Field Application of Evaluation Model for Development Effect on Horizontal Waterflooding in Thin Low-permeability Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Low-permeability carbonate reservoirs with layered characteristics generally exist in Middle East district. The reservoirs achieved low recovery efficiency using vertical well by depletion mode in the early stage, and low horizontal and vertical sweep efficiency by vertical waterflooding due to high permeability streak and interlayer. Some oilfields in the district has successively implemented horizontal waterflooding technology and achieved good development results. Feasible and effective report has not been provided yet on the problem that the range of recoverable reserves and recovery efficiency can be increased by horizontal waterflooding. Considering some oilfields accomplished horizontal waterflooding in quick succession and some oilfields were just developed by horizontal waterflooding, practical resolution should be recommended such as the corresponding evaluation model for enhancing recovery efficiency range of the technology. The paper put forward an evaluation model for enhancing recovery reserves and recovery efficiency of horizontal waterflooding based on production decline model and waterflooding curve model. The resolution can also provide an evaluation basis of enhancing recovery efficiency for optimization and adjustment of horizontal water flooding. Applications of these concepts are illustrated with D oilfield in Oman. Results indicated that recovery efficiency by horizontal waterflooding after natural depletion development may be increased around 25% and about 4% by the optimization and adjustment process. The evaluation model will be used effectively to predict IOR effect of the similar reservoirs in Middle East oilfields.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".