Bibliographic record
Abstract
Abstract The Montney Formation in Alberta and British Columbia presents many challenges for core analysis. Its high saline and sub-irreducible formation water, organic matter content and type, nanometer sized pores, and friability from laminations prevent core analysis from being dependable. Despite this, Routine Core Analysis (RCA) is still commonly performed without modifying the techniques to address these challenges. Over the past five years, RCA has been conducted and examined by the author on over thirty Montney cores. Each step of RCA has been assessed to identify and quantify sources of error and uncertainty. The Montney Formation has been found to be very sensitive to errors that are associated with the limitations of RCA methods. Excluding standard errors, the combination of inappropriate analysis types, ineffective RCA processes, and RCA method limitations, can lead to additional absolute porosity errors that range from −0.8% to +0.6%. The presence of unobservable fractures has been found to incorrectly increase permeability up to three orders of magnitude. The low porosity and permeability of the Montney Formation magnify the relative errors which significantly affect the accuracy stated by American Petroleum Institute Recommended Practice 40 (API RP 40) (1998). Modifications to the RCA process have been made to minimize errors and uncertainties. The techniques discussed were developed to improve accuracy on the Montney Formation, but most are applicable to any rock types that are suitable for RCA.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".