The Ultimate Potential for Unconventional Gas in the Horn River Basin: Integrating Geological Mapping with Monte Carlo Simulations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Horn River Basin of northeastern British Columbia, Canada, contains natural gas in three Devonian shale units. Isopachs, depths, and net-to gross-pay ratios were determined from well logs for the Muskwa, Otter Park, and Evie Shales and then gridded. Pressure gradients were determined from well test and production data and then gridded into a single grid shared between shales. Because grid points were shared between each grid, volumetric and adsorbed gas equations could be integrated into each grid point. Static values or distributions could then be applied to equation variables and Monte Carlo simulations run to determine probabilistic gas in place (GIP) and marketable resources for each grid point, which were then summed for each shale. Grid points for the isopach and depth maps were treated as static values in the equations while net-to-gross and pressure gradient grid points became most likely values in Beta distributions where end points were assigned using regional low and high values. Most non-mapped variables in the equations were filled with Beta distributions based on typical values in the area and then applied across the basin without any local variations. On each distribution, whether based on mapped or unmapped variables, a second, overlying distribution was applied on a basin scale. This made entire iterations run a full range from pessimistic to optimistic. A few non-mapped variables in the equations were given static values. Recoverable gas resources were estimated by applying a recovery factor to free GIP estimates. Recoverable volumes from adsorbed GIP estimates were determined from a recovery factor applied to the portion of gas that would desorb during production as pressure decreased to the assumed well abandonment pressure. To determine marketable gas, gas impurities and fuel gas that would be used for processing and transport were estimated and subtracted from the recoverable estimates. Further, certain lower quality areas of the basin were excluded from the assessment, based on a low likelihood of being developed. The Horn River Basin shales are estimated to contain 10 466 109m3 (372 Tcf) to 14 894 109m3 (529 Tcf) of GIP with the expected outcome of 12 629 109m3 (448 Tcf). The marketable resource base is expected to be 1 715 109m3 (61 Tcf) to 2 714 109m3 (96 Tcf), with an expected outcome of 2 198 109m3 (78 Tcf).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it