Fracture Closure Stress: Reexamining Field and Laboratory Experiments of Fracture Closure Using Modern Interpretation Methodologies
Bibliographic record
Abstract
Abstract During the 1990s, field and laboratory experiments measured hydraulic fracture creation, propagation, and closure, and the archived data represent the finest collection of measurements that can be used to evaluate fracture models and fracture closure interpretation methodologies. None of the current fracture closure interpretation methods, including G-function derivative analysis, log-log storage diagnostics, and the changing-compliance method have been evaluated versus the field and laboratory measured data. Recent papers have proposed fracture closure pressure interpretations that differ from established methodologies, and under some circumstances, will result in a closure pressure that is higher than traditionally accepted. Thus, it seems an opportune time to reexamine the field and laboratory fracture closure data using interpretation methodologies developed over the last twenty years. Additional issues cloud closure pressure interpretations, including different definitions of fracture closure used in numerous publications, like mechanical fracture closure, hydraulic fracture closure, progressive fracture closure, and complete fracture closure. Evidence from downhole tiltmeters and finely-instrumented laboratory experiments of fracture propagation and closure all demonstrate that residual width is retained after closure. Consequently, closure is somewhat of a misnomer, and if a "closed" fracture remains open, the relationship between what we interpret as fracture closure and the minimum horizontal stress needs to be clearly defined based on measurements as opposed to simulation. Based on field tiltmeter deformation and pressure measurements in hard rock formations, we find that G-function derivative analysis and the log-log storage diagnostic plot interpretations together provide a fracture closure pressure that is consistent with the minimum horizontal stress identified using tiltmeter-measured rock deformation. Additionally, the closure pressure interpretations, and corresponding minimum horizontal stress, are invariant over multiple injection/falloff sequences of varying volume and time. Field experiments exhibiting variable-storage/changing-compliance signatures were also observed, and the changing-compliance method interpretations of fracture closure pressure are inconsistent with tiltmeter-measured rock deformation. Finally, we find the fracture re-opening pressure identified using tiltmeter deformation and the fracture closure pressure interpreted using pressure falloff data are essentially equal. Based on laboratory measurements of fracture closure and pressure, we find that G-function derivative analysis and the log-log storage diagnostic plot together provide a fracture closure interpretation generally consistent with measured fracture closure, but despite attempts to define an objective closure identification methodology, fracture closure signatures are often non-distinct and interpretations are subjective. In soft rock reservoirs, like unconsolidated sand, the fracture closure pressure interpretation does not correspond to the minimum horizontal stress, but in hard rock reservoirs, the fracture closure pressure identified using G-function derivative analysis and log-log storage diagnostic interpretations are approximately equal to the imposed minimum horizontal 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".