Design of Autonomous Inflow Control Device Completions in Heavy Oil for Complex Reservoir Structures
Bibliographic record
Abstract
Abstract Understanding reservoir structures is necessary when using autonomous inflow control devices (AICDs). Without a sound knowledge of nearby structures or manmade elements, such as injectors, production from heavy oil applications or any advanced completion may fall short of operator expectations. This paper provides several field examples to help illustrate the importance of understanding reservoir structures when using AICDs. The approach of this study is based on using a near wellbore (NWB) hydraulic simulator coupled with a reservoir simulator to capture holistically the completion/reservoir effects and interaction. With this approach, the production of unwanted fluids is delayed. When unwanted fluids do eventually break through, the AICD can greatly reduce the negative effects of the unwanted fluids on well production. Usually, the unwanted fluids are water and/or gas. Unwanted water is typically (but not limited to) coning or fingering from bottom or edge water drive or from nearby injectors. Gas is coned or fingered downward from the gas cap. Results show that, without understanding the structure and nearby anomalies of the reservoir, operators utilizing ICDs or AICDs can exacerbate the production of unwanted fluids. Field examples are derived primarily from heavy oil well completions designed for multiple operators in Columbia, Ecuador, Argentina, Brazil, Mexico, Alaska, and Canada over the last eight year period. Typically, completion design must change as more information is gathered about the reservoir. Structural maps are often difficult to obtain, however importing the reservoir model into the design reservoir simulator model adds value. However, the field simulator grid blocks can be too large, creating a lack of definition along the horizontal well, and log data may not be included. This paper presents some simple methodologies in generating the most effective design possible from provided data. Establishing clear communication, gathering as much data as possible (including structural maps), and using proper models and simulators are key to optimizing advanced completion designs, particularly when utilizing AICDs in heavy oil.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".