A coupled phase-field and reactive-transport framework for fracture propagation in poroelastic media

Clavijo, Santiago Pena; Addassi, Mouadh; Finkbeiner, Thomas; Hoteit, Hussein

doi:10.1038/s41598-022-22684-1

Cited by 12 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the free CO 2 phase at the CO 2 -brine interface will mix with the formation brine and slowly dissolve in it, leading to solubility trapping. Mineral trapping is another type of trapping mechanism in which chemical interactions between dissolved CO 2 and rock minerals will occur, resulting in mineral precipitation [3,22,23]. These physical and geochemical trapping mechanisms determine the effectiveness of the storage capacity and the fate of CO 2 migration [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Abdulwahab

Yan

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

Geological CO2 sequestration (GCS) has been proposed as an effective approach to mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate of CO2 dynamics and storage are essential aspects of large-scale reservoir simulations. This work presents a rigorous machine learning-assisted (ML) workflow for the uncertainty and global sensitivity analysis of CO2 storage prediction in deep saline aquifers. The proposed workflow comprises three main steps: The first step concerns dataset generation, in which we identify the uncertainty parameters impacting CO2 flow and transport and then determine their corresponding ranges and distributions. The training data samples are generated by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. The second step involves ML model development based on a data-driven ML model, which is generated to map the nonlinear relationship between the input parameters and corresponding output interests from the previous step. We show that using Bayesian optimization significantly accelerates the tuning process of hyper-parameters, which is vastly superior to a traditional trial–error analysis. In the third step, uncertainty and global sensitivity analysis are performed using Monte Carlo simulations applied to the optimized surrogate. This step is performed to explore the time-dependent uncertainty propagation of model outputs. The key uncertainty parameters are then identified by calculating the Sobol indices based on the global sensitivity analysis. The proposed workflow is accurate and efficient and could be readily implemented in field-scale CO2 sequestration in deep saline aquifers.

show abstract

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Abdulwahab

Yan

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…This feature is important for applications that involve strong coupling between different physical processes, such as WIPCR. In other fields, many studies utilized this approach to examine complex problems in the subsurface, such as studying carbon dioxide storage, reservoir simulation, − reactive transport, , and natural convection in porous media. − For instance, Benetatos and Giglio introduced a comprehensive workflow for full-scale reservoir simulation, including multiple data sources . However, GSA has not yet been applied to WIPCR.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Uncertainty Quantification Method for Reservoir Stimulation through Carbonate Acidizing

2022

Self Cite

View full text Add to dashboard Cite

Reservoir stimulation is a widely used technique in the oil and gas industry for increasing the productivity of hydrocarbon reservoirs, most notably in carbonate formations. This work aims to develop an optimization workflow under uncertainty for matrix acidizing. A reactive transport model is implemented in a finite-element framework to simulate the initiation and propagation of dissolution channels in porous carbonate rock. The model is verified using an analytical solution. We utilize surrogate modeling based on polynomial chaos expansion (PCE) and Sobol indices to identify the most significant parameters. We investigate the effect of varying 12 identified parameters on the efficiency of the stimulation process using dimensionless groups, including the Damkoḧler, Peclet, and acid capacity numbers. Furthermore, the surrogate model reproduces the physics-based results accurately, including the dissolution channels, the pore volume to breakthrough, and the effective permeability of the stimulated rock. The developed workflow assesses how uncertainties propagate to the model's response, where the surrogate model is used to calculate the univariate effect. The global sensitivity analysis shows that the acid capacity number is the most significant parameter for the pore volume to breakthrough with the highest Sobol index. The marginal effect calculated for the individual parameter confirms the results from Sobol indices. This work provides a systematic workflow for uncertainty analysis and optimization applied to the processes of rock stimulation. Characterizing the impact of uncertainty provides physical insights and a better understanding of the matrix acidizing process.

show abstract

“…This method introduces an already stable and non-buoyant phase into the reservoir, which allows the storage of CO 2 directly by solubility trapping [14][15][16][17][18] . Moreover, water-dissolved CO 2 can also promote the dissolution of divalent metal-bearing silicate minerals leading to the formation of carbonate minerals fixing the injected dissolved gas in the solid-state 17,[19][20][21][22] .The injection of CO 2 -charged water into the subsurface overcomes the slow dissolution of CO 2 into reservoir fluids if injected as a single phase. Although carbon dioxide dissolution into water is fast, and equilibrium is generally assumed for this reaction at the CO 2 -water interface [23][24][25] , in the subsurface the overall dissolution process is slow.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Assessing the potential of solubility trapping in unconfined aquifers for subsurface carbon storage

Addassi

Omar

Hoteit

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Carbon capture and storage projects need to be greatly accelerated to attenuate the rate and degree of global warming. Due to the large volume of carbon that will need to be stored, it is likely that the bulk of this storage will be in the subsurface via geologic storage. To be effective, subsurface carbon storage needs to limit the potential for CO2 leakage from the reservoir to a minimum. Water-dissolved CO2 injection can aid in this goal. Water-dissolved CO2 tends to be denser than CO2-free water, and its injection leads immediate solubility storage in the subsurface. To assess the feasibility and limits of water-dissolved CO2 injection coupled to subsurface solubility storage, a suite of geochemical modeling calculations based on the TOUGHREACT computer code were performed. The modelled system used in the calculations assumed the injection of 100,000 metric tons of water-dissolved CO2 annually for 100 years into a hydrostatically pressured unreactive porous rock, located at 800 to 2000 m below the surface without the presence of a caprock. This system is representative of an unconfined sedimentary aquifer. Most calculated scenarios suggest that the injection of CO2 charged water leads to the secure storage of injected CO2 so long as the water to CO2 ratio is no less than ~ 24 to 1. The identified exception is when the salinity of the original formation water substantially exceeds the salinity of the CO2-charged injection water. The results of this study indicate that unconfined aquifers, a generally overlooked potential carbon storage host, could provide for the subsurface storage of substantial quantities of CO2.

show abstract

A coupled phase-field and reactive-transport framework for fracture propagation in poroelastic media

Cited by 12 publications

References 67 publications

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Optimization and Uncertainty Quantification Method for Reservoir Stimulation through Carbonate Acidizing

Assessing the potential of solubility trapping in unconfined aquifers for subsurface carbon storage

Contact Info

Product

Resources

About