Hydrocarbon source correlation and multiphase accumulation in the northern Kuqa Thrust Belt: Implications from geochemical fingerprinting and tectono-thermal evolution of the Dibei Field, NW China
Bibliographic record
Abstract
Abstract The Kuqa Depression, a prolific hydrocarbon province in China’s Tarim Basin, hosts a dual-source petroleum system with Jurassic coal-bearing and Triassic lacustrine strata. However, the origins and accumulation mechanisms of hydrocarbons in the Dibei Structural Belt remain contentious, particularly regarding contributions from Jurassic (J2kz, J1y) versus Triassic (T3h, T2-3k) source rocks, compounded by ambiguities in biomarker interpretations and uncalibrated thermal models. Advanced geochemical fingerprinting — including sterane distributions, gammacerane indices (GI; ratio of gammacerane to C30 hopane), and δ¹³C isotopes — was integrated with calibrated burial-thermal modeling and structural analysis, which allowed us to clarify source contributions, hydrocarbon charging history, and structural controls on accumulation. Geochemical results demonstrated that Yangxia Formation (Paleogene; a major regional seal and secondary reservoir unit within the Kuqa Depression) oils exhibit “inverted-L” sterane patterns (C27 < C29) and δ¹³C values of −26‰ to −23‰, confirming derivation from Jurassic coal measures. In contrast, Ahe Formation (Jurassic; a major regional sandstone reservoir unit within the Kuqa Depression) hydrocarbons displayed “V-shaped” sterane ratios (C27 > C29), δ¹³C values of −32‰ to −30‰, and elevated GI (0.21–0.32), indicative of Triassic lacustrine sources. Notably, the identification of Triassic-sourced hydrocarbons in deep Ahe reservoirs challenged previous Jurassic-centric models, resolving ambiguities through multiproxy integration. Burial-thermal modeling, constrained by fluid inclusion homogenization temperatures (80°C–160°C), revealed three charging phases: phase I (19–16 Ma, Jidike Fm.), phase II (16–12 Ma, Kangcun Fm.), and phase III (5–1 Ma, Kuqa Fm.), with phase III Himalayan tectonics critically reshaping paleo-accumulations into an inverted “gas-below-oil” stratification (phase reversal due to tectonic reorganization). Structural analysis revealed (1) two boundary-fault anticlinal traps (defined by opposed north- and south-dipping thrust faults) with vertical gas migration in Ahe sandstones and (2) fault-sealed tight gas accumulations controlled by reservoir quality. These findings highlighted the dominant contribution of Triassic sources to deep gas reservoirs in Dibei and underscored the importance of multiphase tectonic evolution and source–reservoir coupling. This study provided a predictive framework for hydrocarbon exploration in fold-and-thrust belts, advocating prioritized assessment of fault connectivity and Triassic saline lacustrine source deposits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".