A Catalogue of Fiber Optics Strain-Rate Fracture Driven Interactions
Bibliographic record
Abstract
Abstract The downhole monitoring of strain using Fiber Optics (FO) can reveal unique information about the propagation and geometry of hydraulic fractures between nearby wells during stimulation and production. This work aims at creating a catalogue of commonly observed strain-rate signals captured in a not yet stimulated nearby observation well equipped with either a permanently or temporarily installed FO cable. This catalogue is the result of an informal collaboration between experience FO users from academia, service providers, consulting companies, and operators. In the creation of this first edition of a strain-rate catalogue, we considered two main types of stimulation categories (single and multi-entry) as well as the angle between the hydraulic fractures and the segment of the well where the strain-rate signals are observed (horizontal vs. vertical segments). In the catalogue we show a series of representative examples of two main types of far-field strain Fracture Driven Interactions (s-FDI) commonly encountered in frac diagnostics: 1. Vertical hydraulic fractures being monitored in a lateral portion of a horizontal well and 2. Vertical fractures being monitored in a vertical observation well. The catalogue is organized around commonly observed s-FDI motifs. Because interpretation of observed strain-rate signals can be subjective, when possible, we included observed examples with a brief description of our interpretation, as well as synthetic signals from geomechanical models of similar motifs. The strain-rate motifs were modeled based on first physical principles for rock deformation. These models serve to support the proposed interpretation of the observed signals. FO strain rate monitoring is changing our understanding about the hydraulics fracturing process. The information from FO strain is not available by other commonly used fracture diagnostic techniques. Strain- rate fractures driven interactions between wells occur in predictable patterns (Frac Domain and Stage Domain Corridors – FDC & SDC respectively) which are typically in line with the cluster spacing and stage length in the borehole being stimulated. Using FO strain monitoring, we now know that hydraulic fractures are larger than first anticipated, both in length and height. Many examples indicated that there is a direct correspondence between the near-field and far-field stimulation geometries. The lack of isolation due to cement quality and or plug failure manifests in the far-field geometries observed via FO strain-rate in nearby wells. The use of FO strain monitoring has also revealed that reopening of hydraulic fractures is common not only between prior and infill wells but also between wells from the same stimulation vintage. All these observations and conditions must be considered when interpreting new strain-rate datasets and more importantly when designing new hydraulic fracturing operations and considering different stimulation order (zipper schedule), as well as when making decisions about the vertical and lateral spacing of adjacent wells. The purpose of this industry-first edition strain-rate catalogue is to aid, new and experienced FO users, on the interpretation of strain-rate datasets. Ultimately, the accurate interpretation of FO strain data will not only help calibrate geomechanical and reservoir models but also directly influence where and how we complete unconventional wells. Nowadays, many s-FDI examples exist in scattered publications with formats that aren’t easily comparable for new users of the technology. In this project, we expand upon those publications to create an encompassing analysis with up-to-date interpretations where we have formalized the formatting of figures for better readability (color scheme, scales, etc.). What has resulted from this collaborative effort is a novel catalogue not available before in the FO published literature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.006 | 0.004 |
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".