Impact of Pre-Mesozoic Strike-Slip Faults on Dolomite Gas Reservoir in the Central Sichuan Basin and Its Exploration Potential
Bibliographic record
Abstract
Abstract: A large strike-slip fault system has been found in the central Sichuan Basin, although its effects on the pre-Mesozoic tight dolomite gas reservoirs in the deep (>4500 m) subsurface are uncertain. By integrating 3D seismic fault mapping, detailed fracture characterization, and well production data, this study demonstrates that strike-slip faults are extensively developed as vertically stratified arrays within the Ediacaran, Cambrian, and Permian dolomite intervals. These faults connect Lower Cambrian source rocks to multiple reservoir horizons, thereby establishing both lateral and vertical hydrocarbon migration pathways. A defining element of this system is the spatiotemporal coupling of “source-fault-reservoir,” which underpins the formation of a large-scale, pre-Mesozoic fault-controlled gas accumulation. Seismic evidence shows that many of these faults exhibit near-vertical geometries, en echelon arrangements, and step-over structures, all of which foster intense fracturing in the adjacent dolomites. Such fracturing substantially enhances porosity and permeability, yielding localized “sweet spots” with improved storage capacity and fluid flow properties, particularly within slope areas where structural conditions favor gas trapping. Production data strongly corroborate the geological and seismic observations, with wells that intersect or closely adjoin these fault zones typically exhibiting higher flow rates and more stable production profiles. This phenomenon highlights the pivotal role of fault-induced fractures in reservoir performance and underscores the need for detailed fault mapping and fracture network analysis in deep, tight carbonate plays. Furthermore, the recognition of this large-scale, strike-slip fault-controlled dolomite reservoir in a deep intracratonic setting underscores its considerable exploitation potential and points to broader implications for petroleum geology. Consequently, this study provides a robust framework for understanding the interplay between fault architecture and reservoir quality, offering valuable insights for guiding future exploration and development in analogous deep carbonate basins worldwide.Key words: Strike-slip fault; Deep tight dolomite reservoir; Strike-slip fault-related petroleum system; Migration and accumulation; Exploration; Sichuan Basin
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".