Multilateral Well Completion Design Using a 3D Reservoir Simulator: Real Application in Offshore Western Australia
Bibliographic record
Abstract
Abstract The need to develop thin oil reservoirs and heavy oil plays is fast becoming common place in the North Western Shelf (NWS) of Australia. Drilling horizontal multilateral wells is often a requirement to maximise the recovery from a thin oil column and to ensure a field remains economic. Early water coning and heterogeneity uncertainty are problems associated to these reservoir types and it is with the help of Inflow Control Device (ICD) completions that we can combat these issues. This paper focuses on investigating the technical aspects of multilateral completion design; including packer spacing, ICD calibration, modelling, and upscaling. Of the numerous ICD types that exist, two ICDs (nozzle and spiral) are considered. Using 3D reservoir simulation software, we investigate the effects of calibrating the ICD’s parameters in order to refine a simulation model with historical production data. Methods of upscaling the ICDs to reduce model complexity, computation time and the ability to tune parameters are covered in an attempt to understand the differences and benefits of each ICD type. Although preset ICD models with default parameters are provided in most simulation packages, ICD calibration using historical data is required in order to achieve a model that can accurately portray the production of a reservoir. It is important that the Reservoir Engineer informs the completions manufacturer about unknown ICD model parameters during the development phase of a field so that an accurate production profile can be created thus avoiding any disappointing outcomes in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".