A Hierarchical Framework for CO2 Storage Capacity in Deep Saline Aquifer Formations

Carbon dioxide (CO2) storage in deep saline aquifers is a vital option for CO2 mitigation at a large scale. Determining storage capacity is one of the crucial steps toward large-scale deployment of CO2 storage. Results of capacity assessments tend toward a consensus that sufficient resources are available in saline aquifers in many parts of the world. However, current CO2 capacity assessments involve significant inconsistencies and uncertainties caused by various technical assumptions, storage mechanisms considered, algorithms, and data types and resolutions. Furthermore, other constraint factors (such as techno-economic features, site suitability, risk, regulation, social-economic situation, and policies) significantly affect the storage capacity assessment results. Consequently, a consensus capacity classification system and assessment method should be capable of classifying the capacity type or even more related uncertainties. We present a hierarchical framework of CO2 capacity to define the capacity types based on the various factors, algorithms, and datasets. Finally, a review of onshore CO2 aquifer storage capacity assessments in China is presented as examples to illustrate the feasibility of the proposed hierarchical framework.

Economic Evaluation of CO2 Capture, Transportation, and Storage Potentials in Oklahoma

Oklahoma is known for having ample sources of CO2, pipelines and sinks where for many decades, oil and gas operators were injecting CO2 into geological formations for EOR purposes. We utilized SimCCS, an economic-engineering software tool (DOE-NETL), to integrate infrastructure related to CO2 sources, pipeline, and geological formations. The approved tax incentive program by IRS (45Q) has motivated many oil and gas operators to participate in reducing CO2 concentration and minimizing global warming effect by collecting CO2 from various sources, select the best pipeline route and the safest location to inject into geological formation for EOR purpose or deep saline aquifer for sequestration. This paper presents an economic evaluation of CO2 capture, utilization, storage (CCUS) into geological formation in the state of Oklahoma. Under this comprehensive approach, the process of capturing, transporting, and storing CO2 into geological or saline formations has been economically evaluated for different sites and routes utilizing an ad hoc simulation software (SimCCS) for integrated modeling of CCUS. The outcome of this paper determines the most optimal scenario using optimization algorithms embedded in SimCCS. This case study will mitigate the CO2 sequestration approval process when operator apply for tax credit under 45Q program. Our work will assist oil and gas operators by comparing different scenarios based on utilizing existing infrastructure, making decision in building new transportation system or new injection well to benefit the approved tax incentive program at its maximum capacity. Moreover, the outcome of this work will shed lights into future legislation demands (locally and nation-wide) to maintain CCUS momentum after its initial implementation phase is concluded.

Solute Driven Transient Convection in Layered Porous Media

CO2 geological sequestration has been proposed as a climate change mitigation strategy that can contribute towards meeting the Paris Agreement. A key process on which successful injection of CO2 into deep saline aquifer relies on is the dissolution of CO2 in brine. CO2 dissolution improves storage security and reduces risk of leakage by (i) removing the CO2 from a highly mobile fluid phase and (ii) triggering gravity-induced convective instability which accelerates the downward migration of dissolved CO2. Our understanding of CO2 density-driven convection in geologic media is limited. Studies on transient convective instability are mostly in homogeneous systems or in systems with heterogeneity in the form of random permeability distribution or dispersed impermeable barriers. However, layering which exist naturally in sedimentary geological formations has not received much research attention on transient convection. Therefore, we investigate the role of layering on the onset time of convective instability and on the flow pattern beyond the onset time during CO2 storage. We find that while layering has no significant effect on the onset time, it has an impact on the CO2 flux. Our findings suggest that detailed reservoir characterisation is required to forecast the ability of a formation to sequester CO2.

Numerical Study of CO2 Geological Storage in Saline Aquifers without the Risk of Leakage

The purpose of this study is to reduce the risk of leakage of CO2 geological storage by injecting the dissolved CO2 solution instead of the supercritical CO2 injection. The reservoir simulation method is used in this study to evaluate the contributions of the different trapping mechanisms, and the safety index method is used to evaluate the risk of CO2 leakage. The function of the dissolved CO2 solution injection is performed by a case study of a deep saline aquifer. Two scenarios are designed in this study: the traditional supercritical CO2 injection and the dissolved CO2 solution injection. The contributions of different trapping mechanisms, plume migrations, and the risk of leakage are evaluated and compared. The simulation results show that the risk of leakage via a natural pathway can be decreased by the approach of injecting dissolved CO2 solution instead of supercritical CO2. The amount of the CO2 retained by the safe trapping mechanisms in the dissolved CO2 solution injection scenario is greater than that in the supercritical CO2 scenario. The process of CO2 mineralization in the dissolved CO2 solution injection scenario is also much faster than that in the supercritical CO2 scenario. Changing the injection fluid from supercritical CO2 to a dissolved CO2 solution can significantly increase the safety of the CO2 geological storage. The risk of CO2 leakage from a reservoir can be eliminated because the injected CO2 can be trapped totally by safe trapping mechanisms.

Evaluation of CO2 Storage in a Shale Gas Reservoir Compared to a Deep Saline Aquifer in the Ordos Basin of China

As a new “sink” of CO2 permanent storage, the depleted shale reservoir is very promising compared to the deep saline aquifer. To provide a greater understanding of the benefits of CO2 storage in a shale reservoir, a comparative study is conducted by establishing the full-mechanism model, including the hydrodynamic trapping, adsorption trapping, residual trapping, solubility trapping as well as the mineral trapping corresponding to the typical shale and deep saline aquifer parameters from the Ordos basin in China. The results show that CO2 storage in the depleted shale reservoir has merits in storage safety by trapping more CO2 in stable immobile phase due to adsorption and having gentler and ephemeral pressure perturbation responding to CO2 injection. The effect of various CO2 injection schemes, namely the high-speed continuous injection, low-speed continuous injection, huff-n-puff injection and water alternative injection, on the phase transformation of CO2 in a shale reservoir and CO2-injection-induced perturbations in formation pressure are also examined. With the aim of increasing the fraction of immobile CO2 while maintaining a safe pressure-perturbation, it is shown that an intermittent injection procedure with multiple slugs of hug-n-puff injection can be employed and within the allowable range of pressure increase, and the CO2 injection rate can be maximized to increase the CO2 storage capacity and security in shale reservoir.

A machine learning based monitoring framework for CO2 storage

<p>With increasingly visible effects of climate changes and a growing awareness of the possible consequences, Carbon Capture and Storage (CCS) technologies are gaining momentum. Currently preparations are being done in Norway for a full-scale CCS project where CO<sub>2</sub> will be stored in a deep saline aquifer. A possible candidate for such storage is Smeaheia, located in the North Sea.</p><p>One of the main risks related to large scale storage projects is leakage of CO<sub>2</sub> out of the storage complex. It is important to design measurement, monitoring and verification (MMV) plans addressing leakage risk together with other risks related to conformance and containment verification. In general, geophysical monitoring represents a significant part of storage monitoring costs. Tailored and cost- effective geophysical monitoring programs that consider the trade-off between value and cost are therefore required. A risk-based approach can be adopted to plan the monitoring, but another more quantitative approach coming from decision analysis is that of value of information (VOI) analysis. In such an analysis one can define a decision problem and measure the value of information as the additional value obtained by purchasing information before making the decision.</p><p>In this work, we study the VOI of seismic data in a context of CO<sub>2</sub> storage decision making. Our goal is to evaluate when a seismic survey has the highest value when it comes to detecting a potential leakage of CO<sub>2</sub>, in a dynamic decision problem where we can either stop or continue the injection. We describe the proposed workflow and illustrate it through a constructed case study using a simplified Smeaheia model. We combine Monte Carlo and statistical regression techniques to estimate the VOI at different times. In a first stage, we define the decision problem. We then efficiently generate 10000 possible distributions of CO<sub>2</sub> saturation using a reduced order-based reservoir simulation tool. We consider both leaking and non-leaking scenarios and account for uncertainties in petrophysical properties (porosity and permeability distributions). From the simulated saturations of CO<sub>2</sub>, we derive distributions of geophysical properties and model the corresponding seismic data. We then regress those values on the reference seismic data, to estimate the VOI. We evaluate the use of two machine learning based regression techniques- the k-nearest neighbours' regression with principal components and convolutional neural network (CNN). Both results are compared. We observe that VOI estimates obtained using the k-nearest neighbours' regressions were consistently lower than the estimates obtained using the CNN. Through bootstrapping, we show that the k-nearest neighbours approach produced more stable VOI estimates when compared to the neural networks' method. We analyse possible reasons of the high variability observed with neural networks and suggest means to mitigate them.</p><p><strong>Acknowledgments</strong></p><p>This publication has been produced with support from the NCCS Centre (NFR project number 257579/E20).</p>

Seismic velocity change in sandstone during CO2 injection

This paper presents analytical and experimental studies of the effects of supercritical CO2 injection on the seismic velocity of sandstone initially saturated with saline water. The analytical model is based on poroelasticity theory, particularly the application of the Biot-Gassmann substitution theory in the modeling of the acoustic velocity of porous rocks containing two-phase immiscible fluids. The experimental study used a high pressure and high temperature triaxial cell to clarify the seismic response of samples of Berea sandstone to supercritical CO2 injection under deep saline aquifer conditions. Measured ultrasonic wave velocity changes during CO2 injection in the sandstone sample showed the effects of pore fluid distribution in the seismic velocity of porous rocks. CO2 injection was shown to decrease the P-wave velocity with increasing CO2 saturation whereas the S-wave velocity was almost constant. The results confirm that the Biot-Gassmann theory can be used to model the changes in the acoustic P-wave velocity of sandstone containing different mixtures of supercritical CO2 and saline water provided the distribution of the two fluids in the sandstone pore space is accounted for in the calculation of the pore fluid bulk modulus.