Testing of a Novel Autonomous ICD with Low-Viscosity Multiphase Fluids
Bibliographic record
Abstract
Abstract Applying proven technology to control the production of water and gas has become necessary to extend the life of very light-oil reservoirs while optimizing economics. Traditional inflow control devices (ICDs) can help balance the flow of oil, but are not helpful once water and gas breakthrough occurs. Multiphase data and field-evaluation applications show that low-viscosity, fluidic-diode, autonomous ICDs (AICDs) support the production of very light oil while restricting gas and water. Testing has proven that the low-viscosity, fluidic-diode AICD can differentiate oil from water and gas, even very light oils. Tool performance was characterized by measuring the pressure differential vs. the flow rate of diverse oil viscosities representing very light-oil formations in Canada, Russia, Malaysia, and Brazil. The AICD was flow tested with very light oils, water, and gas, as well as multiphase testing simulating mixtures of oil/water for different water cuts and oil/gas at diverse gas-volume fractions. The characterization of flow performance was embedded into sophisticated reservoir simulators for steady and transient evaluations. The multiphase condition of the test fluids was achieved by increasing water cuts and gas-volume fractions. The flow performance tests indicated that the highly sensitive fluidic sensor of the low-viscosity AICD enhances the production of very light oil and restricts water and gas as the water cut and gas-volume fraction increase. The restriction process gradually increases as per the water and gas ratio in the mixture and is reversible if water and gas production recede. Comparisons of the low-viscosity, fluidic-diode AICD vs. a traditional ICD show approximately 25% less water production and 40% less gas production with the AICD. The ability of the low-viscosity AICD to produce very light oils while restricting the flow of gas and water extends the life of light-oil reservoirs by increasing the production of hydrocarbons while helping to lower costs. For optimum reliability, this unique fluidic-sensor technology has no moving parts or control lines, but uses fluid dynamics to distinguish fluids. Multiphase-flow performance testing and field simulation of light-oil reservoirs indicate that the low-viscosity, fluidic-diode AICD favors the production of light oil (0.3 cP–1.5 cP) and restricts the flow of gas and/or water in a multiphase production-flow environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".