Oil retention and intrasource migration in the organic-rich lacustrine Chang 7 shale of the Upper Triassic Yanchang Formation, Ordos Basin, central China
Bibliographic record
Abstract
ABSTRACT The Zhang 22 well was drilled in the Ordos Basin, penetrating the Chang 7 Member of the Triassic Yanchang Formation, which, cumulatively, has more than 80 m (>260 ft) of black organic-rich shale of oil window maturity. Using 76 samples collected every 1 m (3 ft) from the well, the effects of stratigraphic fractionation and petroleum expulsion within five intervals of the Chang 7 shale were qualitatively and quantitatively documented. The organic-rich intervals 1, 2, and 5, having an average total organic carbon (TOC) of 6.79 wt. % and pyrolyzable hydrocarbon potential of 9.40 mg/g rock (i.e., the amount of hydrocarbons generated by pyrolysis between 300°C and 650°C [S2]), are defined as “generative units” in the Chang 7 shale system, compared to the “in-source reservoirs” or “sweet spots” (i.e., the third and fourth intervals), which contain a lower average TOC of 4.19 wt. % and an average S2 value of 7.17 mg/g rock, but the highest amount of free oil (average total oil of 7.35 mg/g rock). Geochemical and molecular compositions display distinctive differences between samples from these source and reservoir groupings. For example, bitumens from the generative units proportionally possess lower saturated hydrocarbons (56%–66%) than those from the in-source reservoirs (up to 81%). The proportions of aromatic and polar compounds in the generative units are accordingly higher than in their counterpart. The individual molecular weight distribution of sample extracts displays more light-end moieties being enriched in the generative units. By applying the compositional mass balance calculation, the overall and compound-specific expulsion efficiencies in the in-source reservoirs are abnormally negative compared to the positive values in the generative intervals. This finding, in conjunction with the effects of the preferential retention of aliphatic hydrocarbons and the differential expulsion of light molecular weight compounds in the in-source reservoirs, indicates a short-distance intrasource migration of generated petroleum into the sweet-spot intervals (intervals 3 and 4) from the overlying units (intervals 1 and 2) and the underlying interval 5. Furthermore, when quantifying the total amount of retained petroleum in the shale system, an amended assessment has been introduced to overcome the systematic misestimations if only unextracted values for the amount of thermally extractable hydrocarbons volatilized at 300°C (mg HC/g rock) were considered. Thus, the oil crossover effect, temperature at which the rate of S2 generation is at maximum shift phenomenon, and the hydrogen index being shifted to higher values after extraction all account for identifying intervals 3 and 4 as the in-source reservoirs. In this study, we have not only identified a set of promising in-source targets for shale oil exploration and production, but we also presented the chemical and molecular composition for these shale oils. We have additionally speculated for the intrasource migration model and further discussed the different expulsion efficiencies in the shale system upon the compositional mass balance calculation as well as the stratigraphic fractionation on differentiating the chemical compositions during migration. The improved oil quality by fractionation, the extra storage potential derived from microfossil quartz, the weak adsorptive affinity of oil to organic matter, and the good shale susceptibility to hydraulic fracturing all give a promising prospective for exploring and producing shale oil from the Chang 7 shale system in the Ordos 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 | 0.002 | 0.001 |
| 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 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".