Spatio-temporal microseismic analysis of the Woodford Shale, Canadian County, Oklahoma
Bibliographic record
Abstract
Microseismicity provides data that can be used to monitor hydraulic fracture stimulation programs as well as to characterize the resulting hydraulic fractures. This is especially important in low permeability gas and oil shales, where the creation of additional permeability through these fracture treatments is essential to production. However, many applications and analyses of these data are either qualitative in nature or include a large element of interpretational bias. This study looks at two microseismic analytical techniques: the radius of gyration (ROG) tensor, as described in Sayers & Le Calvez (2010), and the methods described in Shapiro (2008) that relate spatio-temporal microseismic signatures to hydraulic diffusivity and ultimately hydraulic permeability. The radius of gyration tensor is used to generate a characteristic ellipsoid for any set of microseismic events, and the aspect ratio of this ellipsoid can be related to local in-situ horizontal stress ratios. These methods are applied to surface microseismic data collected for six horizontal wells drilled and completed in the Woodford Shale in Canadian County, Oklahoma. Additionally, an attempt is made to bridge the gap between these methods. Specifically, the characteristic ellipsoid generation from the radius of gyration tensor in Sayers & Le Calvez (2010) is applied to Shapiro’s workflow. Shapiro attempts to link the 3D anisotropic triggering front of seismicity, which is an ellipsoid that envelops time-scaled microseismic events, to reservoir permeability. The radius of gyration tensor will generate this ellipsoid and remove interpretational bias that would have been present otherwise. Lastly, an attempt is made at relating the signatures present in the characteristic ellipsoids to zones of natural fracture reactivation. This is done on a stage-by-stage basis. The hypothesis is that an ellipsoid with a significant tilt in its most vertical principal axis and a significant azimuthal rotation away from the maximum horizontal stress direction will be indicative of natural fracture reactivation. What defines a “significant” amount of deviation in each case is open to discussion and further study. The results of this study are mixed. Due to data and time restrictions, the radius of gyration tensor was only able to generate a rough range of approximations for maximum horizontal stress (see Section 3.3.1), and the lack of key core data prevented hydraulic permeability from being estimated. However, there were several results of this project that can be considered a success. First, the radius of gyration tensor can arguably be used as a natural fracture reactivation indicator, as detailed in Section 3.4. If a correlation can be drawn between natural fracture reactivation and production improvements, this tool can then be used as an indicator of which stages will perform better. This same radius of gyration tensor can also be leveraged in an otherwise interpretive analytical setting defined by Shapiro (2008), as mentioned above. This process is detailed in…
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".