Optimal Design and Uncertainty Assessment of CO2 WAG Operations: A Field Case Study
Bibliographic record
Abstract
Abstract While CO2 flooding is expected to increase oil recovery, deviations of actual production from predicted values add significant challenges when optimizing flood design under uncertain conditions. The aim of this paper is to introduce a comprehensive optimization process with uncertainty analysis to obtain a more plausible decision for a field application scenario. In this paper, a comprehensive optimization process is developed to optimize the production performance of entire production lifespan for a CO2-WAG EOR process in Pubei reservoir, Turpan-Hami Basin. Start times of the waterflooding and CO2 WAG proess (i.e., durations of the primary production and waterflooding) are also included in the optimization process as well as the producer's bottomhole pressures and injection rates, in addition to the water and gas injection rates for the WAG process, WAG ratio, and well bottomhole pressures at the producers. The comparison is then performed between the conventional WAG optimization processes with the comprehensive optimization process. A total of 80 reservoir realizations is generated and history-matched to consider the impacts of the geological uncertainty on the optimization process. Finally, the reliability of this optimization design is quantified under the geological uncertainty. Results from a deterministic comprehensive optimization design demonstrate that the oil recovery and NPV of the optimized CO2-WAG process are increased by 23.4% and 51.3%, respectively, in comparison to the optimal case obtained by the conventional WAG optimization process. After incorporating uncertainties into the geological model, the distributions of oil recovery and NPV, including P10, P50, P90 are quantified. Based on uncertainty assessment, it is found that the optimized CO2-WAG scheme is a reliable scheme for the reservoir development. This paper provides quantitative insights on the significance of both geological and operational factors on the reliability of optimal design over the entire life span of a CO2 WAG operation. It is expected that the integrated workflow will help operators to optimize well performance more efficiently and predict production performances with higher reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".