Data Driven Modeling Improves the Understanding of Hydraulic Fracture Stimulated Horizontal Eagle Ford Completions
Bibliographic record
Abstract
Abstract The subject of this paper is the results from a data driven modeling effort to derive best practices for the completion of hydraulically fractured horizontal Eagle Ford wells. The well, reservoir and production information used in this evaluation were provided by an operator, and are from a five county area in Texas consisting of Karnes, Gonzales, Atascosa, Dewitt and Live Oak. Hydraulically fractured horizontal completions pose significant modeling and evaluation challenges. This is primarily due to two issues; 1) lack of well specific data about the reservoir/rock properties and 2) unrealistic assumptions used in the modeling process. As shown in this paper, a data driven approach to modeling these completions provides a much needed pragmatic perspective, identifies high impact parameters and provides direction about how to improve the effectiveness of these complex completions. Sensitivities performed on the predictive model developed from Eagle Ford data indicate that well to well variation in reservoir quality and geology has a dominant effect on Eagle Ford production. In addition, issues such as fracture spacing, frac volume, perforation distribution, proppant selection and wellbore length also effect well production and economics. A ranking of controllable (Completion and Frac) and non-controllable (Reservoir and Geology) parameters that affect Eagle Ford production is included in this paper. This information can be used to derive best practices and is useful in explaining well to well variation in Eagle Ford production by quantifying the effect of reservoir quality, completion and frac methodology on results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".