Hydrothermal Dissolution of Deeply Buried Cambrian Dolomite Rocks and Porosity Generation: Integrated with Geological Studies and Reactive Transport Modeling in the Tarim Basin, China
Bibliographic record
Abstract
The burial dissolution of carbonate rocks has long been an interesting topic of reservoir geologists. Integrated with geological studies and reactive transport modeling, this study investigated the Cambrian dolomites that were buried at depths up to 8408 m and still preserved a large amount of unfilled dissolution vugs from the borehole TS1 in the northern Tarim Basin. Studies indicate that these vugs were formed in association with fault-channeled hydrothermal fluids from greater depth through “retrograde dissolution” as the fluid temperature dropped during upward migration. The reactive transport modeling results suggest an important control of the vertical permeability of wall-rock on fluid and temperature patterns which, in turn, would control the spatial distribution of dissolving-originated porosity. The hydrothermal dissolution mainly occurred in dolomite wall-rocks with higher vertical permeability (extensive development of tensional fractures and connected pore spaces), producing additional dissolved porosity there during deep burial. This study implicates the importance of multidisciplinary approaches for understanding the burial/hydrothermal dissolution of dolomite rocks and predicting favourable deep/ultradeep carbonate reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".