Can You Feel the Strain? DAS Strain Fronts for Fracture Geometry in the BC Montney, Groundbirch
Bibliographic record
Abstract
Abstract The use of Distributed Acoustic Sensing for Strain Fronts (DAS-SF) is gaining popularity as one of the tools to help characterize the geometries of hydraulic fracs and to assess the far-field efficiencies of stimulation operations in Unconventional Reservoirs. These strain fronts are caused by deformation of the rock during hydraulic fracture stimulation (HFS) which produces a characteristic strain signature measurable by interrogating a glass fiber in wells instrumented with a fiber optic (FO) cable cemented behind casing. This DAS application was first developed by Shell and OptaSense from datasets acquired in the Groundbirch Montney in Canada. In this paper we show examples of DAS-SF in wells stimulated for a variety of completion systems: plug-and-perforating (PnP), open hole packer sleeves (OHPS), as well as, data from a well completed via both ball-activated cemented single point entry sleeves (Ba-cSPES) and coil-tubing activated cemented single point entry sleeves (CTa-cSPES). By measuring the strain fronts during stimulation from nearby offset wells, it was observed that most stimulated stages produced far-field strain gradient responses in the monitor well. When mapped in space, the strain responses were found to agree with and confirm the dominant planar fracture geometry proposed for the Montney, with hydraulic fractures propagating in a direction perpendicular to the minimum stress. However; several unexpected and inconsistent off-azimuth events were also observed during the offset well stimulations in which the strain fronts were detected at locations already stimulated by previous stages. Through further integration and the analysis of multiple data sources, it was discovered that these strain events corresponded with stage isolation defects in the stimulated well, leading to "re-stimulation" of prior fracs and inefficient resource development. The strain front monitoring in the Montney has provided greater confidence in the planar fracture geometry hypothesis for this formation. The high resolution frac geometry information provided by DAS-SF away from the wellbore in the far-field has also enabled us to improve stage offsetting and well azimuth strategies. In addition, identifying the re-stimulation and loss of resource access that occurs with poor stage isolation also shows opportunities for improvement in future completion programs. This in turn, should allow us to optimize operational decisions to more effectively access the intended resource volumes. These datasets show how monitoring high-resolution deformation via FO combined with the integration of other data can provide high confidence insights about stimulation efficiency, frac geometry and well construction defects not available via other means.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".