Investigation of Fine-Scaled Reservoir Heterogeneity within the Sulphur Mountain Formation
Bibliographic record
Abstract
This study integrates two perspectives to comprehensively assess the geological and reservoir characteristics of the Sulphur Mountain Formation within the Western Canadian Sedimentary Basin, an outcrop analog of the Lower Triassic Montney Formation, one of Canada's largest unconventional plays. The unconventional resource plays' reservoir quality and production optimization relies on a nuanced understanding of geomechanical rock properties and their influence on fractures and fracture networks in the subsurface. Challenges in studying subsurface fractures, particularly the lateral constraints of petrophysical and core data, have prompted the utilization of outcrop analogs to enhance understanding. This research examines the rock fabric control on natural fractures in the Montney Formation's outcrop-equivalent, employing an up to 36 m thick, well-exposed outcrop of the Sulphur Mountain Formation. Unmanned aerial vehicle (UAV) based photomosaics are utilized to characterize the outcrop, evaluating natural fracture distribution. A novel methodology utilizing high-resolution scanned core images is introduced to quantify lamination and assess sedimentary fabric. Results indicate a discernible relationship between sedimentary fabric and fracture characteristics, shedding light on the geometry of fracture networks and aiding predictions of fracture behavior in fine-grained rocks in the subsurface. Additionally, this study investigates fine-grained reservoir heterogeneity, a critical factor for hydraulic fracturing operations, influenced by parameters such as clay content, lamination, and bedding planes. Hyperspectral imaging emerges as a valuable non-destructive analytical technique, effectively measuring composition without damaging the sample. Its application overcomes limitations of geophysical well logs in heterogeneous lithologies, playing a crucial role in defining heterogeneity. This research further identifies Hyperspectral core scanning as a valuable tool for characterizing fine-scale facies variations and underscores the challenge of constructing an accurate compositional model when the training data has a low variance. To enhance model correlation, the study suggests optimizing the data variance to measurement error range ratio. X-ray diffraction (XRD) is proposed as a tool to increase data variance, addressing the heightened standard deviation observed in the investigation. This integrated project emphasizes the pivotal role of hyperspectral imaging in enhancing core analysis and provides a holistic perspective on reservoir heterogeneity in unconventional plays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".