Development of a Probabilistic Correlation between the Gamma Ray Index and Shale Volume Factor to Improve Resource Estimation for the Niger Delta Basin, Nigeria
Bibliographic record
Abstract
The shale volume factor is among the critical petrophysical parameters for reservoir characterization and formation evaluation. Inaccurate estimates of the shale volume factor can lead to poor reserves or resource estimates and wrong business decisions. While the current industry standard is to estimate the shale volume factor from the gamma ray logs using the concept of the gamma ray index, a relationship between the shale volume factor and the gamma-ray index needs to be established for any region/basin under consideration. For most applications in the Niger Delta Basin, a linear relationship is often assumed. However, there is no proven relationship between the shale volume factor and the gamma-ray index for the formations in the Niger Delta Basin. This paper proposes a new shale volume factor prediction correlation for the Niger Delta Basin in Nigeria. The correlation development is based on establishing a relationship between the shale volume factor obtained from cores and the gamma ray index obtained from petrophysical logs for over thirty wells drilled across the Niger Delta Basin. The results show that the relationship between the shale volume factor and the gamma-ray index is not linear as often assumed but a power law model. The new probabilistic correlation predicts lower shale volume factors than the linear model for all ranges of the gamma-ray index. This recent correlation will significantly impact how the hydrocarbon resources and reserves are quantified in the Niger Delta 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".