Early Warning Systems - Using PTA Analysis of DFITs to Understand Complex Hydraulic Fractures and Optimize Treatment Designs
Bibliographic record
Abstract
Abstract This paper outlines methods to characterize hydraulic fracture geometry and optimize full-scale treatments using knowledge gained from Diagnostic Fracture Injection Tests (DFITs) in settings where fracturing pressures are at or above the overburden gradient. Hydraulic fractures, whether created during a DFIT or a larger scale treatment, are usually represented by vertical plane fracture models. These models work well in a relatively normal stress regime with homogeneous rock fabric where fracturing pressure is less than the Overburden (OB) pressure. However, many hydraulic fracture treatments are pumped above the OB pressure. This high pressure may be caused by near well friction or tortuosity but may also be the result of more complex fractures in multiple planes. Bachman et al (2012, 2015), Hawkes et al (2018) and Nicholson et al (2019) advanced DFIT analysis by using the Pressure Transient Analysis (PTA) technique. This allows the identification of flow regimes useful for understanding fracture geometry and closure behavior beyond that available from more familiar G-function analysis techniques. In this paper DFITs from the Duvernay, Montney, Rock Creek and Cardium formations of Western Canada are analyzed using the PTA method. Particular attention is given to Early-Time Flow Regimes (ETFRs) present between the end of pump shut-down (End of Job Instantaneous Shut-In Pressure, EOJ ISIP) and the 3/2-slope Nolte flow regime. Identification of pressure gradients at the start and end of these flow regimes is of vital importance to the interpretation process. This allows the authors to build on case histories of DFIT-derived fracture geometry interpretations presented in Nicholson et al (2017, 2019). Procedures are proposed for picking Farfield Fracture Extension Pressure (FFEP) in place of conventional IIP estimates while distinguishing between storage, friction and tortuosity vs. fracture geometry indicators. Analysis of FFEP and ETFRs combined with the context of rock fabric and stress setting are useful for designing full-scale fracturing operations. A DFIT may help identify potentially problematic multi-plane fractures, predict high fracturing pressures or screen-outs. Fluid and completion system designs, well placement and orientation may be adjusted to mitigate some of these effects using the intelligence gained from the DFIT early warning system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".