Wellbore modeling and design of Nozzle-Based Inflow Control Device (ICD) for SAGD wells
Bibliographic record
Abstract
Abstract Production of bitumen from heavy oil reservoirs is a complex process due to high oil viscosity and heterogeneity of reservoirs. Steam assisted gravity drainage (SAGD) process have been applied to Western Canadian bitumen reservoirs as a feasible and technically effective process to produce heavy oil and bitumen. Considering cost associated with SAGD process and complexity involved in this recovery method, it is vital to optimize design and operation of SAGD to achieve the maximum return. Horizontal wells have become an indispensable component of SAGD process. Therefore, optimizing and improving their design will have significant impact on SAGD recovery method. Despite the fact that horizontal wells improve reservoir access, they suffer from high frictional pressure loss due to their long lengths. In addition, combination of limited control points on horizontal wells (toe and heal) and reservoir quality variation along the well and in the reservoir, poses significant challenges in well control and production optimization. One solution for such issues is the use of Inflow control devices (ICD). ICD's equalize and balance the pressure drop in the wellbore by shortening the flow paths, choking back the steam, and changing pressure potential to velocity potential and provide us with better well control. In this paper, a SAGD well pair with eccentric dual tubing completion is numerically modeled. The numerical modeling consists of a history matching step that is followed by five years of production forecast. The base case is compared with several scenarios with nozzle ICD's installed on the producer well. The simulation cases with ICD include different nozzle sizes and various compartmentalization schemes along the wellbore. The results show how ICD's can improve well performance and increase efficiency of the SAGD process. With the sensitivity analysis done using numerical simulation results, the optimum number of ICD's and nozzle size is proposed based on several screening criteria such as low SOR, steam chamber uniformity, high steam chamber temperature, low subcool, and low steam production. This study reveals the advantages of ICD-equipped wells over the conventional dual-tubing and slotted liner completions for SAGD operation. The improved well performance, increased bitumen production, and longevity of the wells will compensate for additional cost of ICD installation in a short period of time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".