Questions Over Permeability Prompt Producer-Led Group To Rebuild Key Fracture Diagnostic
Bibliographic record
Abstract
For the past 20 years, the diagnostic fracture injection test (DFIT) has been used across the frontlines of the shale revolution to paint a picture of what cannot be seen. However, that picture has not always been so clear in the eyes of subsurface engineers. It is not the test has been questioned. It is the way a DFIT is analyzed. Last year, a joint-industry project supported by six shale producers formed to develop a new methodology that aims to bring fractures into sharper focus—especially when it comes to obtaining accurate permeability estimates from within shale gas formations. The operators who funded and shared field data for the project were ConocoPhillips, Hess, Shell, Equinor, Range Resources, and Apache. The other key participants included oilfield services firm Keane Group (recently renamed NextTier Oilfield Solutions) and a startup called ResFrac whose software was used to run the study’s models and numerical simulations. The group’s existence reflects both the rising value of pressure data in unconventional engineering and the struggles industry has in trusting it. The outcome of this yearlong study though hopes to improve upon the latter. The group has offered to the unconventional sector a new template for DFIT analysis, one that represents a distinct alternative to the classical methods used by most practitioners today. “We have a slightly different conceptual approach, and that impacts a lot of details. So it was necessary to basically start from scratch and make a new procedure,” said Mark McClure, a principal researcher in the industry study and the chief executive of ResFrac. The new procedure is the centerpiece of two technical papers recently shared at the Unconventional Resources Technology Conference (URTeC) in Denver, and the SPE’s Annual Technology Conference and Exhibition (ATCE) in•Calgary. Usually performed at the toe-end of a horizontal well, a DFIT involves pumping a small volume of fluid (typically 20•bbl or less) into the formation to induce a small fracture. How that fracture seals back up is then measured over a period that usually spans a 1–3 weeks. The test is used widely across shale plays to obtain estimates of rock stress, pore pressure, and, perhaps most importantly, permeability. It has become ubiquitous in the shale sector because it is an affordable way to quantify such properties before a pressure pumping crew arrives on a well pad. Additionally, where rock cores are susceptible to stress and permeability changes as they are brought to surface, the DFIT represents an in situ measurement that avoids these issues. However, the operator-led study asserts that the shale sector has misinterpreted the signals provided by a DFIT. The reason: accepted interpretation methods were largely designed for conventional reservoirs that feature far simpler physics than those that dominate tight-rock formations. The result: a systematic inflation of permeability estimates in gas shales and, to a lesser extent, inaccuracies in permeability estimates of oil shales.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".