Current situation of carbon dioxide capture, storage, and enhanced oil recovery in the oil and gas industry
Bibliographic record
Abstract
Carbon capture and storage (CCS) is an important low‐carbon management technology used to reduce CO 2 emissions with the captured anthropogenic CO 2 for enhanced oil recovery (EOR). This paper provides an overview and analysis of current issues related to CCS projects and CCS technologies. The paper also assesses risks and costs as well as policy, legal, and regulatory frameworks relevant for CCS and the major countries with CCS deployment. Currently, the few CCS projects in operation are mostly for EOR purposes. However, miscible CO 2 ‐EOR in depleted oil and gas reservoirs appears to be the industry's first choice for CO 2 sequestration and increasing oil production. Potential CO 2 leakage is a major risk for pipelines and geological storage and a comprehensive monitoring program needs to be developed to ascertain its impact on pipeline material integrity, humans, and the environment. The cost of the CCS chain largely depends on the compression solvent for the synthesis gas or flue gas treatment for separation, heat rate, energy required for capture, capital costs of capture equipment, pipeline diameter, and flow capacity, and the homogeneity and permeability of the geological formations. An effective carbon pricing and cap‐and‐trade system as a part of national carbon policy is needed to achieve the goal of CCS. This paper finally discusses China's carbon capture utilization and storage (CCUS) systems and proposes a new CCUS‐LNG transportation process system for the coastal areas of China. Special attention was focused on CO 2 transportation, CCUS‐EOR, and a new CCUS‐LNG process system for China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".