Integrated Geological, Petrophysical and Geophysical Characterisation of a World Class Shale Gas Play, Horn River Basin, British Columbia, Canada
Bibliographic record
Abstract
Abstract The Devonian-aged strata of the Horn River Basin, located in northeast British Columbia, Canada, host a giant shale-gas play with an estimated OGIP of 14.2 trillion cubic metres (500 TCF). Ten distinct mudstone reservoir and non-reservoir lithofacies can be recognized in core, differentiated with a suite of wireline well logs and inverted from seismic data. Reservoir lithofacies are rich in total organic carbon (TOC) and the predominant pore type is organic micro-porosity. These lithofacies are predictable within a sequence stratigraphic framework which allows us to interpret the depositional processes, map the resulting depositional geobodies and thereby optimize the development plan. Motivation Lithofacies classifications associate sedimentary characteristics with depositional processes. In petroleum exploration and development, lithofacies classifications are typically tied to porosity and permeability - the two key parameters that control productive capacity. A powerful tool is obtained when the lithofacies classification is constructed within a predictive sequence stratigraphic framework that integrates geological and geophysical data. Although depositional processes and environments for fine-grained, organic-rich sedimentary rocks are not as well established as those for coarse siliciclastic or for carbonate systems, herein we show how we adapted a "conventional" workflow to allow such prediction in an "unconventional" shale hydrocarbon system.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".