Hydraulic Fracturing Diagnostics Utilizing Near and Far-Field Distributed Acoustic Sensing DAS Data Correspondences
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Bibliographic record
Abstract
Abstract Distributed fiber-optic sensing (DFOS) has been utilized in unconventional reservoirs for hydraulic fracture efficiency diagnostics for many years. Downhole fiber cables can be permanently installed external to the casing to monitor and measure the uniformity and efficiency of individual clusters and stages during the completion in the near-field wellbore environment. Ideally, a second fiber or multiple fibers can be deployed in offset well(s) to monitor and characterize fracture geometries recorded by fracture-driven interactions or frac-hits in the far-field. Fracture opening and closing, stress shadow creation and relaxation, along with stage isolation can be clearly identified. Most importantly, fracture propagation from the near to far-field can be better understood and correlated. With our current technology, we can deploy cost effective retrievable fibers to record these far-field data. Our objective here is to highlight key data that can be gathered with multiple fibers in a carefully planned well-spacing study and to evaluate and understand the correspondence between far-field and near-field Distributed Acoustic Sensing (DAS) data. In this paper, we present a case study of three adjacent horizontal wells equipped with fiber in the Permian basin. We can correlate the near-field fluid allocation across a stage down to the cluster level to far-field fracture driven interactions (FDIs) with their frac-hit strain intensity. With multiple fibers we can evaluate fracture geometry, the propagation of the hydraulic fractures, changes in the deformation related to completion designs, fracture complexity characterization and then integrate the results with other data to better understand the geomechanical processes between wells. Novel frac-hit corridor (FHC) is introduced to evaluate stage isolation, azimuth, and frac-hit intensity (FHI), which is measured in far-field. Frac design can be evaluated with the correlation from near-field allocation to far-field FHC and FHI. By analyzing multiple treatment and monitor wells, the correspondence can be further calibrated and examined. We observe the far-field FHC and FHI are directly related to the activities of near-field clusters and stages. A leaking plug may directly result in FHC overlapping, gaps and variations in FHI, which also can be correlated to cluster uniformity. A near-far field correspondence can be established to evaluate FHC and FHI behaviors. By utilizing various completion designs and related measurements (e.g. Distributed Temperature Sensing (DTS), gauges, microseismic etc.), optimization can be performed to change the frac design based on far-field and near-field DFOS data based on the Decision Tree Method (DTM). In summary, hydraulic fracture propagation can be better characterized, measured, and understood by deploying multiple fibers across a lease. The correspondence between the far-field measured FHC and FHI can be utilized for completion evaluation and diagnostics. As the observed strain is directly measured, completion engineering and geoscience teams can confidently optimize their understanding of the fracture designs in real-time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it