Case Study of Fracturing Technology to Thin Barrier in Daqing Oilfield
Bibliographic record
Abstract
Abstract This paper analyzes the difficulties met in reservoir fracturing in the tertiary infill campaign to recover oil from those thin, low porosity and low permeability sands in Daqing Oilfield. Vertically, these sands are characterized by thin barrier, long stridden interval between two adjacent target intervals. Due to the limitations of the barrier thickness must be longer than 2.0m and the stridden span must be shorter than 40.0m, the existing fracturing technique-separate layer fracturing cannot be adopted in these tertiary infill wells. If these thin, low porosity and low permeability sands could not be fractured, these tertiary infill wells would not produce oil economically. Based on the barrier channeling mechanism-studying results, the minimum limit of the barrier thickness is determined theoretically, 4 types of balance fracturing string have been developed, and 20 tertiary infill wells have been fractured successfully. Thus, an effective stimulation measure-balance fracturing has been developed to produce oil from those poor and thin sands in these tertiary infill wells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".