The Fracture-Compliance Method for Picking Closure Pressure From Diagnostic Fracture-Injection Tests
Bibliographic record
Abstract
Summary In this paper, we present the fracture-compliance method, a technique for estimating the closure pressure from diagnostic fracture-injection tests (DFITs). The method is based on the observation that fractures retain a finite aperture after asperities come into contact (mechanical closure). An empirical, nonlinear joint-closure law is used to relate the after-closure fracture aperture and stiffness (the reciprocal of compliance) to effective normal stress. Fracture closure increases fracture stiffness, which, in low-permeability formations, causes an increase in the pressure derivative. On the basis of these insights, we propose the fracture-compliance method, which consists of picking closure at the first point of deviation from linearity on a plot of pressure or G×dP/dG vs. G-time (after the end of the very-early-time transient associated with wellbore and near-wellbore friction and fracture tip-extension). The contribution of this paper is to provide theoretical justification for why closure is best picked with the fracture-compliance method, and not with other widely used methods. We provide a series of numerical DFIT simulations to demonstrate the sensitivity of the pressure transient to input parameters. Governing equations are derived and used to demonstrate the effect of changing fracture aperture after closure. A field DFIT data set is analyzed with the new method. Finally, a field example is presented in which downhole tiltmeter measurements provide an independent estimate of the minimum principal stress.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".