Design a Multiport Completion with Inflow Control Devices: Hydraulic Modeling to Meet Financial Function Optimization in a High Water Cut and CO2 Environment
Bibliographic record
Abstract
Abstract The Midale field, located in Saskatchewan, Canada, is one of the largest water-alternate-gas (WAG) injection operations in the world for enhanced oil recovery (EOR). Wells are normally completed across the three main oil horizons of the Marly formation (M1, M2, and M3), with production from all three zones being commingled. Horizontal producers and injectors are openhole wells, whereas vertical wells are usually casedhole. After years of operation, the WAG injected fluids started to communicate from injectors toward producers, resulting in high water and CO2 cuts and making oil production optimization and recovery more challenging. A new completion was envisioned to improve the oil producers’ rates and overall EOR performance. A casedhole vertical well was selected for upgrading into a new adaptable multiport completion, designed to give independent control of each zone. This new completion was designed considering the challenges of the produced fluids, as well as the interaction between each port, the nozzle size of each inflow control devices (ICD), and the financial impact of producing higher rates under the current (2015–2016) market, which is challenged by low oil prices. The engineering analyses used a steady-state hydraulic well model and a compositional fluid model to consider the impact of the CO2 operating in near critical conditions. Production well tests and laboratory analysis were used to tune the hydraulic well model and the fluid model, respectively. The optimization workflow used the ICD nozzle sizes as decision variables for each port of the new completion, and the financial function, which used reference prices for oil and handling costs for gas and water as coefficients, was maximized by a numerical mixed integer nonlinear programming (MINLP) solver for each case of study. The ICD nozzle sizes were selected for each port of the new completion, based on the hydraulic performance of a series of possible production scenarios, the optimization of the financial function, and the available settings of the ports. This paper presents the results of an integration of production test separator data, production logging test (PLT) surveys, fluid pressure/volume/temperature (PVT) data, equation of state (EOS) fluid models, hydraulic models, and optimization solvers. These data and models enabled engineers to analyze several possible ICD designs to maximize financial gain.
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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".