Integration of Seismic Data and a Triple Porosity Model for Interpretation of Tight Gas Formations in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract A method is presented that integrates a triple porosity model with sonic, density and resistivity logs for evaluation of tight gas formations. The interpretation takes into account results from petrographic work in the Western Canada Sedimentary Basin (WCSB), which indicates that tight rocks are comprised of different types of pores including (i) intergranular, (ii) slot + microfractures, and (iii) isolated non-effective porosities. Seismic data are powerful in the exploration and production domains but a method that integrates seismic velocities and the observed triple porosity petrographic characteristics of tight gas formations is not available. This paper provides the theoretical foundation and development of equations for this integration along with examples using real data from tight gas formations in the WCSB. The proposed method provides estimates of inter-well formation resistivity, porosity and water saturation to obtain estimates of original gas in place. The comparison between resistivity from seismic velocities and resistivity from well logs is good with a strong statistical effectiveness. Under favorable conditions, the partition between effective and non-effective porosity might be estimated. The proposed methodology has significant potential for application in tight gas formations of the WCSB. The method can probably be extended to other regions around the world, which possess tight gas formations with similar characteristics to the ones described in this work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".