Understanding Hydraulic Fracture Stimulated Horizontal Eagle Ford Completions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper will present results from a modeling effort to derive best practices for the completion of hydraulically fractured horizontal Eagle Ford wells. The well, reservoir, completion/frac and production information used in this evaluation were provided by an operator from a five-county area in Texas. 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) improper 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 data model 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 affect well production and economics. A summary of completion and frac methodology for the Eagle Ford, in addition to a ranking of controllable (completion and frac design) and non-controllable (reservoir and geology) parameters that affect Eagle Ford production, will be included in this paper. The information contained in this paper will be useful to those interested in reservoir, completion and frac parameters that affect production from shales analogous to the Eagle Ford. Reservoir quality, completion and frac methodology effects on production results will be quantified in this paper.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it