Qualification Testing Towards Utilizing an In Well Flow Meter for Production Optimization and Allocation in Nigeria
Bibliographic record
Abstract
Abstract The Agbami project offshore Nigeria uses a suite of production monitoring, control and optimization tools and techniques. The project uses an intelligent well completion whereby production and injection is optimized from the well through the relevant time feedback of information from downhole pressure, temperature gauges and flow meters. A downhole interval control valve provides the capability to control production and injection, thus maximizing the recovery of hydrocarbon from the field. The field consists of both injection and production wells. Oil production is from multiple zones, which are co-produced into the wellbore. Due to the complexities of the subsea production and injection manifold and riser configurations, downhole flow meters are used for production and injection allocation purposes for the different formations within the reservoir. Agbami Field utilizes an in-well flow meter. The technology is based on a differential pressure full bore electronic flow meter which is first in the industry. The sensors utilized are high resolution pressure and temperature sensors. In order to demonstrate the robustness and the capability of the flow meter for production and injection allocation purposes, a series of flow loop qualification testing have been designed. One of the tests used a mixture of oil and water in a test facility to demonstrate the capability of the flow meter to accurately measure oil and water production. This test is probably the first of its kind using a test structure over 100 ft in height. The paper will outline the aims, the preparation requirements, the conducted test and the resulting qualification testing. It will also demonstrate how the results will assist in the production allocation and optimization of recovery from the field. Through this testing, the operator demonstrated commitment to the intelligent well completion initiative as well as the provision of an accurate method of allocating production using in-well flow meter and pressure and temperature gauges.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".