Field Application Results of Water Permeability Modifier in Fracture Stimulation Treatments in Western Siberia
Bibliographic record
Abstract
Abstract Historically, hydraulic-fracture stimulation of zone zones with directly underlying waters has not been considered a practical method for improved oil recovery. This was because the stimulation treatment could result in water cuts from 90-100%. Many of the oil and gas reservoirs in Western Siberia have production zones that fall into this scenario. The zones contain high-permeability streaks of mobile water and have water bearing layers above and/or below the producing zone. When a hydraulic-fracturing treatment is performed on such production zones, unintentional stimulation of the adjacent water bearing layers occurs. As a result, high water production is observed from the high permeability streaks of water or the adjacent water-bearing layers. Over a period of time, those wells will continue to lose hydrocarbon production rate, while the water cut continues to increase. Water separation and disposal of the produced water is expensive and not always successful. Using recent water permeability modifier (WPM) chemistry advances, the hydraulic-fracture stimulation of such zones has now been shown to be an effective way to stimulate and produce the mature oil and gas fields such as those found in Western Siberia. The incorporation of a water permeability modifier with a hydrophobic backbone containing hydrophilic branches with the hydraulic fracture stimulation has proven to be very effective in obtaining oil production above what was anticipated based upon comparison with offset wells fracture stimulated without the WPM. It was further observed that not only was the produced water rate typically less than previously observed, but that it continues to decline over time instead of increasing. This paper presents the results of the implementation and analysis of 11 WPM fracture-stimulation treatments that were performed in a field in Western Siberia in 2009. Discussed will be the WPM technology, treatment design, post production analysis, and recommendation selection of candidates and successful implementation of the WPM technology in the western Siberian fields.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".