Evaluating the Impact of Fracture Proppant Tonnage on Well Performances in Eagle Ford Play Using the Data of Last 3-4 Years
Bibliographic record
Abstract
Abstract Since 2008, horizontal wells completed with multi-stage hydraulic fracturing have dramatically boosted Eagle Ford production. Due to high drilling and completion costs, operators have been trying to improve development economics by optimizing the stimulation treatments. This paper presents a look-back analysis on the stimulation treatments performed by different Eagle Ford operators from 2008-2012 using data mining methodologies, focusing on how proppant tonnage has impacted early-time production of their Eagle Ford wells. The Eagle Ford wells have been observed having high decline rates and the early time production performance evaluation will be critical in project economics evaluations and EUR forecasts. The results of this look-back analysis may be used for field development optimization, where to drill and how much proppant should be pumped in each of different areas of Eagle Ford play. The focus of this paper is to present how proppant tonnage could impact the early time production performance. However, there are many variables affecting the early time performance of Eagle Ford wells. In this paper, we present data using bubble maps, contour maps, grid maps and scatter plots to show how some major geological and reservoir engineering variables could affect the early time performance of the Eagle Ford wells. The wells with similarities are grouped in order to minimize the variation of geological variables and other engineering variables and to derive consistent correlations between proppant tonnage and early time performances in different well groups. We present data in different well groups to show the correlations between proppant tonnage and early time well performance. For this study, the authors built a database of 3000+ Eagle Ford wells (including 2300+ horizontals). The authors found that it was very challenging to clean the public data and reformat it to a presentable manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".