Identifying Frac Growth in Multiple Horizons using Integrated Microseismic and Induced Seismic Monitoring Approaches
Bibliographic record
Abstract
Summary Understanding out of zone frac growth can lead to designing stimulation programs that can effectively enhance production from adjacent horizons. The effectiveness of the stimulation program can be assessed by incorporating monitoring programs that include instrumentation to detect seismic events over a broad range of magnitudes from the smallest detectable events with magnitudes below zero (microseismic) to larger events with magnitudes above zero (induced seismic), typically related to larger pre-existing fractures or faults. Stimulating these larger structures could lead to loss of fluid from the reservoir and affect estimates of stimulated volume. In this study, we examine data recorded using a typical downhole microseismic wireline supplemented with a near surface array designed to record induced seismic events. In this study, two horizons were stimulated. The intent of the program was to stimulate both zones by stimulating wells in the lower horizon by increasing pressure rates both early and late into the injection program in the upper horizon. About 4500 microseismic events and 28 induced seismic events were observed. These larger events represent approximately 83% of the total seismic energy released during the stimulation, which, if only using standard recording, would have been mis-interpreted as microseismic events and thereby would not have contributed to the overall energy dissipation levels. The larger events were associated with fractures with lengths varying from about 40 m to over 110 m, whereas the microseismic fracture lengths varied from ∼5 m up to ∼ 35m. The microseismic and induced seismic events could be used to identify growth from the upper to lower horizon at different pressure rates. The occurrences of larger magnitude events appeared to precede pressure increases in the program, suggesting that larger structures were activated as a result of the injection program even before pressures were increased. This observed process sets the foundation to better control stimulation programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".