Microseismic Monitoring of a Restimulation Treatment to a Permian Basin San Andres Dolomite Horizontal Well
Bibliographic record
Abstract
Abstract The results of a microseismic monitoring of a multi-stage refracturing treatment of a Permian Basin San Andres dolomite interval in an open-hole horizontal well will be presented in this paper. The treatment well has a horizontal well trajectory of approximately 3,000 feet within the reservoir section and had been extensively acid fractured during earlier production enhancement operations. The microseismic mapping objectives of the re-fracturing treatment for each of the stages were to characterize the azimuthal orientation of the fractures, the length of each wing, fracture height, and overall stimulation effectiveness. The study discusses mapping microseismic events in a challenging re-fracturing environment. The microseismic activities generated during a re-fracturing treatment may be very low in acoustic energy and detection may be problematic, compared to the acoustic energy released during initial hydraulic fracture propagation. In this study, few microseismic events were detected, and this data indicates that the previously propagated fractures created preferential paths for fluid flow thus reducing the propagation of a new fracture network. In fact, for the stage located the furthest from the monitor well, no microseismic events were detected. This was consistent with an Instrument Magnitude Analysis performed on the located microseismic events from the other stages that showed events further than 1,400 feet away from the monitor well were not detectable. A chemical packer was used for zonal isolation, and ball- activated sliding sleeves were used for selective injectivity for each stage along the horizontal well in the re-fracturing treatment. The operation of the sliding sleeves, for each stage and the ball drops, generated compressional and shear events which were detected by the geophone array in the monitor well. This confirmed that the instrumentation was able to detect events between the treatment well and monitor well in this job and that the microsesimic events induced during the re-stimulation treatment were at a much lower energy. The low-energy events that were located confirmed the ball- activated sleeve worked correctly and the induced fractures stayed in zone. However, the source locations detected did not delineate clear linear propagation of hydrofractures from the wellbore but described a complex fracture network.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".