Application and Performance of Single-Trip, Multi-zone, High-Rate Water Packs in a Mature Low-Pressure, Low-Temperature Gas Field
Bibliographic record
Abstract
Abstract A total of six high rate water packs were installed in two wells utilizing a single trip, multi-zone gravel pack technique. The multi-zone gravel pack tool system allowed the installation of multiple gravel packs with a single run into the wellbore.1, 2, 3 This gravel pack system was chosen to reduce rig time and to minimize fluid loss into the low pressure, high permeability gas sands. In this high permeability reservoir, frac packing did not appear necessary for stimulation. The multi-layered reservoir and low strength rock made it highly unlikely that single, planar fractures could be created that would grow and connect up all the individual sands. Emphasis was placed on completion efficiency to minimize the effects of non-Darcy flow that severely limit the productivity of conventionally completed wells. This paper describes the nodal analysis and well test evaluation techniques used to assess the rate limiting effects and completion inefficiencies of existing completions in this field. Also presented are single-trip, multi-zone gravel pack completion design and installation techniques that provided two to five times the productivity over conventional completions in a mature, low pressure, low temperature gas field. Sustained deliverability from the two completed wells has deferred both rig and non-rig wellwork for several years.
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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) | 0.000 | 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 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".