The Advantage of Incorporating Microseismic Data into Fracture Models
Bibliographic record
Abstract
Abstract The Canadian sedimentary basin has long been exploited using vertical well-completion techniques, sometimes producing from multiple formation zones. With recent advancements in horizontal drilling and multistage-completion techniques, horizontal wells are quickly replacing vertical completions as the completion method of choice in many unconventional oil and gas reservoirs. A popular completion method uses openhole isolation packers and ball-activated sliding sleeves to target specific intervals along the wellbore during fracture treatments. This allows multiple stages to be completed in short periods of time because fracture operations often do not have to be shut down to precede to the next stage, compared to the more traditional plug-and-perf completion technique. Microseismic mapping has proven effective in measuring fracture geometries, such as fracture half-length, height, azimuth, and stimulated reservoir volume. This paper outlines the workflow used in understanding and interpreting the created fracture geometry within individual openhole intervals of the openhole packer completion technique. The microseismic data proves that created fracture geometry can vary dramatically along the openhole section of a horizontal wellbore. Microseismic mapping also indicates that fractures do not always initiate across from the sliding sleeve port, but can in fact initiate anywhere along the openhole section, exhibiting, in some cases, multiple fracture initiation points. The microseismic-mapping results of this project were used to identify reservoir coverage along the horizontal wellbore as well as identify areas in the reservoir that were not sufficiently stimulated. By using information gained through microseismic monitoring, fracture models can be calibrated to match actual fracture geometry with modeled fracture geometry, resulting in a calibrated fracture model. Once defined, and using the well production history, the fracture model was used to forecast the future production of the well. Using the calibrated model can help operators optimize the number of stages, stage spacing, and fracture-treatment design to maximize reservoir contact and hydrocarbon recovery while minimizing completion costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".