Use of Pickett Plots for Evaluation of Shale Gas Formations
Bibliographic record
Abstract
Abstract A practical method, based on the pattern recognition approach used in Pickett plots, is presented for preliminary yet accurate quantitative evaluation of shale gas formations. The method is inspired by a quick-evaluation, time-tested methodology developed by Passey et al. (1990) for shales, which utilizes primarily sonic and resistivity logs. In Passey et al. method the sonic and resistivity logs are overlain in such a way that the curves track each other in fine-grained non-source rocks. Separation of the curves indicates the presence of organic-rich intervals. The Pickett method presented in this study reproduces data published by Passey et al. with coefficients of determination (R2) greater than 0.99 for cases related to sandstones, limestones, dolomites and shales. As Pickett plots have been used thousands of times in the past for evaluation of the first 3 mentioned lithologies, this paper concentrates primarily on the evaluation of shale gas reservoirs. The advantage of the proposed Pickett plot for shale gas formations is that it allows quick estimates of water saturation, total organic carbon, and under favorable conditions, estimates of fracture intensity and diffusion. The objective of the proposed approximate approach, however, is not to replace detailed petrophysical and thermal maturity studies but to provide quick and accurate evaluations of shale gas formations. It is concluded that Pickett plots provide a powerful practical tool for quick evaluation of shale gas formations. Examples of applications and comparisons with previously published interpretations are presented in detail.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| 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) | 0.004 | 0.001 |
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".