Is water saturation a fracability indicator for organic‐rich, yet low‐clay content, tight carbonate source rock reservoirs?
Bibliographic record
Abstract
Abstract Hydraulically fracturing long horizontal wells is the key technology for economically producing hydrocarbon from unconventional reservoirs. A reservoir's fracability (the ease by which it can be hydraulically fractured) has often been used as an important parameter for identifying the sweet spots for production. Several fracability indicators, based on different types of rock properties, including mechanical, geochemical, and mineralogical properties, have been developed and used in industry. This study, based on observations from a source rock reservoir, proposes the use of reservoir water saturation as a new fracability indicator for organic‐rich tight carbonate source rocks that are not clay rich. The results from a machine learning model trained with the observations clearly show the strong and positive correlation between the linear flow parameter (that is obtained based on the newly proposed equivalent‐state approximation and characterizes the effectiveness of hydraulic fracturing) and the water saturation for oil wells, but not for gas wells. While further investigation is needed, the results may be due to the dual wettability of the carbonate source rock. Since minerals are more water‐wet than the organic matter, reservoir water tends to occupy pore spaces in the mineral matrix. Thus, water saturation reflects the relative portion of mineral matrix pore spaces. Given that the low‐clay content mineral matrix contains all the brittle components, the pore‐space development in the mineral matrix may have important implications for the fracability in the hydraulic fracturing process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".