How New Horizontal Wells Affect the Performance of Existing Vertical Wells
Bibliographic record
Abstract
Abstract Horizontal wells are reviving many marginal oil fields through accelerated oil production. In some fields, a single horizontal well produces as much as all the vertical wells put together. While there are prolific results as far as the profitability of the operation is concerned, there are other pertinent questions that arise. Does the vertical well flow pattern suffer as a result of horizontal well production? Does the ultimate oil recovery increase as a result of horizontal well operation? Given a pattern of vertical wells, can a horizontal well location be so chosen that the total oil production (and recovery) is maximized? These are the questions this paper attempts to address. The results show that horizontal wells do indeed capture the oil in the drainage area of vertical wells. The extent of this "loss" depends on several factors. These include direction and allocation of the horizontal well within the drainage area, position of the horizontal well between the vertical boundaries, permeability anisotropy, net pay thickness, and oil viscosity. For a given pattern, there is a horizontal well configuration that maximizes the total production rate. The question of ultimate oil recovery is quite complex, and it may be said that in light oil reservoirs a horizontal well would tend to increase the ultimate oil recovery. Although it is concluded that, each of these factors ascribes to significant effects, it is the location of horizontal well that warrant the greatest effect on overall performance of vertical well.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".