Accelerating Completions Concept Select in Unconventional Plays Using Diagnostics and Frac Modeling
Bibliographic record
Abstract
Abstract Data pads in unconventional plays have shown significant value when they are carefully designed to tackle specific problems or concerns. This includes the use of diagnostics to cross-validate development concepts such as stimulation design, well architecture, frac and well spacing, and numerous other variables. In this paper, it is demonstrated how various diagnostics technologies together with subsurface data can be used to calibrate a frac model. The model can then be coupled with a reservoir simulator to accelerate completions concept select decisions in unconventional plays. This process (a) eliminates multiple field trial costs, (b) tests different completions and stimulation designs, and (c) assists in de-risking various field development planning scenarios. This paper focuses on a real-life case-study where integrated diagnostics and modeling were applied to de-risk multiple completions scenarios. An intermediate planar frac model was calibrated and used to lower the uncertainty of key frac parameters including frac geometry and conductivity. In addition, subsurface parameters such as in-situ stresses and rock properties were tuned. The results from the integrated modeling effort were used to propose future development options for the play.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".