpretation of physical measurements and properties of porous media. In particular, the presence of paramagnetic and/or ferromagnetic ions like iron, nickel, and magnesium in clays can complicate the analysis of nuclear magnetic resonance (NMR) data for porous media characterization. This is due to the internal magnetic field gradient induced by the clay minerals. In this study, we aim to investigate the impact of clay content on spin-spin relaxation time (T 2), which is strongly influenced by the pore surface chemistry. Seven rock core plugs, characterized with variable clay content, were used for this purpose. The clay mineralogy and volume were determined by means of quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN). The T 2 relaxation time was measured using a Carr-Purcell-Meiboom-Gill (CPMG) sequence with variable echo spacing (T E). The maximum percentage difference in dominant T 2 values (MRDT 2) between shortest and longest echo spacing was subsequently correlated with clay content obtained from QEMSCAN. Our results show that the reduction in T 2 distribution with increasing echo time T E is more significant in samples characterized by higher clay contents. The MRDT 2 was found to be strongly correlated with clay content. An analytical equation is presented expressing MRDT 2 as a function of clay content providing a quick and non-destructive approach for clay content estimation. Moreover, the MRDT 2-clay content relationship showed a nonlinear behavior: MRDT 2 increases drastically as the clay content increases up to 15%, beyond which the rate of MRDT 2 change with clay content diminishes. This behavior could be attributed to the clay distribution. At higher clay contents (above 15%), it is more likely for clay to form clusters (structural clays), which will not significantly increase the clay surface in contact with the pore fluid. Further, experimental data suggests that ignoring the impact of clay on internal magnetic gradients and T 2 signal may result in considerable underestimation of the actual pore size distribution.

An experimental platform for triaxial high-pressure/high-temperature testing of rocks using computed tomography.

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.