Application of After-Closure Analysis Techniques to Determine Permeability in Tight Formation Gas Reservoirs
Bibliographic record
Abstract
Abstract The primary objective of hydraulic fracturing is to create a propped fracture with sufficient conductivity and length to maximize or at least optimize well performance. In permeable reservoirs where transient flow is short lived, a fracture with a Dimensionless Fracture Capacity, FCD, of 2 is required to meet the design objective. In low permeability formations where transient flow can be extensive and where fracture fluid cleanup requires additional conductivity, an FCD in excess of 10 is desired. As a result, reservoir permeability becomes/is a key fracture design and analysis parameter. In higher permeability applications, permeability is determined simply, inexpensively, and routinely through conventional well testing techniques. Conventional well testing in tight formation gas reservoirs has not been proven as effective, can be expensive (cost of lengthy tests and production deferment), and is quite simply not routinely utilized. These reservoirs are often non productive without fracture stimulation and post fracture stimulation testing requires extensive shut-in time as the time to pseudo radial flow is proportional to the square of the fracture half-length. As a result, the development and routine use of any technique to determine permeability in these tight formation gas reservoirs has great value. In addition, without adequate well testing techniques and capabilities in tight gas reservoirs, the engineer is left with the use of log derived values of permeability which can often overstate in-situ permeability by factors of five to ten. Determination of in-situ permeability not only aids the well completion and stimulation but can be used to calibrate the log and core derived estimates of permeability improving performance predictions and field development. Prior papers have developed the use of After Closure Analysis techniques in permeable reservoirs, this paper will show the application of this technique to several tight gas formations in North America. This paper will demonstrate the following: The effective application of this technique in tight gas formations in the U.S. and Canada,Develop a cost effective and operationally simple means of collecting and analyzing the data,Compare and contrast the technique to other methods of determining permeability in tight formation gas reservoirs, such as impulse, Perforation Inflow Diagnostic (PID), Closed Chamber Drill-Stem Tests (CCDST), post-frac build-up, production decline analysis, Modular Dynamic Formation Tester (MDT).Show the application and value of calibrating log and core-derived permeability with in-situ measurements for improved well performance predictions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".