Successful Field Trial of a Novel, Reservoir-Triggered Polymer: Results, Interpretation and Simulation
Bibliographic record
Abstract
Abstract This paper describes the interpretation of a successful inter-well field trial of a novel reservoir-triggered polymer technology, making use of pressure transient analysis and numerical simulation. The polymer has been engineered to improve sweep in oil-bearing formations whilst reducing the impact of two of the key operational and economic challenges facing polymer enhanced oil recovery (EOR). The polymer employs a chemical strategy to render it resistant to shear during injection and in the high flux region at the sand face. In addition, the injection solution has a viscosity similar to that of water until triggered in the reservoir, which sustains injectivity. We demonstrate the use of laboratory kinetics, rheology data, high-resolution surveillance of the injector, and comprehensive analysis of produced fluids to constrain the simulation of the in-situ viscosification of this polymer. Numerical models using commercial and in-house R&D codes were calibrated to tracer effluent data, pressure fall-off tests, and injection pressures, to interpret the size and mobility of the polymer bank and its response to water injection. The field trial has qualified the polymer to be considered for deployment. A comprehensive surveillance programme and downhole sampling was used to successfully demonstrate that the polymer was protected from shear degradation upon injection and propagation, and it viscosified under flow at the designed location in the reservoir. Kinetic and rheology data from laboratory testing, combined with reservoir-scale simulations and field trial surveillance, enabled the reaction and adsorption characteristics of the polymer to be estimated. Simulations of the injection pressure demonstrate that this polymer has significantly better injectivity under matrix conditions than would be obtained with a conventional polymer of an equivalent deep-reservoir viscosity.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".