Use of Pressure/Rate Deconvolution To Estimate Connected Reservoir-Drainage Volume in Naturally Fractured Unconventional-Gas Reservoirs From Canadian Rockies Foothills
Bibliographic record
Abstract
Summary Case studies are presented in this paper to demonstrate the use of the pressure/rate deconvolution-approach in estimating drainage areas for wells completed in some of the naturally fractured tight gas reservoirs of the Canadian Rockies foothills. These case studies demonstrate the application of deconvolution to two key carbonate-stratigraphical horizons in the area: the Triassic Baldonnel and the Permo-Carboniferous Taylor Flat formations. In these structural plays with significant areal formation-rock heterogeneity, the matrix-rock properties controlling the gas storativity are low, with porosity between 3 and 6%, causing low matrix-rock permeability (from 0.01 and 0.1 md). However, all of these formations have been thrusted, overturned, and subjected to reverse faulting. These diagenetic factors have created swarms of natural fractures that control flow rates and may define rock volumes connected to individual wells. In each well, a preproduction flow test was performed with the intent of ensuring acceptable flow rates and scoping facility design. At this stage of early development, initial-gas-in-place (IGIP) estimates were derived mainly from geophysical mapping, with plans to calibrate the IGIP number through the application of gas material balance, rate-transient analysis, and/or simple late-time rate decline. The rate-history data available in the early stage of production were integrated with pressure-buildup (PBU) data collected later in the production life of the well during annual or routine shut-in periods that were relatively short. Application of deconvolution in this paper is aimed at detecting early signs of pseudosteady-state pool depletion and estimating connected drainage volume. The deconvolution procedures help calibrate and/or reconcile geosciences-defined volumetric resource sizes, map remaining reserves, and help identify possible infill-drilling opportunities.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".