Developing a New Technique for Calculating Accurate Water Saturations from Well Logs in Source Rocks
Bibliographic record
Abstract
Abstract Determining log-based water saturation using Archie's (1942) equation, or any derivative shaly sand method, requires correct inputs to produce valid results. In resource plays, the rock matrix is composed of water wet and oil wet constituents, therefore, correct values of Archie's cementation factor (m) and saturation exponent (n) are critical. In practice, it is pragmatic to use the Pickett plot (Pickett, 1973) to set connate water resistivity (Rw) and Archie's ‘m’. However, it is difficult, if not impossible, to derive Archie's ‘n’ parameter without additional information. Research combining core and log data shows evidence of a positive correlation between Archie's saturation exponent and the total organic content (TOC) in a given unit volume. Using this relationship, Archie's equation may be used to define a variable ‘n’. It is hypothesized that ‘n’ increases with increasing TOC volume as a result of an interruption of electrical pathways that resistivity tools exploit. This disruption results in an increase in the apparent value of ‘n’ required to compute correct water saturations. Due to the apparent excess resistivity in organic-rich rocks, an increase in ‘n’ values or kerogen corrected resistivity is needed to produce a fit to core-derived water saturations. This article will demonstrate the methodology used to derive a variable ‘n’ parameter and kerogen corrected resistivity in an organic-rich interval.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".