Developments in Diagnostic Tools for Hydraulic Fracture Geometry Analysis
Bibliographic record
Abstract
Summary URTeC 1619968 In this paper we examine diagnostic tools for hydraulic fracture stimulation (HFS) that can help determine the geometry, reach, and effectiveness of the created fracture(s). In particular we focus on using the combination of micro-seismic and Distributed Acoustic Sensing (DAS) technology to gain a more complete understanding of the resulting stimulation treatment and identify further optimization opportunities. DAS has many applications, and this paper will consider two particular cases in combination with micro-seismic data acquired by geophones in an observation well: When DAS is recorded in a treatment well, it can be used to determine and quantify which stages and perforations take fluid. When this is combined with micro-seismic data we can establish a relationship between the micro-seismic events and the fracture fluid itself. This can help determine the overall effectiveness of the fracture design and resulting treatment. When DAS is recorded in an offset well (developed in the same pad as the treatment well), it can be used to determine when hydraulic fracture fluid (initiated from the neighboring treatment well in the same pad) intersects that well. We can then establish how the micro-seismic events relate to actual fluid placement and interference from neighboring wells. It can also provide an exact location and timing where fractures intercept offset wells and are linking up, and are a direct measurement of a conductive fracture. In both cases micro-seismic combined with DAS data provides an opportunity for further completion optimization, well spacing optimization, and/or pad design and/or well spacing.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".