Advancing PetroChina’s Development Strategies for Low-Permeability Oil Reservoirs
Bibliographic record
Abstract
Based on PetroChina’s status and situation of low-permeability oil reservoir development, this paper analyzes the key common issues in the production capacity construction of new oilfields, the stable production of old oilfields, and enhanced oil recovery, and, in connection with the progress made in major development technologies and the results of major development tests for low-permeability oil reservoirs in recent years, puts forward the technical countermeasures and development directions. For optimizing the development of low-grade reserves, a comprehensive life-cycle development plan is essential, alongside experimenting with gas injection and energy supplementation in new fields. Addressing challenges in reservoir classification, multidisciplinary sweet spot prediction, and displacement–imbibition processes can significantly boost well productivity. In fine water flooding reservoirs, the focus should shift to resolving key technological challenges like dynamic heterogeneity characterization, and functional and nano-intelligent water flooding. For EOR, accelerating the application of carbon capture, utilization, and storage (CCUS) advancements, along with air injection thermal miscible flooding, and middle-phase microemulsion flooding, is crucial. This approach aims to substantially enhance recovery and establish a new model of integrated secondary and tertiary recovery methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".