New Solutions for Well Test Analysis of Horizontal Wells
Bibliographic record
Abstract
Abstract Horizontal wells are becoming popular for primary and enhanced oil recovery operations because of unique advantages of horizontal wells in comparison to those for vertical wells. For example, horizontal wells are being used, at present, for the primary recovery of heavy oil worldwide. They allow a larger reservoir exposure, which increases productivity, accelerate recovery, and reduce coning tendencies. However, horizontal well testing is considerably more complex than vertical well testing. This work addresses new solutions for well test analysis of horizontal wells. With the similar methods in Ref. [1,2,3], taking a horizontal well as a uniform line sink in three dimensional space, by developing necessary mathematical analysis, this paper presents horizontal wells pressure drawdown and buildup analyses in a single porosity reservoir with a circular cylinder drainage volume. The solutions obtained here which are based on three dimensional models are more accurate than the two dimensional model based well testing equations in the literature. Simplified solutions for short and long times that exhibit straight-line sections when pressure is plotted vs. time are presented. Methods for analyzing pressure drawdown and build-up data are presented. The method allows reservoir characteristics, including permeability, skin to be determined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".