Densification mechanism of siltstone reservoirs: A case study of lower Triassic M Formation in Western Canada Sedimentary Basin
Bibliographic record
Abstract
The tight siltstone reservoirs of the lower Triassic M Formation in the Western Canadian Basin are rich in oil and gas resources， but it has a series of problems such as fine grain size， low porosity and permeability， complex sedimentary and diagenesis processes， large differences in rock fabric between layers， and strong vertical and horizontal heterogeneity， resulting in low accuracy in geological sweet spot prediction and difficulties in oil and gas exploration and development. To better understand the reservoir characteristics and achieve efficient development， research on the restoration of the reservoir densification process should be deepened. Based on the core description and thin section analysis， X-ray diffraction， scanning electron microscopy， and high-pressure mercury injection were comprehensively used to determine the reservoir pore development characteristics and diagenetic types of the three main layers of the lower Triassic M Formation in the study area. On this basis， the evolution process of diagenesis-reservoir formation was further clarified， and the causes of reservoir densification were divided. The results show that the pores in the tight siltstone reservoirs of M Formation are dominated by residual intergranular pores and dissolution pores， and the pore throat radius is concentrated between 10 and 150 nm， which belongs to tight siltstones with ultra-low porosity and ultra-low permeability. The diagenesis types are dominated by compaction， cementation， and metasomatism. The present diagenesis falls between the middle stage A and middle stage B. The dissolution is relatively developed in the LE layer. Due to the influence of sedimentary environments， the processes of porosity variation in the three main layers differ greatly. LC layer is dominated by compaction-based porosity reduction， supplemented by carbonate cementation-based porosity reduction. In the LE layer， carbonate cementation is the main factor of porosity reduction， and dissolution has improved the reservoir properties. LG layer has a higher shale content， and early compaction is the primary mechanism for porosity reduction. Based on the diagenesis and porosity variation， the causes of densification in the LC， LE， and LG layers are classified as primary mixed origin， secondary cementation origin， and primary fine-grained origin， respectively.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".