Rock Classification from Conventional Well Logs in Hydrocarbon-Bearing Shale
Bibliographic record
Abstract
Abstract This paper introduces a rock typing method for application in hydrocarbon-bearing shale (specifically source rock) reservoirs using conventional well logs and core data. Source rock reservoirs are known to be highly heterogeneous and often require new or specialized petrophysical techniques for accurate reservoir evaluation. In the past, petrophysical description of source rock reservoirs with well logs has been focused to quantifying rock composition and organic-matter concentration. These solutions often require many assumptions and ad-hoc correlations where the interpretation becomes a core matching exercise. Scale effects on measurements are typically neglected in core matching. Rock typing in hydrocarbon-bearing shale provides an alternative description by segmenting the reservoir into petrophysically-similar groups with k-means cluster analysis, which can then be used for ranking and detailed analysis of depth zones favorable for production. A synthetic example illustrates the rock typing method for an idealized sequence of beds penetrated by a vertical well. Results and analysis from the synthetic example show that rock types from inverted log properties correctly identify the most organic-rich sections better than rock types detected from well logs in thin beds. Also, estimated kerogen concentration is shown to be the most reliable property in an under-determined inversion solution. Field cases in the Barnett and Haynesville shale gas plays show the importance of core data for supplementing well logs and identifying correlations for desirable reservoir properties (kerogen/TOC concentration, fluid saturations, and porosity). Qualitative rock classes are formed and verified using inverted estimates of kerogen concentration as a rock-quality metric. Inverted log properties identify 40% more of a high-kerogen rock type over well-log based rock types in the Barnett formation. A case in the Haynesville formation suggests the possibility of identifying depositional environments as a result of rock attributes that produce distinct groupings from k-means cluster analysis with well logs. Core data and inversion results indicate homogeneity in the Haynesville formation case. However, the distributions of rock types show a 50% occurrence between two rock types over 90 ft vertical-extent of reservoir. Rock types suggest vertical distributions that exhibit similar rock attributes with characteristic properties (porosity, organic concentration and maturity, and gas saturation). The interpretation method considered in this paper does not directly quantify reservoir parameters and would not serve the purpose of quantifying gas-in-place. Rock typing in hydrocarbon-bearing shale with conventional well logs forms qualitative rock classes which can be used to calculate net-to-gross, validate conventional interpretation methods, perform well-to-well correlations, and establish facies distributions for integrated reservoir modeling in hydrocarbon-bearing shale.
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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".