An Integrated Approach To Design Completions for Horizontal Wells for Unconventional Reservoirs
Bibliographic record
Abstract
Summary This paper presents a comprehensive and integrated workflow to design completions for a heavy-oil recovery process that involves injection and production through the same well. Unlike in traditional completion design, the transient effects are particularly important to consider while analyzing the long-term performance for these types of completions to capture the effect of variations or uncertainties in reservoir and fluid-flow characteristics over time. The proposed integrated workflow involves initial screening and selection of flow-restricting completions that can meet the desired injection and production performance based on a detailed wellbore hydraulics modeling tool. A select few completions are then analyzed for longer-term performance using a reservoir simulator that couples the flow-restricting nature of completions with flow in the reservoir. The use of best-in-art wellbore hydraulics model and reservoir simulator in a staged process yields an effective way to assess and optimize the completion design for these wells in a reduced time span. The workflow disclosed here can be used to design effective completions for a broad class of cyclic liquid-injection methods for heavy-oil resources.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".