A New Analytical Method for Analyzing Production Data from Shale Gas Reservoirs Exhibiting Linear Flow: Constant Rate Production
Bibliographic record
Abstract
Abstract Hydraulically-fractured vertical and horizontal wells completed in shale gas and some tight gas plays are known to exhibit long periods of linear flow. Recently, techniques for analyzing this flow period using (normalized) production data have been put forth, but there are known errors associated with the analysis. In this paper, linear flow from fractured wells completed in tight/shale gas reservoirs, subject to a constant production rate constraint, is studied. It is shown analytically that the square root-time plot, a plot of rate-normalized pressure versus square root of time which is commonly used to interpret linear flow, depends on the production rate. It is also shown that depending on production rate, the square root-time plot may not be a straight line during linear flow; the higher the production rate the earlier in time the plot deviates from the expected straight line. This deviation creates error in the analysis. To address this issue, a new analytical method is developed for analyzing linear flow data for the constant gas rate production constraint. The method is then validated using a number of numerically-simulated cases. As expected, based on the analytical derivation, the square root-time plots for these cases depend on gas production rate and for some cases it does not appear as a straight line during linear flow. Finally, it is found that there is excellent agreement between the fracture half-lengths obtained using this method and the input fracture half-lengths to numerical simulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".