INVESTIGATING WELL MANAGEMENT CONSIDERING INTEGRATION OF RESERVOIR AND PRODUCTION SYSTEM IN LIFE CYCLE RESERVOIR PERFORMANCE, ENERGY DEMAND, AND CARBON EMISSION
Bibliographic record
Abstract
This study aims to evaluate how well management impacts reservoir performance, energy demand, and carbon emissions considering integrated simulations in life cycle through long-term control rules and short-term optimization of reservoir with production system. The case study is a benchmark case representing a restricted offshore production platform with fluid treatment, water injection, and gas compression. The well controls are choke valves and variable gas lift injection rates. Unintegrated well management was compared in different scenarios for life cycle control rules and closed-loop cycle management using oil production optimization methods. For integrated management, results such as cumulative oil production and energy demand were similar to unintegrated (differences up to 5% and 1% respectively) for all life cycle scenarios. Considering gas processing capacity, particularly the gas rate required for gas lift, it was relevant to free up gas production capacity, increase oil recovery, and anticipate oil production, which is often simplified in non-integrated simulations. Short-term production optimization resulted in a greater oil anticipation (up to 10%), but lower recovery factor. The total carbon emission showed significant differences (up to 20%) due to the total gas compression, including gas lift. The results confirmed that assessment of gas processing capacity with integration that evolves artificial lift methods is important to forecast accurately production, energy consumption, and GHG emissions, especially in projects with restricted capacity. Implementing a more realistic oil optimization problem involving choke operating and gas injection proved to be quite complex and time-consuming in integrated simulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".