Smoke Free Testing Solution in Sour Heavy Oil Wells Protects Environment and Gains Production
Bibliographic record
Abstract
Abstract Umm Niqa field has heavy oil reserves in the shallow sandstone reservoir located in Northern Kuwait (NK) is probably the one of the important accumulation of heavy oil (μ > 100 cP). Although a small resource in comparison to Canadian and Venezuelan heavy oil resource, these heavy oil reserves nonetheless represents a significant fraction of Kuwaiti resources1. Kuwait Oil Company (KOC) is aiming to develop and produce from this heavy oil reservoir with the latest technologies and methodologies in a safe and environmentally friendly way. One of the important issue to reach KOCs overall company objective is to protect the environment of Kuwait by eliminating the effect of hydrocarbon burning from oil exploration operations which causes many forms of pollution, noise, toxic gases, soot, acid rain and carbon dioxide emissions. The high H2S content found in the reservoir increases the complexity of the operation and creates a dilemma over operational safety versus environmental concerns. KOC and Schlumberger has been developing strategies to reduce burning of oil having high Hydrogen Sulfide (H2S) concentrations during well clean-up and well testing operations. The ultimate aim is to test the wells with zero flaring, which will also enable significant production gain by immediate flow of hydrocarbon to production. The developed methodology for testing of the sour heavy wells with zero burning of oil consist mainly: Efficient separation of the gas from oil, burning the separated gas separated and scavenging the oil from the remaining H2S afterwards, sending the scavenged oil to storage tanks and transfer it to the production facility. This methodology has been applied successfully in two heavy oil wells resulting over 6000 bbl of production gain and preventing emissions that could have arisen from that amount oil burning. This paper explains the methodology applied to cleanup and well testing operations in two wells from a sour heavy oil reservoir and the results obtained by application of the methodology in details.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".