Quick Evaluation of Present‐Day Low‐Total Organic Carbon Carbonate Source Rocks from Rock‐Eval Data: Middle–Upper Ordovician in the Tabei Uplift, Tarim Basin
Bibliographic record
Abstract
Abstract Previous studies have postulated the contribution of present‐day low‐total organic carbon (TOC) marine carbonate source rocks to oil accumulations in the Tabei Uplift, Tarim Basin, China. However, not all present‐day low‐TOC carbonates have generated and expelled hydrocarbons; therefore, to distinguish the source rocks that have already expelled sufficient hydrocarbons from those not expelled hydrocarbons, is crucial in source rock evaluation and resource assessment in the Tabei Uplift. Mass balance can be used to identify modern low‐TOC carbonates resulting from hydrocarbon expulsion. However, the process is quite complicated, requiring many parameters and coefficients and thus also a massive data source. In this paper, we provide a quick and cost effective method for identifying carbonate source rock with present‐day low TOC, using widely available Rock‐Eval data. First, we identify present‐day low‐TOC carbonate source rocks in typical wells according to the mass balance approach. Second, we build an optimal model to evaluate source rocks from the analysis of the rocks' characteristics and their influencing factors, reported as positive or negative values of a dimensionless index of Rock‐Eval data (IR). Positive IR corresponds to those samples which have expelled hydrocarbons. The optimal model optimizes complicated calculations and simulation processes; thus it could be widely applicable and competitive in the evaluation of present‐day low TOC carbonates. By applying the model to the Rock‐Eval dataset of the Tabei Uplift, we identify present‐day low‐TOC carbonate source rocks and primarily evaluate the contribution equivalent of 11.87×109 t oil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".