Key Aspects of Project Design for Polymer Flooding at the Daqing Oilfield
Bibliographic record
Abstract
Summary This paper describes the design procedures that led to favorable incremental oil production and reduced water production during 12 years of successful polymer flooding in the Daqing oil field. Special emphasis is placed on some new design factors that were found to be important on the basis of extensive experience with polymer flooding. These factors include (1) recognizing when profile modification is needed before polymer injection and when zone isolation is of value during polymer injection, (2) establishing the optimum polymer formulations and injection rates, and (3) time-dependent variation of the molecular weight of the polymer used in the injected slugs. For some Daqing wells, oil recovery can be enhanced by 2 to 4% of original oil in place (OOIP) with profile modification before polymer injection. For some Daqing wells with significant permeability differential between layers and no crossflow, injecting polymer solutions separately into different layers improved flow profiles, reservoir sweep efficiency, and injection rates, and it reduced the water cut in production wells. Experience over time revealed that larger polymer-bank sizes are preferred. Bank sizes grew from 240–380 mg/L.PV during the initial pilots to 640 to 700 mg/L.PV in the most recent large-scale industrial sites [pore volume (PV)]. Economics and injectivity behavior can favor changing the polymer molecular weight and polymer concentration during the course of injecting the polymer slug. Polymers with molecular weights from 12 to 35 million Daltons were designed and supplied to meet the requirements for different reservoir geological conditions. The optimum polymer-injection volume varied around 0.7 PV, depending on the water cut in the different flooding units. The average polymer concentration was designed approximately 1000 mg/L, but for an individual injection station, it could be 2000 mg/L or more. At Daqing, the injection rates should be less than 0.14–0.20 PV/year, depending on well spacing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".