Scoring completion effectiveness in unconventional horizontal wells using 3D seismic attributes
Bibliographic record
Abstract
Abstract Using a multihorizontal well-completion pilot in an unconventional oil field in the Lower 48 (contiguous United States) Midcontinent, a workflow is proposed to evaluate the effectiveness of horizontal-well completions through 3D surface-seismic attributes and microseismic data. Three-dimensional surface seismic is often continuous over unconventional fields and provides a measurement in areas where no other data are available. In addition, microseismic is the only geophysical data that allows us to “see” the apparent hydraulic-fracture zone around a wellbore. Using these two geophysical tools integrated with engineering and geologic data such as image logs, total proppant, number of stages, estimates of fracture length and height, permeability, chemical tracers, and production rates, a “geophysical completion scorecard” is developed to evaluate a multiwell horizontal pilot and apply it to additional wells drilled outside the pilot in an effort to predict their completion effectiveness. A postdrill review of production rates in those wells shows good correlation to the scorecard's predictions of completion effectiveness.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".