Successful Applications of Pressure-Rate Deconvolution in the Cad-Nik Tight Gas Formations of the British Columbia Foothills
Bibliographic record
Abstract
Summary The Cadomin-Nikanassin (Cad-Nik) sandstone formations in the Lower Cretaceous reservoirs along the reverse-thrust faulting belt of northeastern British Columbia (NEBC), Canada, have emerged in recent years as a new tight gas play. The low porosity (3?6%) of the rock matrix controls gas storativity, while the presence of natural fractures in the form of clusters or swarms allows significant and sustainable flow rates for commercial production. Newly drilled wells are commonly hydraulically fractured to establish or enhance wellbore connectivity to the natural-fracture network. Seismic mappings of these structural unconventional-gas reservoirs provide the early assessments of resource sizes and initial gas in place (IGIP), which usually bear large uncertainties because of the difficulty in determining reservoir structural closures and pay-porosity cutoffs. Regional analogue wells are often used to guide development decisions. Meanwhile, estimating connected reservoir volumes through conventional-gas material balances (p/z vs. cumulative production) and production-data analysis [rate-transient analysis (RTA)] has not been without challenges. Fairly long pressure buildups (PBUs), on the order of hundreds of hours, are often performed without seeing the pressure stabilization required to estimate accurately the reservoir pressure needed for material-balance calculations. The applicability of pressure extrapolation to these tests has not been systematically investigated; therefore, no reliable methods for using shorter shut-ins to estimate reservoir pressure currently exist. Thus, reliable average reservoir-pressure estimates require significantly longer well shut-in times in order to perform meaningful gas material balance. Because this is not practical, confidence in material-balance results requires a second, independent method for establishing connected well volumes to be used in comparisons and cross checking. One possible choice is RTA, but, in these fields, numerous times wellhead-pressure data are also unavailable or unreliable. This paper presents two field-case studies that demonstrate the successful application of the pressure/rate-deconvolution approach, combining a well?s long, high-quality production-rate history with accurate downhole-pressure data from relatively short buildup tests. This approach allows the reservoir engineer to (1) reconcile the performance-based estimated-ultimate-recovery estimates with the volumetric IGIPs; (2) establish, at the least, minimum well-drainage size and connected volume; and (3) select possible infill-drilling opportunities. A final benefit is that this often leads to a better understanding of well/reservoir parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".