Production Optimization and Uncertainty Assessment in a CO2 Flooding Reservoir
Bibliographic record
Abstract
Abstract The main objective of modern reservoir management is to maximize the oil recovery when a displacing agent, such as CO2, is injected to displace the residual oil in a reservoir. Such process can be controlled properly by allocating the injected fluids to the injectors and adjusting the produced fluids from the producers. Inappropriate production-injection strategy leads to early breakthrough, unstable pressure distribution, and low ultimate oil recovery. Furthermore, presence of physical and/or financial uncertainties elevates the complexity of the field production optimization. In this paper, a pragmatic technique has been developed and successfully applied to determine the optimum production-injection strategy in a CO2 flooding reservoir by incorporating well performance into reservoir simulation in the presence of both physical and financial uncertainties. More specifically, well rates of the injectors and flowing bottomhole pressures of the producers are chosen as the controlling variables. Several variable candidates are first assessed, determined and finally assigned to each well based on the inflow performance curve, multiphase flow behavior in the wellbore, and voidage balance within the reservoir. An objective function associated with both the average net present value (NPV) and the range of NPV uncertainty is then defined, while a modified genetic algorithm is utilized as optimization engine to determine the optimum production-injection strategy. In addition, multiple equal-probable reservoir models are used to account for the physical uncertainty, while prices of oil and CO2 are applied to assess and quantify the financial uncertainty. Compared to the production-injection strategies without optimization, it is shown from a field case that the optimum strategy can postpone the CO2 breakthrough time by 1.5 years, decrease the water cut by 8.4%, and increase the oil recovery and net present value by 7.8% and 6.6%, respectively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".