Carbon Capture, Utilisation & Storage in Offshore Facilities
Bibliographic record
Abstract
Abstract A group of technological procedures known as "carbon capture and storage" involve transferring and injecting carbon dioxide (CO2) that has been captured from industrial waste gases into geological formations. The acronym CCUS, or carbon capture, utilization, and storage, is a key technique for reducing emissions that can be used across the energy system. Meeting the fluctuating need for energy while reducing the harmful effects on the environment are two challenges faced by oil and gas production in floating, production, storage, and offloading (FPSO)units. The most crucial operational parameters to reduce energy demand, boost efficiency, and enhance production are identified in this study through the integration of screening analysis and thermodynamic analysis of oil and gas processing plants. Therefore, the primary objectives of this work are to determine how much the operational parameters of an FPSO with carbon capture, use, and storage (CCUS) contribute to the overall effect. The input parameters for the sensitivity analysis are chosen from some thermodynamic and structural design variables. To accomplish the objectives, four machine learning-based screening analysis algorithms—Linear Regression, Support Vector Machine (SVM), Analysis of Variance (ANOVA) and Gradient Boosting Machine (GBM) are used. The impact of three actual crude oil compositions with different GORs and CO2 contents is evaluated. The optimal operating pressure settings of CCS greatly minimize the energy consumption and exergy destruction of the main and utility plants, according to a combination of thermodynamic and screening evaluations. The findings also showed that while the total oil output is solely affected by the GOR content, the total power consumption, CCS efficiency, and CCS power consumption are all significantly more sensitive to the fluid reservoir's CO2 content than GOR. The impact of design variable interactions is also critical in affecting the separation efficiency and/or compression unit performance for situations with high CO2 or GOR content.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".