Novel Approach to High Water Cut Measurement in a Mature Oil Field
Bibliographic record
Abstract
Abstract Canadian Nexen Petroleum Yemen (CNPY) produces two million barrels of fluid per day at 95 percent watercut from the Masila Block (Block 14) in Yemen. Asset development decisions are based on numerous factors, one of the most important being the amount of oil produced from a well. With increasing oil price, it is becoming economically feasible to produce wells at significantly higher watercuts. The analytical variability of volumetric methods used in Block 14 to measure watercut restricts the accuracy and precision of these measurements. The authors developed a robust and reproducible water cut determination method for this mature petroleum asset based on gravimetric measurement of oil and water phases. The gravimetric measurement approach follows the volumetric method procedures but uses a balance to weigh the oil or water fractions. To keep consistent with historical reporting of oil production, the density of each phase is then used to correct the values to a volume basis. The methodology outperforms the volumetric determination of water cut by being more reproducible (better precision) and allowing for the accurate measurement of water cuts up to 99.5 percent. In side by side comparison of the gravimetric and volumetric methods at 99.5 percent water cut, the gravimetric methodology showed a five fold increase in accuracy. In addition, the gravimetric method provides six to ten times better precision in the measurement of watercut on representative field samples compared to the volumetric methodology. The program at Block 14 investigated the use of a reference calibration matrix standard as a data quality system to establish the detection limits of the methodology and to institute a testing procedure to monitor laboratory performance. The increased accuracy and precision in watercut measurement gives reservoir engineers the confidence to make economic decisions on well viability to maximize overall project NPV.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".