Drag Reducing Agent Test Result for ChevronTexaco, Eastern Operations, Nigeria
Bibliographic record
Abstract
Abstract The result of a field test of ConocoPhillips Specialty Products Inc.'s (CSPI) LiquidPowertm Flow Improver, a type of Drag Reducing Agent (DRA), at ChevronTexaco's Inda platform in Nigeria is reported in this paper. Analytically, Inda platform started experiencing deviation in pipeline pressure drop between the actual and theoretical values in about April 1998 and gradually worsened ( Figure 4 ). However, the impact was not felt as the field was under production curtailment. But in March 2003 when the need to increase production arose, it was discovered that the throughput could not be increased beyond 18,400 bopd. Consequently, various analyses and trouble shootings were carried out. Some of the results point to, reduction in the internal diameter of the pipeline, pipeline frictional losses, repair works needed on pumps amongst other problems. And subsequently, the search to finding a technology that would enable the production of additional 2500 bopd shut-in as a result of this pumping handicap commenced. Several companies’ drag reducing agents were evaluated and ConocoPhillips’ LiquidPowertm Flow improver was selected for field test. Upon selection, the LiquidPowertm Flow improver was imported and tested on the Inda PP. The primary test lasted for about 36 hours and was followed by a prolonged secondary test. Some of the challenges faced were mobilizing the hard and soft wares for the test since all the equipment were been imported from Europe (CSPI office base). Also because the Inda crude is injected to a third party facility, we had to contend with seeking approval of the third party company. Additionally since this is the first time the product is being tested in ChevronTexaco Nigeria, we had to contend with pioneer status challenges like extensive discussions, buy-in difficulties, money sourcing, logistics, etc. At the end of the testing period, discussion and analysis were made on what should be the way forward for the extensive use of the DRA and thereafter business decisions were made.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".