An NPV-Based Comparison of Performance of Subsurface Conformance Materials in EOR
Bibliographic record
Abstract
Abstract In water injection operations, oil is displaced from the heterogeneous porous media that increases recovery factors while providing pressure support. The most important parameter to be considered in such water flooding operations is the mobility ratio. For increasing the mobility ratio to maximize volumetric sweep of injectants, gelling agents (polymers) are added to the injected water. Extreme temperature and shear stresses can result in the degradation of these polymers, i.e., long chain macromolecules of the polymer are split into smaller chains. Therefore, it is of utmost importance to select the right type and amount of these viscosity reducing agents for each reservoir. In this paper, synthetic polymer mechanisms have been compared to a natural polymer (Xanthan), using a commercial full physics reservoir simulator. The assumption made in the simulation is simplified gel kinetics that forms a microgel without redox catalysis. The injection schedule is as follows; Continuous water injection over all 6 layers for first 450 days, gel system injection in the bottom two layers for the next 150 days and water injection is continued for 4 years. Reservoir model consists of a high permeability streak at the bottom of the reservoir while the top 4 layers have high horizontal permeabilities. The simulator is also coupled to an optimizer and an uncertainty analysis tool in which control and uncertainty variables are set to investigate the sensitivity under this process. Simulated model results show that the gel penetrates deep within the reservoir model and the high permeability bottom layers are blocked. The relative merits of synthentic ploymers to natural polymers are obtained from the sensitivity studies. These suggest that Biopolymers and xanthan polymers have better performance in terms of viscosity effects whereas resistance factor and in-situ gelation treatments are highlighted for synthetic PAM. Adsorption and retention of polymer and gel are permeability dependent. Given the potential for the application of polymers in reservoirs worldwide, this study compares and highlights the relative advantages of different treatments in terms of different parameters for the same model while showing the significance of each control and uncertainty variable. The economics of injecting different conformance enhancers have been analyzed as well in this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".