Evaluation of CO2 storage capacity and injectivity using a relief well in a saline aquifer in Pohang basin, offshore South Korea

2015 ◽  
Vol 20 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Joongseop Hwang ◽  
Soohyun Baek ◽  
Hyesoo Lee ◽  
Woodong Jung ◽  
Wonmo Sung
Keyword(s):  
South Korea ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Saline Aquifer ◽  
Pohang Basin

scholarly journals Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers

2012 ◽  
Author(s):  
Zheming Zhang ◽  
Ramesh Agarwal
Keyword(s):  
Numerical Simulation ◽  
Co2 Sequestration ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Injection Well ◽  
Co2 Injection ◽  
Great Promise ◽  
Saline Aquifer ◽  
Simulation And Optimization ◽  
Co2 Plume

With recent concerns on CO2 emissions from coal fired electricity generation plants; there has been major emphasis on the development of safe and economical Carbon Dioxide Capture and Sequestration (CCS) technology worldwide. Saline reservoirs are attractive geological sites for CO2 sequestration because of their huge capacity for sequestration. Over the last decade, numerical simulation codes have been developed in U.S, Europe and Japan to determine a priori the CO2 storage capacity of a saline aquifer and provide risk assessment with reasonable confidence before the actual deployment of CO2 sequestration can proceed with enormous investment. In U.S, TOUGH2 numerical simulator has been widely used for this purpose. However at present it does not have the capability to determine optimal parameters such as injection rate, injection pressure, injection depth for vertical and horizontal wells etc. for optimization of the CO2 storage capacity and for minimizing the leakage potential by confining the plume migration. This paper describes the development of a “Genetic Algorithm (GA)” based optimizer for TOUGH2 that can be used by the industry with good confidence to optimize the CO2 storage capacity in a saline aquifer of interest. This new code including the TOUGH2 and the GA optimizer is designated as “GATOUGH2”. It has been validated by conducting simulations of three widely used benchmark problems by the CCS researchers worldwide: (a) Study of CO2 plume evolution and leakage through an abandoned well, (b) Study of enhanced CH4 recovery in combination with CO2 storage in depleted gas reservoirs, and (c) Study of CO2 injection into a heterogeneous geological formation. Our results of these simulations are in excellent agreement with those of other researchers obtained with different codes. The validated code has been employed to optimize the proposed water-alternating-gas (WAG) injection scheme for (a) a vertical CO2 injection well and (b) a horizontal CO2 injection well, for optimizing the CO2 sequestration capacity of an aquifer. These optimized calculations are compared with the brute force nearly optimized results obtained by performing a large number of calculations. These comparisons demonstrate the significant efficiency and accuracy of GATOUGH2 as an optimizer for TOUGH2. This capability holds a great promise in studying a host of other problems in CO2 sequestration such as how to optimally accelerate the capillary trapping, accelerate the dissolution of CO2 in water or brine, and immobilize the CO2 plume.

scholarly journals Optimization of Relief Well Design Using Artificial Neural Network during Geological CO2 Storage in Pohang Basin, South Korea

2021 ◽  
Vol 11 (15) ◽  
pp. 6996
Author(s):  
Youngsoo Song ◽  
Jihoon Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
South Korea ◽  
Co2 Storage ◽  
Coefficient Of Determination ◽  
Co2 Injection ◽  
Ann Model ◽  
Well Design ◽  
Pohang Basin ◽  
Artificial Neural

This study aims at the development of an artificial neural network (ANN) model to optimize relief well design in Pohang Basin, South Korea. Relief well design in carbon capture and geological storage (CCS) requires complex processes and excessive iterative procedures to obtain optimal operating parameters, such as CO2 injection rate, water production rate, distance between the wells, and pressure at the wells. To generate training and testing datasets for ANN model development, optimization processes for a relief well with various injection scenarios were performed. Training and testing were conducted, where the best iteration and regression were considered based on the calculated coefficient of determination (R2) and root mean square error (RMSE) values. According to validation with a 20-year injection scenario, which was not included in the training datasets, the model showed great performance with R2 values of 0.96 or higher for all the output parameters. In addition, the RMSE values for the BHP and the trapping mechanisms were lower than 0.04. Moreover, the location of the relief well was reliably predicted with a distance difference of only 20.1 m. The ANN model can be robust tool to optimize relief well design without a time-consuming reservoir simulations.

Exploring pore water biogeochemical characteristics as environmental monitoring proxies for a CO2 storage project in Pohang Basin, South Korea

2018 ◽  
Vol 137 ◽  
pp. 331-338 ◽  
Author(s):  
Meilian Chen ◽  
Myongho Park ◽  
Ji-Hoon Kim ◽  
Young Jae Shinn ◽  
Yun Kyung Lee ◽  
...  
Keyword(s):  
South Korea ◽  
Environmental Monitoring ◽  
Pore Water ◽  
Co2 Storage ◽  
Pohang Basin

Revisiting the Utsira Saline Aquifer CO2 Storage Resources Using the SRMS Classification Framework

2019 ◽  
Author(s):  
Sylvain Thibeau ◽  
Lesley Seldon ◽  
Franco Masserano ◽  
Jacobo Canal Vila ◽  
Philip Ringrose
Keyword(s):  
Co2 Storage ◽  
Saline Aquifer ◽  
Classification Framework

scholarly journals Investigation of CO2 storage capacity in open saline aquifers with numerical models

2012 ◽  
Vol 31 ◽  
pp. 886-892 ◽  
Author(s):  
Yang Wang ◽  
Yaqin Xu ◽  
Keni Zhang
Keyword(s):  
Storage Capacity ◽  
Numerical Models ◽  
Co2 Storage ◽  
Saline Aquifers
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