USE OF CONTROL VARIABLES TO IMPROVE THE PERFORMANCE OF WATER AND POLYMER FLOODING STRATEGIES
Bibliographic record
Abstract
In oil industry, decisions related to field development take into consideration scenarios that involve many uncertainties and high investments. Thus, a comprehensive decision analysis process is necessary to select the production strategy that maximizes field performance considering these uncertainties. For the selection of a production strategy, two main groups of optimization variables may be considered: design and control variables. The design variables relate to the development of the field, and cannot be altered after the implementation of the strategy (e.g. capacity of the platform, number and position of wells). On the contrary, control variables relate to the management of the field, and can be altered daily by the companies (e.g. production and injection rates). However, even with a robust production strategy selection process, unexpected or undesirable events can occur and decrease the economic efficiency of the project. The objective of this work is to evaluate the use of control variables when undesirable events occur after the implementation of a production strategy, to improve the economic performance of the project. Two production strategies are used: one that uses only water flooding and other one prepared for polymer flooding. The simulation model is based on a heterogeneous heavy oil offshore field. Two approaches (undesirable events) are considered in this work: (1) polymer degradation and (2) a geologic model that is different than the expected one. The results show that the economic performance can be improved greatly by simply adjusting the control variables for the described situations. Moreover, the gain in the polymer flooding case is higher than for water flooding, because of the higher number of optimization variables, such as polymer solution concentration and slug size, giving more flexibility to this kind of project.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".