Optimization of Horizontal Well Completion Design
Bibliographic record
Abstract
Abstract A well completion is a critical interface between the productive formation and the wellbore. An effective completion must maintain mechanical integrity of the borehole without creating any significant restrictions in the flow capacity of the well. In this paper, we outline a process to design optimal completions for horizontal wells by applying comprehensive skin factor models that include damage and turbulence effects for all common types of completions. Slotted or perforated liner, cased and perforated completions, or gravel pack completions have been used in horizontal wells for borehole stability and sand control purposes. However, these completions may have lower productivity (as characterized by a positive skin) relative to an equivalent openhole completion because the convergent flow to perforations or slots increases fluid velocity in the near-well vicinity. In addition, any reduced permeability zones (formation damage caused by drilling, completion, or other processes) magnify the convergent flow effects, and hence, may result in severe skin factors. Compound effects of formation damage around the well completion, a crushed zone due to perforating, the plugging of slots, and turbulent flow, as well as interactions among these effects are included in the model. We first illustrate how to use the skin factor models to screen the available completion types for different applications. This screening approach considers reservoir permeability, permeability anisotropy, fluid properties, formation damage effects, and rock mechanical characteristics as the key parameters. The types of completions that yield the most productive well performance for this matrix of properties are presented. A more detailed completion design is then illustrated by showing the use of the skin factor models for selection of liner completions for viscous oil reservoirs on the North Slope of Alaska. Application of the slotted or perforated liner models to the readily available liners showed that the completion skin factor can vary by as much as 40%, depending on the detailed characteristics of the slots or perforations in the liner (slot or perforation size, density, and distribution). This showed how analyzing the performance of the completion design can greatly increase well productivity at little or no cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".