Active CO2 Reservoir Management for CO2 Capture, Utilization, and Storage: An Approach to Improve CO2 Storage Capacity and to Reduce Risk

2012 ◽  
Author(s):  
Thomas A. Buscheck ◽  
Samuel Julius Friedmann ◽  
Yunwei Sun ◽  
Mingjie Chen ◽  
Yue Hao ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Reservoir Management ◽  
Reduce Risk ◽  
And Storage

scholarly journals Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

2016 ◽  
Vol 17 (3) ◽  
pp. 725-744 ◽  
Author(s):  
Kurt C. Solander ◽  
John T. Reager ◽  
Brian F. Thomas ◽  
Cédric H. David ◽  
James S. Famiglietti
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Storage Capacity ◽  
Model Performance ◽  
Reservoir Management ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Essential Components ◽  
Model Equations ◽  
And Storage

Abstract The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM.

scholarly journals Integrated static modelling and dynamic simulation framework for CO2 storage capacity in upper Qishn Clastics, S1A reservoir, Yemen

2021 ◽  
Author(s):  
Ayman Mutahar AlRassas ◽  
Hung Vo Thanh ◽  
Shaoran Ren ◽  
Renyuan Sun ◽  
Nam Le Nguyen Hai ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Large Scale ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Uncertainty Assessment ◽  
Alternative Measure ◽  
Modeling Framework ◽  
Significant Difference ◽  
Static Modelling ◽  
And Storage

Abstract Carbon dioxide (CO2) capture and storage (CCS) is presented as an alternative measure and promising approach to mitigate the large-scale anthropogenic CO2 emission into the atmosphere. In this context, CO2 sequestration into depleted oil reservoirs is a practical approach as it boosts the oil recovery and facilitates the permanent storing of CO2 into the candidate sites. However, the estimation of CO2 storage capacity in subsurfaces is a challenge to kick-start CCS worldwide. Thus, this paper proposes an integrated static and dynamic modeling framework to tackle the challenge of CO2 storage capacity in a clastic reservoir, S1A filed, Masila basin, Yemen. To achieve this work's ultimate goal, the geostatistical modeling was integrated with open-source code (MRST-CO2lab) for reducing the uncertainty assessment of CO2 storage capacity. Also, there is a significant difference between static and dynamic CO2 storage capacity. The static CO2 storage capacity varies from 4.54 to 81.98 million tons, while the dynamic CO2 simulation is estimated from 4.95 to 17.92 million tons. Based on the geological uncertainty assessment of three ranked realizations (P10, P50, P90), our work was found that the upper Qinshn sequence could store 15.64 Million tons without leakage. This result demonstrates that the potential of CO2 utilization is not only in this specific reservoir, but the further CO2 storage for the other clastics reservoirs is promising in the Masila Basin, Yemen.

scholarly journals Technical and Economic Evaluation of CO2 Capture and Reinjection Process in the CO2 EOR and Storage Project of Xinjiang Oilfield

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5076
Author(s):  
Liang Zhang ◽  
Songhe Geng ◽  
Linchao Yang ◽  
Yongmao Hao ◽  
Hongbin Yang ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Membrane Separation ◽  
Co2 Storage ◽  
Pilot Project ◽  
Gas Production ◽  
Co2 Emission Reduction ◽  
Production Scale ◽  
Co2 Eor ◽  
Pressure Swing ◽  
And Storage

CO2 capture and reinjection process (CCRP) can reduce the used CO2 amount and improve the CO2 storage efficiency in CO2 EOR projects. To select the best CCRP is an important aspect. Based on the involved equipment units of the CCRP, a novel techno-economic model of CCRP for produced gas in CO2 EOR and storage project was established. Five kinds of CO2 capture processes are covered, including the chemical absorption using amine solution (MDEA), pressure swing adsorption (PSA), low-temperature fractionation (LTF), membrane separation (MS), and direct reinjection mixed with purchased CO2 (DRM). The evaluation indicators of CCRP such as the cost, energy consumption, and CO2 capture efficiency and purity can be calculated. Taking the pilot project of CO2 EOR and storage in XinJiang oilfield China as an example, a sensitivity evaluation of CCRP was conducted based on the assumed gas production scale and the predicted yearly gas production. Finally, the DRM process was selected as the main CCRP associated with the PSA process as an assistant option. The established model of CCRP can be a useful tool to optimize the CO2 recycling process and assess the CO2 emission reduction performance of the CCUS project.

scholarly journals Quantifying Uncertainty through 3D Geological Modeling for Carbon Capture Utilization and Storage in the Unayzah Formation in Saudi Arabia

2021 ◽  
Author(s):  
Sofia Mantilla Salas ◽  
Miguel Corrales ◽  
Hussein Hoteit ◽  
Abdulkader Alafifi ◽  
Alexandros Tasianas
Keyword(s):  
Saudi Arabia ◽  
Carbon Capture ◽  
Large Scale ◽  
Storage Capacity ◽  
Co2 Storage ◽  
High Energy ◽  
Sequential Gaussian Simulation ◽  
Storage Site ◽  
The Impact ◽  
And Storage

<p>The development of Carbon Capture Utilization and Storage (CCUS) technology paired with existing energy systems will facilitate a successful transition to a carbon-neutral economy that offers efficient and sustainable energy. It will also enable the survival of multiple and vital economic sectors of high-energy industries that possess few other options to decarbonize. Nowadays, just about one-ten-thousandth of the global annual emissions are being captured and geologically-stored, and therefore with today’s emission panorama, CCS large-scale deployment is more pressing than ever. In this study, a 3D model that represents the key reservoir uncertainties for a CCUS pilot was constructed to investigate the feasibility of CO2 storage in the Unayzah Formation in Saudi Arabia. The study site covers the area of the city of Riyadh and the Hawtah and Nuayyim Trends, which contain one of the most prolific petroleum-producing systems in the country. The Unayzah reservoir is highly stratified and it is subdivided into three compartments: the Unayzah C (Ghazal Member), the Unayzah B (Jawb Member), and the Unayzah A (Wudayhi and Tinat Members). This formation was deposited under a variety of environments, such as glaciofluvial, fluvial, eolian, and coastal plain. Facies probability trend maps and well log data were used to generate a facies model that accounted for the architecture, facies distribution, and lateral and vertical heterogeneity of this high complexity reservoir. Porosity and predicted permeability logs were used with Sequential Gaussian Simulation and co-kriging methods to construct the porosity and permeability models. The static model was then used for CO2 injection simulation purposes to understand the impact of the flow conduits, barriers, and baffles in CO2 flow in all dimensions. Similarly, the CO2 simulations allowed us to better understand the CO2 entrapment process and to estimate a more realistic and reliable CO2 storage capacity of the Unayzah reservoir in the area. To test the robustness of the model predictions, geological uncertainty quantification and a sensitivity analysis were run. Parameters such as porosity, permeability, pay thickness, anisotropy, and connectivity were analyzed as well as how various combinations between them affected the CO2 storage capacity, injectivity, and containment. This approach could improve the storage efficiency of CO2 exceeding 60%. The analyzed reservoir was found to be a promising storage site. The proposed workflow and findings of the static and dynamic modeling described in this publication could serve as a guideline methodology to test the feasibility of the imminent upcoming pilots and facilitate the large-scale deployment of this very promising technology.</p>

The CO2 Capture Project Phase 4 - Storage Monitoring & Verification Program (CCP4-SMV) – Continuing Contributions to CO2 Storage Assurance and Economics

2018 ◽  
Author(s):  
Scott Imbus ◽  
Harvey Goodman ◽  
Tony Espie ◽  
Juan Anguiano ◽  
Mark Crombie ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Co2 Storage
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