Chemical flooding in low‐grade reservoirs of Daqing Oilfield: Newly developed chemical systems and successful practices
Bibliographic record
Abstract
Abstract Enhancing oil recovery (EOR) of low‐grade reservoirs through chemical flooding is of great significance for the efficient overall development of oilfields. However, the chemical‐agent systems of current chemical flooding techniques are basically designed for high‐grade reservoirs, without fully considering the compatibility between the flooding agents and the low‐grade reservoir properties. To address this notable knowledge gap, this work systematically analyzes the geological characteristics and pore structures of low‐grade reservoirs in the Daqing Oilfield. Based on the low‐grade reservoir properties, we innovatively designed the DS800 polymer and the alkali‐free compound system. The composition of DS800 can be described as a quadripolymer of acrylamide (AM), 2‐acrylamido‐2‐methylpropanesulphonic acid (AMPS), acrylic acid (AA), and a hydrophobic monomer. The low molecular weight of DS800 system ensures entry into low‐grade reservoirs with relatively low injection pressure. The rigid functional groups help inhibit molecular chain coiling in the highly tortuous pore spaces, thereby maintaining high bulk viscosity. The surfactant is composed of hexadecyl toluene sulphonate. Our experiments demonstrate that the newly developed chemical flooding systems can improve oil recovery by more than 10% compared to conventional compound systems in low‐grade reservoirs. The field trials in low‐grade reservoirs in the Daqing Oilfield showed that the composite flooding performance surpassed that of polymer flooding alone, with an additional recovery improvement of 3%–5% in the same reservoir. This study provides a newly designed surfactant–polymer system that is compatible for low‐grade reservoirs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| 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".