Case Study: Integrated Strategy Solves Well Test Issues in Deep-Water and Heavy-Oil Environments
Bibliographic record
Abstract
Abstract As the operator, CNOOC completed drilling two exploratory wells in a deep-water field of the Republic of Congo from May to November of 2013, in 600 m of water depth. Indications of oil and gas- heavy oil layer, gas layer and thin oil layer were found in the E-1 well according to logging data analysis. There are many challenges for the operator and service companies to perform the appraisal of this heavy oil reserve in a deep-water offshore environment, such as operational cost control, low temperature near the seafloor/mud line, heavy oil flow assurance, sand control, etc. In order for the well test to be effective, all the above issues must be fully understood and methodology employed that will reduce the chance that operational risks will occur. This paper presents an integrated well test program of the E-1 well developed and implemented for reservoir characterization and formation evaluation in the Congo basin. The program used a combination of various formation evaluation techniques such as wire wrapped screen sand control, PCP with electric heating rod and insulation tubing, hydrate prevention, emergency response in complex condition. The integrated well test program was designed and modified to meet the project delivery timeline and cost constraints, while responding to the challenge of properly testing the oil/gas reservoirs. This paper also gives the data analysis results and summarizes the encountered problems and learned lessons from field operations. The treatment practices and gained experiences from the field operation presented here provide valuable guidance for future deep-water heavy oil exploration and development operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".