Real-Time Monitoring of One-Dimensional Oil-In-Water Emulsion Composition Profiles Using Electromagnetic Frequency Sweeps
Bibliographic record
Abstract
Abstract Accurate knowledge of the oil to water ratio in storage tanks is a crucial parameter in providing accurate water cut production data to downstream processing facilities. This information is especially important within the rag layers which are commonly comprised of a stable oil-in-water emulsion. Various methods have been used to obtain water cut and fluid level measurements that involve capacitance, conductance, acoustic and gamma ray measurements. The biggest shortcomings of most previously used methods lie in their price, accuracy and ecological concerns. To address these, a patented electromagnetic sensing method is used to monitor the composition profiles along the storage oil tanks. One-dimensional spatial profiles of the mineral oil-in-water emulsions sandwiched between water and oil were measured over time in the coaxial cell that represents an oil storage tank. The frequency-domain electromagnetic sweeps are transmitted and received from the top and bottom of the cell using coax cables and a vector network analyzer (VNA). The resulting signals are processed using the Inverse Chirp Z-transform (ICZT) algorithm to obtain the time-domain signals which are then normalized and converted to the spatial fluid composition profiles along the cell's length. Material balance is used to validate the accuracy of the measurements. One-dimensional spatial composition profiles of the mineral and crude oils were measured in real-time. A high-pass frequency filter was applied to attenuate the high frequency components of the frequency sweeps. The frequency range between 300 kHz to 500 MHz is found to be optimal in providing the best signal-to-noise ratio and the match with the material balance. The proposed method enabled monitoring of the spatial profiles of the rag layers as thick as 20 cm with the material balance errors below 1% of the total cell's volume. The novel one-dimensional fluid composition monitoring method is proposed and tested on the mineral oil-in-water emulsions to successfully recover fluid composition by volume and fluid levels at bench scale. This low-cost, environmentally considerate method has a potential to greatly benefit the oil field producers, operators and processing facility stakeholders by providing more accurate real-time spatial water cut spatial distributions of crude oil products stored in tanks.
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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) | 0.000 | 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) | 0.001 | 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".