MODEL-BASED PRODUCTION STRATEGY OPTIMIZATION FOR AN OFFSHORE HEAVY OIL RESERVOIR CONSIDERING POLYMER FLOODING AND INTELLIGENT WELLS
Bibliographic record
Abstract
Heavy oil reservoirs are known for their low recovery factors. Additional energy consumption, special operations, and enhanced oil recovery (EOR) techniques are required for production due to high viscosities. Also, unfavorable water-oil mobility ratio is a serious problem when waterflooding (WF) is implemented, usually causing early breakthrough and higher water cut. Developing and managing a production strategy through a comprehensive decision-making procedure is also complex due to the high number of variables, uncertainties, and physical phenomena involved. Polymer flooding (PF) is an EOR method that can be applied to heavy oil reservoirs to improve field performance by producing more oil and reducing water production. This improvement is achieved through the increase in water viscosity caused by the injection of polymers, thus reducing water-oil mobility ratio, and obtaining better oil displacement efficiency. In the case of intelligent wells (IW) equipped with Inflow Control Valves (ICVs), the WF limitations can be mitigated by controlling multiple production/injection zones, increasing oil production, and maintaining the reservoir pressure. This work aims to perform a nominal production strategy optimization to develop and manage a heavy oil reservoir considering PF as a production strategy (using conventional wells only) and comparing it to waterflooding with ICVs (WF+ICV) for the same case. A complete methodology to optimize the design and control variables is applied to the strategies by using model-based reservoir simulation. The objective function (OF) is the Net Present Value (NPV), this study case is named EPIC001, which has a 13° API heavy oil reservoir that represents part of a Brazilian offshore field. We have applied a specific methodology to optimize the PF strategy for a heavy oil reservoir of a nominal case which is practical and clear in the selection and comparison of strategies for similar cases. The results found PF strategy is the more suitable for the case, obtaining an NPV that is 21% higher than WF+ICV. Injecting polymers in the earlier stages of the life cycle at lower polymer concentration rendered PF with greater oil recovery (+13%) with a better efficiency in management of water and polymers, therefore surpassing the good ICV management from WF+ICV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".