CO2 geological storage: Critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods

Zapata, Yuliana; Kristensen, Martin; Huerta, Nicolas; Brown, C. F.; Kabir, C.S.; Reza, Zulfiquar

doi:10.1016/j.jngse.2020.103542

Cited by 40 publications

(16 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dome geometry of the reservoir/storage site also acts as a trap for lateral migration of CO 2. 39 The reservoir is located at a depth of 2300 m and the liquid CO 2 used for injection at the wellhead turns into supercritical CO 2 in the reservoir. Variations in the depth of the gas‐water contact (GWC) and fluid pressure indicate that the reservoir is compartmentalised (see Fig.…”

Section: Reservoir Characterisationmentioning

confidence: 99%

Full‐scale simulation of a nearly depleted gas field in South Africa for carbon utilisation and storage

et al. 2022

View full text Add to dashboard Cite

Enhanced gas recovery (EGR) is a recognised CO2 utilisation technique that can reduce the cost of carbon storage and improve its economic feasibility. Although there are many studies on the potential of depleted gas fields for CO2 storage, there has not been a comprehensive full‐scale study that can analyse different aspects of carbon utilisation together with the potential of storage, from reservoir characterisation to modelling and economic evaluation. The aim of this study is to develop a benchmark for assessing the technical and economic feasibility of carbon utilisation and storage in abandoned and nearly depleted gas fields. A depleted gas field from the Bredasdorp Basin in South Africa was selected for CO2‐EGR and storage, given the availability of data and infrastructure. The reservoir of interest is divided into compartments, with this study focusing exclusively on compartment C1. The gas in place (Gi) in compartment C1 was 5.8E+08 MSCF, which was reduced to 3.33E+08 MSCF at the end of the primary production phase. The results obtained indicated that the gas reservoir is very heterogeneous, and a recovery of 20% can be achieved if the injection and production rates are 500 and 3000 MSCF/day, respectively. The amount of CO2 injected after 71 years was about 7.78E+07 MSCF. It was shown that the CO2 in this field can be injected for 71 years without causing problems with the integrity of the reservoir. The economic model was built assuming a project duration of 10 years. The model shows that the total cost of carbon capture, transport and storage is higher than the total revenue for 8 years of natural gas utilisation, which means that the amount of gas recovered from compartment C1 of the reservoir could not offset the cost of CCUS. It is recommended that additional data be collected for the C2 compartment, the carbon capture processes at the Mossel Bay GTL plant, and pipeline transportation to reduce the uncertainty of the proposed economic model. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

show abstract

Section: Reservoir Characterisationmentioning

confidence: 99%

Full‐scale simulation of a nearly depleted gas field in South Africa for carbon utilisation and storage

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The first step of developing any machine learning model is gathering the datasets. Here we collected 1509 simulation samples from reliable published literature (Ajayi et al, 2019;Alkhdheeawi et al, 2018;Al-Khdheeawi et al, 2018b, 2018a, 2018cAnchliya et al, 2012;Foroozesh et al, 2018;Hsieh et al, 2013;Jin et al, 2012;Jun et al, 2019;Khudaida and Das, 2020;Kim et al, 2019;Kumar et al, 2005;Lee et al, 2010;Liner et al, 2011;Liu et al, 2020;Mohajeri and Shariatipour, 2019;L Nghiem et al, 2009;Noushabadi et al, 2018;Pham et al, 2013;Sifuentes et al, 2009;Song et al, 2020;Sung et al, 2014;Vo Thanh et al, 2020b;Xiao et al, 2019;Zapata et al, 2020). The study area of collected data was conducted around the world, including Sleipner (Norway), Shenhua (China), Taiwan, Cuu Long Basin (Vietnam), Gorge V (Korea), Kansas (USA), Ketzin (Germany), Abu Dhabi, Israel, and UK.…”

Section: Data Preparationmentioning

confidence: 99%

“…Among these storage candidates, the saline aquifers demonstrated as the most potential candidate for CCS solution (Vo Thanh et al, 2019). CO2 storage in deep saline formations could be evaluated by conventional simulation or analytical methods (Aminu et al, 2017;Bachu, 2008;Zapata et al, 2020). Also, CO2 sequestration for field-scale simulation considered many data types such as geological background, petrophysical properties, and reservoir characterization (Song et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Thanh¹,

Lee²

2021

Preprint

View full text Add to dashboard Cite

Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

show abstract

“…The dissolved CO 2 chemically interacts with minerals present in the formation layer and undergoes mineral reactions to form secondary carbonate minerals. This geochemical transformation of CO 2 is called mineral trapping [18,19]. Among all the trapping mechanisms, solubility trapping plays a significant intermediate role in permanently disposing CO 2 over geological time.…”

Section: Introductionmentioning

confidence: 99%

Influence of Caprock Morphology on Solubility Trapping during CO2 Geological Sequestration

2022

View full text Add to dashboard Cite

Carbon capture and sequestration (CCS) technology is one of the indispensable alternatives to reduce carbon dioxide (CO2) emissions. In this technology, carbon capture and transport grid will send CO2 to the storage facilities that are using various storage techniques. Geologic carbon sequestration (GCS) is one such storage technique where CO2 is injected into a deep geological subsurface formation. The injected CO2 is permanently stored in the formation due to structural, residual, solubility, and mineral trapping phenomena. Among different trapping mechanisms, solubility trapping plays a significant role in the safe operation of GCS. In this work, the study is conducted to elucidate the influence of top surface caprock morphology on the solubility trapping mechanism. The simulation results show that the naturally available heterogeneous formations with anticline and without anticline structure influence the solubility fingering phenomena and solubility entrapment percentage over a geological time scale. The lateral migration and sweeping efficiency results of both the synthetic domains for the injected CO2 have shown the importance of caprock morphology on solubility trapping and selection of injection rate. Quantification of solubility trapping in two morphological structures revealed that the synthetic domain without anticline morphology had shown higher solubility trapping. In the future, the simulation data using Artificial Neural Networks can be applied to predict the structural and solubility trapping of geological formations. This analysis further helps incorporating the interaction of CO2 with porous media leading to a mineral trapping mechanism.

show abstract

CO2 geological storage: Critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods

Cited by 40 publications

References 57 publications

Full‐scale simulation of a nearly depleted gas field in South Africa for carbon utilisation and storage

Full‐scale simulation of a nearly depleted gas field in South Africa for carbon utilisation and storage

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Influence of Caprock Morphology on Solubility Trapping during CO2 Geological Sequestration

Contact Info

Product

Resources

About