Geomechanics contribution to CO2 storage containment and trapping mechanisms in tight sandstone complexes: A case study on Mae Moh Basin.

Recognized as a not-an-option approach to mitigate the climate crisis, carbon dioxide capture and storage (CCS) has a potential as much as gigaton of CO2 to sequestrate permanently and securely. Recent attention has been paid to store highly concentrated point-source CO2 into saline formation, of which Thailand considers one onshore case in the north located in Lampang - the Mae Moh coal-fired power plant matched with its own coal mine of Mae Moh Basin. Despite a large basin and short transport route from the source, target sandstone reservoir buried at deeper than 1000 m is of tight nature and limited data, while question on storing possibility has thereafter risen. The current study is thus aimed to examine the influence of reservoir geomechanics on CO2 storage containment and trapping mechanisms, with co-contributions from geochemistry and reservoir heterogeneity, using reservoir simulator - CMG-GEM. With the injection rate designed for 30-year injection, reservoir pressure build-ups were ∼77 % of fracture pressure but increased to ∼80 % when geomechanics excluded. Such pressure responses imply that storage security is associated with the geomechanics. Dominated by viscous force, CO2 plume migrated more laterally while geomechanics clearly contributed to lesser migration due to reservoir rock strength constraint. Reservoir geomechanics contributed to less plume traveling into more constrained spaces while leakage was secured, highlighting a significant and neglected influence of geomechanical factor. Spatiotemporal development of CO2 plume also confirms the geomechanics-dominant storage containment. Reservoir geomechanics as attributed to its respective reservoir fluid pressure controls development of trapping mechanisms, especially into residual and solubility traps. More secured storage containment after the injection was found with higher pressure, while less development into solubility trap was observed with lower pressure. The findings reveal the possibility of CO2 storage in tight sandstone formations, where geomechanics govern greatly the plume migration and the development of trapping mechanisms.

Physics-informed deep learning for prediction of CO2 storage site response.

Accurate prediction of the CO2 plume migration and pressure is imperative for safe operation and economic management of carbon storage projects. Numerical reservoir simulations of CO2 flow could be used for this purpose allowing the operators and stakeholders to calculate the site response considering different operational scenarios and uncertainties in geological characterization. However, the computational toll of these high-fidelity simulations has motivated the recent development of data-driven models. Such models are less costly, but may overfit the data and produce predictions inconsistent with the underlying physical laws. Here, we propose a physics-informed deep learning method that uses deep neural networks but also incorporates flow equations to predict a carbon storage site response to CO2 injection. A 3D synthetic dataset is used to show the effectiveness of this modeling approach. The model approximates the temporal and spatial evolution of pressure and CO2 saturation and predicts water production rate over time (outputs), given the initial porosity, permeability and injection rate (inputs). First, we establish a baseline using data-driven deep learning models namely, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). To build a physics-informed model, the loss term is modified using the constraints defined by a simplified form of the governing partial differential equations (conservation of mass coupled with Darcy's law for a two-phase flow system). Our results indicate that incorporating the domain knowledge significantly improves the accuracy of predictions. The proposed modeling approach can be integrated in CO2 storage management to accurately predict the critical site response indicators for a range of relevant input parameters, even when limited training data is available.

Pore-scale characteristics of multiphase flow in heterogeneous porous media using the lattice Boltzmann method.

This study provides a pore-scale investigation of two-phase flow dynamics during primary drainage in a realistic heterogeneous rock sample. Using the lattice Boltzmann (LB) method, a series of three-dimensional (3D) immiscible displacement simulations are conducted and three typical flow patterns are identified and mapped on the capillary number (Ca)-viscosity ratio(M) phase diagram. We then investigate the effect of the viscosity ratio and capillary number on fluid saturation patterns and displacement stability in Tuscaloosa sandstone, which is taken from the Cranfield site. The dependence of the evolution of saturation, location of the displacement front, 3D displacement patterns and length of the center of mass of the invading fluid on the viscosity ratio and capillary number have been delineated. To gain a quantitative insight into the characteristics of the invasion morphology in 3D porous media, the fractal dimension Df of the non-wetting phase displacement patterns during drainage has been computed for various viscosity ratios and capillary numbers. The logarithmic dependence of Df on invading phase saturation appears to be the same for various capillary numbers and viscosity ratios and follows a universal relation.