Use of Pressure-Rate Deconvolution to Estimate Connected Reservoir Drainage Volume in Naturally Fractured Unconventional Gas Reservoirs from Canadian Rockies Foothills
Bibliographic record
Abstract
Abstract A number of successful case studies are presented in this paper to demonstrate the use of the pressure-rate deconvolution approach to estimating drainage areas for wells completed in some of the naturally fractured tight gas reservoirs of the Canadian Rockies Foothills. This study covers the application of deconvolution to two key carbonate stratigraphical horizons in the area: the Triassic Baldonnel and the Permo-Carboniferous Taylor Flat formations. Our original application of pressure-rate deconvolution was to wells in a major Cretaceous-aged sandstone member, the Cadomin-Nikanassin horizon. This process and results of this original application wil be documented and presented in a separate publication (Jones and Chen, 2011). In these structural plays, the matrix rock properties controlling the gas storativity of these formations are low with porosity between 3% and 6%. Correspondingly matrix rock permeability is also low with values between 0.01 and 0.1 md. However, all of these formations have been thrusted, overturned and have been subjected to reverse faulting. These diagentic factors have created swarms of natural fractures which control flow rates and may define rock volumes connected to individual wells. In each well, a pre-production short flow test for each is performed with the intent of ensuring acceptable flow rates and to scope facility design. At this stage of early development, initial gas in-place (IGIP) estimates are derived from geophysical mapping mainly with plans to calibrate this IGIP number through the application of gas material balance, rate-transient analysis and/or simple late-time rate decline. In recent years, the pressure-rate deconvolution approach has been successfully applied to many of these reservoir pools. The rate history data is available in the early stage of production, which is integrated with pressure buildup data collected later in the production life of a well during annual or routine, relatively short shut-in periods. Application of deconvolution in this paper is aimed at detecting early signs of pseudo-steady state pool depletion and to estimate the connected drainage volume. The case studies presented here compare the deconvolution drainage volumes with the estimates from volumetric IGIP, gas material balance, and rate-transient analysis. The procedures help calibrate and/or reconcile geoscience defined volumetric resource sizes, map remaining reserves and possible infill-drilling opportunities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".