3D Printing of natural sandstone at pore scale and comparative analysis on micro-structure and single/two-phase flow properties

Energy and exergy assessment and a competitive study of a two-stage ORC for recovering SFGC waste heat and LNG cold energy

Phase change material microcapsules for smart temperature regulation of drilling fluids for gas hydrate reservoirs

Deformation characteristics of methane hydrate-bearing clayey and sandy sediments during depressurization dissociation

Numerical investigations on performance of sc-CO2 sequestration associated with the evolution of porosity and permeability in low permeable saline aquifers

Study on the multiphase heat and mass transfer mechanism in the dissociation of methane hydrate in reconstructed real-shape porous sediments

Numerical investigations of the PUGA geothermal reservoir with multistage hydraulic fractures and well patterns using fully coupled thermo-hydro-geomechanical modeling


 In the present work, fully coupled dynamic thermo-hydro-mechanical (THM) model was employed to investigate the advantage and disadvantages of supercritical CO2 (SCCO2) over water as geofluids. Low-temperature zone was found in both SCCO2-EGS and water-EGS systems, but spatial expansion is higher in water-EGS. Although, the spatial expansion of SCCO2 into the rock matrix will help in the geo-sequestration. The expansion of stress and strain invaded zones were identified significantly in the vicinity of fracture and injection well. SCCO2-EGS system is giving better thermal breakthrough and geothermal life conditions compared to the water-EGS system. Reservoir flow impedance (RFI) and heat power are examined, and heat power are high in the water-EGS system. Minimum RFI is found in the SCCO2-EGS system at 45°C and 0.05 m/s. Maximum heat power for SCCO2-EGS was observed at 35°C, 20 MPa, and 0.15 m/s. Therefore, the developed dynamic THM model is having greater abilities to examine behaviour of SCCO2-EGS and water-EGS systems effectively. The variations occur in the rock matrix and the performance indicators are dependent on the type of fluid, injection/production velocities, initial reservoir pressure, injection temperature. The advantages of SCCO2-EGS system over the water-EGS system, providing a promising result to the geothermal industry as geofluid.

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Comparison of supercritical co2 with water as geofluid in geothermal reservoirs with numerical investigation using fully coupled thermo-hydro-geomechanical model


 Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes at each potential storage site. The accurate prediction of the flow, geochemical, and geomechanical responses of the formation is essential for the management of GCS in long-term operations because excessive pressure buildup due to injection can potentially induce fracturing of the cap-rock, or activate pre-existing faults, through which fluid can leak. In this study, we build a Deep Learning (DL) workflow to effectively infer the storage potential of CO2 in deep saline aquifers. Specifically, a reservoir model is built to simulate the process of CO2 injection into deep saline aquifers, which considers the coupled phenomenon of flow and hydromechanics. Further, the reservoir model was sampled to account for a wide range of petro-physical, geological, and operational parameters. These samples generated a massive physics-informed simulation database (about 1500 simulated data points) that provides training data for the DL workflow. The ranges of varied parameters were obtained from an extensive literature survey. The DL workflow consists of Fourier Neural Operator (FNO) to take the input of the parameterized variables used in the simulation database and jointly predict the temporal-spatial responses of pressure and CO2 saturation plumes at different periods. Average Absolute Percentage Error (AAPE) and coefficient of determination (R2), Structural similarity index (SSIM), and Peak Signal to Noise Ratio (PSNR) are used as error metrics to evaluate the performance of the DL workflow. Through our blind testing experiments, the DL workflow offers predictions as accurate as our physics-based reservoir simulations, yet 300 times more efficient than the latter. The developed workflow shows superior performance with an AAPE of less than 5% and R2 score of more than 0.99 between actual and predicted values. The workflow can predict other required outputs that numerical simulators can typically calculate, such as solubility trapping, mineral trapping, and injected fluid densities in supercritical and aqueous phases. The proposed DL workflow is not only physics informed but also driven by inputs and outputs (data-driven) and thus offers a robust prediction of the carbon storage potential in deep saline aquifers with considering the coupled physics and potential fluid leakage risk.

Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO2 Injection into Deep Saline Aquifers


 Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are ubiquitous across the worldwide and are potentially very good source to store carbondioxide (CO2) for a longer period of time. The simulation models are great tool to assess the potential and understanding the physics behind CO2-brine interaction in subsurface reservoirs. Simulating the behavior of fluid flow in NFR reservoirs during CO2 are computationally expensive because of the multiple reasons such as highly-fractured and heterogeneous nature of the rock, fast propagation of CO2 plume in the fracture network, and high capillary contrast between matrix and fractures. This paper presents a data-driven deep learning surrogate modeling approach that can accurately and efficiently capture the temporal-spatial dynamics of CO2 saturation plumes during injection and post-injection monitoring periods of Geological Carbon Sequestration (GCS) operations in NFRs. We have built a physics-based numerical simulation model to simulate the process of CO2 injection in a naturally fractured deep saline aquifers. A standalone package was developed to couple the discrete fracture network in a fully compositional numerical simulation model. Then reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and operational parameters. The simulation model parameters were obtained from extensive geological surveys published in literature. These samples generated a massive physics-informed database (about 900 simulations) that provides sufficient training dataset for the Deep Learning surrogate models. Average Absolute Percentage Error (AAPE) and coefficient of determination (R2) were used as error metrics to evaluate the performance of the surrogate models. The developed workflow showed superior performance by giving AAPE less than 5% and R2 more than 0.95 between ground truth and predictions of the state variables. The proposed Deep Learning framework provides an innovative approach to track CO2 plume in a fractured carbonate reservoir and can be used as a quick assessment tool to evaluate the long term feasibility of CO2 movement in fractured carbonate medium.

Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

Shuyu Sun

Papers

Study on the multiphase heat and mass transfer mechanism in the dissociation of methane hydrate in reconstructed real-shape porous sediments

Numerical investigations of the PUGA geothermal reservoir with multistage hydraulic fractures and well patterns using fully coupled thermo-hydro-geomechanical modeling

Comparison of supercritical co2 with water as geofluid in geothermal reservoirs with numerical investigation using fully coupled thermo-hydro-geomechanical model

Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO2 Injection into Deep Saline Aquifers

Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach