Tea-Woo Kim

We introduce a novel framework for effective stochastic history matching in the characterization of channel reservoirs. The proposed framework utilizes both the generator and discriminator of generative adversarial network (GAN) to address issues encountered in reservoir characterization, particularly in conditions of high geological uncertainty. Specifically, it uses the generator to enrich an ensemble of reservoir models, increasing the likelihood of including realizations resembling true reservoir characteristics. The discriminator is adapted as a proxy model, predicting reservoir production responses with significantly reduced computational cost compared to traditional reservoir simulation. In this method, after training GAN using initial reservoir models, we generate additional realizations with the generator of the trained GAN. Subsequently, the discriminator is recycled as a proxy model and trained to predict production responses of the initial and generated reservoir models. The expanded ensemble is then calibrated using an ensemble-based history matching approach in conjunction with the discriminator-based proxy. Validation of this proposed framework was conducted across 2 channel reservoir scenarios, comprising a 2D homogeneous reservoir and the complex 3D heterogeneous Egg benchmark model. Results indicate that expanding the ensembles allows notable enhancements in the reliable representation of reservoir channel connectivity and dynamic production responses, especially in the complex 3D heterogeneous scenario. Furthermore, the computational efficiency of this approach yields considerable time savings of approximately 67.81% and 74.79% relative to the traditional method for the both cases, respectively. We also find that the discriminator-based proxy models effectively reduces the overfitting issue encountered with the proxy models trained from scratch.

Identifying reliable and scalable offshore CO2 storage sites requires systematic workflows that connect geological screening with project maturity assessment. In this study, we present a nine-stage evaluation framework that integrates Storage Readiness Levels (SRL), deterministic filtering, Common Risk Segment (CRS) analysis, semi-quantitative scoring, and milestone-based maturity tracking into a unified methodology. The approach is implemented in a modular Excel platform designed to ensure transparency, reproducibility, and applicability across variable data conditions. We applied this framework to major offshore basins surrounding the Korean Peninsula, including the Yellow Sea, South Sea, and East Sea. Initial screening (SRL1–2) identified four basins with adequate depth, sealing systems, and storage potential. CRS-based risk classification (SRL3) narrowed candidates to the Eastern Gunsan Basin (EGB) and the southwestern Ulleung Basin shelf (SWSUB). Quantitative scoring and structure-scale analysis (SRL4) further differentiated prospects within the SWSUB, where six Miocene prospects advanced to SRL5 due to robust seismic–well control and 3D geologic modeling. Among them, one depleted gas field demonstrates conditional SRL6 maturity supported by dynamic simulation results but requires additional geomechanical and monitoring design. The case study reveals significant variability in basin maturity and highlights key data gaps limiting site progression. More broadly, the SRL-based workflow provides a practical path for harmonizing site screening, risk evaluation, and readiness assessment—offering a transferable tool for offshore CCS planning in regions with heterogeneous datasets. This work demonstrates that structured readiness frameworks can accelerate identification of high-potential CO2 storage opportunities and improve communication with regulatory and industrial stakeholders.

A structured, stage‐gated evaluation framework for CO ₂ storage site assessment is presented as the storage readiness level (SRL)‐based evaluation framework (SBEF). The proposed framework provides an operational implementation that links the SRL maturity system to a multi‐criteria decision‐making workflow through deterministic screening, common risk segment (CRS) mapping, semiquantitative scoring, and stage‐based maturity tracking. The SBEF is implemented as a modular Excel‐based decision‐support tool that ensures transparency, auditability, and consistent application across evaluators and geological settings. The framework establishes a clear progression pathway from basin‐scale screening to operational readiness while maintaining compatibility with international classification systems, including the storage resource management system (SRMS). The methodology is applied to offshore South Korea to demonstrate its practical applicability. Of eight candidate basins, four satisfy the initial screening criteria. Among these, the eastern Gunsan Basin (EGB) and the southwestern shelf of the Ulleung Basin (SWSUB) advance beyond theoretical capacity assessment. The SWSUB exhibits the highest maturity, with six prospects meeting SBEF5 criteria and one depleted gas field conditionally reaching SBEF6, corresponding to contingent storage resources under SRMS. Sensitivity analysis indicates that uncertainties in key geological parameters have limited influence on the relative ranking of prospects, supporting framework robustness at the screening stage. The results demonstrate that the SBEF enables consistent, transparent, and decision‐oriented evaluation of CO ₂ storage sites by linking geological feasibility, risk assessment, and project maturity. The proposed framework provides a scalable, transferable methodology supporting reliable carbon capture and storage (CCS) site selection, portfolio prioritization, and strategic planning across diverse geological and regulatory contexts.

This study investigated integrated modeling and simulation of a geological CO 2 storage system in a depleted gas field. The integrated CO 2 transport-injection-storage system consists of a reservoir model and a facility model that includes an onshore CO 2 source, subsea pipelines, offshore platforms, and injection wells. The reservoir model characteristics, such as model size, grid system, faults, and permeability, were set based on literature on a depleted gas field in Australia. Facility specifications such as well locations and subsea pipeline trajectories were set based on literature on the operational plans of a CCS (Carbon Capture and Storage) project. Using the integrated model, we conducted a numerical simulation injecting 10 MtCO 2 per annum for 20 years. We also analyzed the cumulative CO 2 injection and pressure changes under the maximum allowable injection pressure conditions. The procedure of this study can be applied to integrated modeling of various geological CO 2 storage systems.

This study presents a front‐end engineering design (FEED) methodology for an integrated CO ₂ transport–injection–storage system, utilizing multiobjective optimization (MOO) and nodal analysis. The methodology’s performance is validated through a carbon capture and storage (CCS) demonstration project in the Gunsan Basin (GB), South Korea. This approach employs the dynamic inflow performance relationship (IPR)−outflow performance relationship (OPR) technique, applying it to the FEED of the CO ₂ transport–injection–storage system to enable CO ₂ injection into a saline aquifer via a single injection well connected through an onshore hub terminal and a subsea pipeline. By adjusting decision variables (CO ₂ discharge pressure at the onshore hub terminal, pipeline diameter, tubing diameter, and CO ₂ temperature at the wellhead), three objectives (CO ₂ storage capacity, safety, and economic benefit) are optimized through MOO, identifying the Pareto‐optimal front (POF) among objective functions. These trade‐off solutions provide reliable ranges for the four decision variables used in the nodal analysis, which considers real‐time pressure and temperature variations in the system during CO ₂ injection, along with the associated facility qualifications and operating conditions. This analysis determines the IPR−OPR at the bottom of the injection well and the corresponding pressure–flowrate, defining the practical FEED scope for the integrated CO ₂ transport–injection–storage system. By integrating optimal solutions from both MOO and nodal analysis, the study identifies the final nondominated solutions for efficient and stable CO ₂ geological storage. The proposed methodology offers decision‐makers robust scenarios for facility qualifications and operating conditions, considering CO ₂ storage capacity, safety, and economic efficiency at the FEED stage of a CCS demonstration project.