Sweet Spot Mapping in the Montney Tight Gas Reservoir
Bibliographic record
Abstract
Abstract A common goal in unconventional plays is to create a sweet spot map by integrating all available data, including seismic data. This map could be utilized to optimize future drilling locations. Thus, in order to establish the workflow, we conducted a sweet spot mapping study in the Lower Triassic Montney tight gas play in the Western Canadian Sedimentary Basin, specifically focusing on prediction of lateral variations in condensate-gas ratio (CGR). A 3D geomodel was first created to obtain the 3D distribution of reservoir quality and completion quality properties which are expected to be potentially correlated with CGR. In the model, simultaneous AVO (Amplitude Variation with Offset) inversion results were fully utilized by geostatistically integrating with the well log data. Typical SRV (Stimulated Reservoir Volume) geometry in the study area was estimated from analysis using production data and microseismic data. For each producing well, average values for the reservoir quality and completion quality properties within the estimated SRV were obtained from the 3D geomodel to directly compare with the CGR value. Statistical analysis including crossplot and multiple-regression analysis was conducted to investigate the effectiveness of model properties as predictors of CGR. The analysis result implied that the reservoir depth and gas content are the most dominant properties for predicting lateral variations in CGR at seismic-scale. The reservoir depth is interpreted as a first-order control of thermal maturity and CGR. High gas content and low CGR is also observed in areas of higher porosity, which may correspond to secondary migration pathways for methane (Wood and Sanei, 2016); this is a second order control on gas content and CGR. Multiple-regression analysis was perfomed to obtaine a formula that explains CGR distribution by using the most effective combination of model properties. A CGR map was created by applying the established formula to the entire study area. The map of predicted CGR is consistent with the measured CGR. The map will be utilized for optimization of future drilling locations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".