Optimization of Placement of Flow Control Devices under Geological Uncertainty in Steam Assisted Gravity Drainage
Bibliographic record
Abstract
Abstract Steam chamber conformance in Steam Assisted Gravity Drainage (SAGD) influences the efficiency and economic performance of bitumen recovery. Conventional SAGD well completion designs provide limited control points in long horizontal well pairs leading to development of a non-ideal steam chambers. Developing advanced wellbore completions and optimizing downhole tool settings is critical to achieve optimal steam distribution in heterogeneous reservoirs for optimal recovery. This paper presents a workflow to optimize SAGD well completion design by using flow control devices (FCDs). Optimum FCD placement, and specifications are determined in consideration of reservoir heterogeneity. Uncertainties in spatial distribution of facies and rock types, reservoir rock and fluid properties are represented by multiple equiprobable deterministic and stochastic geological realizations using Monte-Carlo simulation. The methodology is based on constrained nonlinear optimizationtomaximize the net present value (NPV) as the objective function. A coupled wellbore/reservoir simulation model of a well pad is implemented in the study, and the efficacy of different scenarios with varied well designs are assessed from evaluating bitumen production, steam injection, and well completion expenses. Results indicate superior performance of the wells equipped with FCDs compared to conventional concentric and parallel dual string well completion designs. For the cases examined, this translates to an average 7% increase of the expected NPV for different well completion designs when using FCDs. Furthermore, results show using zonal isolation in the well design is essential for compartmentalized reservoirs such aspoint bar deposits with their significant heterogeneity. Advanced wellbore completions provide sufficient tools to constrain steam injection and liquid production into and from different well segments, and manage steam chamber conformance along the horizontal well pairs, improve production efficiency, increase bitumen recovery, and reduce operating costs. A novel workflow is presented to optimize advanced wellbore completions utilizing flow control devices. This integrated assisted optimization approach considers uncertainties in geological properties, and determines the optimal FCD parameters and well completion design with acceptable computational effort. This integrated workflow allowed us to undertake a thorough evaluation of the key subsurface uncertainties, and design an overall development plan. The probabilistic nature of the results legitimize quantifying the uncertainties and identify associated risks for different completion strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".