Quick Evaluation of Source Rock Kerogen Kinetics Using Hydrocarbon Pyrograms from Regular Rock-Eval Analysis
Bibliographic record
Abstract
Source rock kinetics reflects kerogen reactivity that controls the onset and rate of hydrocarbon generation as well as the depth/temperature of oil and gas generation windows. Therefore, understanding source rock kinetics is critical to both quantitative resource modeling and identifying production “sweet spots”. The study of source rock kinetics requires special laboratory procedure and expertise, the cost of which limits research on specific source rock systems. For quantitative modeling of hydrocarbon generation, kinetic parameters are often adopted from an analogous source rock system available in published data sets or are automatically picked by basin modeling software based on kerogen type or depositional environment and facies. Recent studies ( Peters, K. E., Walters, C. C., and Mankiewicz, P. J. AAPG Bull. 2006, 90, 387−403) revealed that source rock kinetics may vary substantially, even for the same type of kerogen, because of compositional variation. Thus, source-rock-specific kerogen kinetics is more desirable for better characterization of the thermal transformational behavior. On the other hand, the requirements for information regarding the characteristics of source rock reactivity and hydrocarbon generation behavior are time-sensitive for supporting a business decision. Directly assessing reactivity and transformation behavior of source rock based on archived Rock-Eval data would allow for rapid and time-sensitive results to be obtained. This paper proposes a method that characterizes source rock kinetics using pyrograms of archived Rock-Eval analysis. Because the method uses existing Rock-Eval data directly, no new samples and laboratory experiments are required, thus providing a quick and cost-effective technique to determine simple kinetic parameters. The mathematical formulation of this numerical model is described herein, with applications showing the advantage and potential limitations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 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".