A New Analytical Model of Productivity Prediction for Offshore Heavy Oil Reservoir With Cycle Steam Stimulation by Horizontal Wells
Bibliographic record
Abstract
The heavy oil thermal recovery technology has been widely used in land oil field both at home and abroad, but no precedent of offshore thermal recovery (except beach) was reported so far because of the platform limitation and operating cost restriction. Offshore thermal recovery needs higher oil recovery rate and higher cumulative oil production of each well. As for an offshore heavy oil reservoir, which can produce oil by natural energy of formation, the ratio of thermal productivity and cold productivity (oil productivity increment factor) decides whether development by thermal recovery or not. Due to the huge investment of offshore oil field development, too high or too low productivity evaluation will have serious consequences for oilfield exploration and development, so it is very important to make reasonable prediction of the relative oil productivity index (ROPI). Due to the complexity, there is no prediction model of ROPI for horizontal well CSS. Based on horizontal well productivity formula for cold production, on the basis of heated radius of CSS horizontal well, combining with the viscosity-temperature curve of heavy oil, considering the viscosity changes with temperature in heated area, a new analytical model of CSS horizontal well productivity prediction is derived. By the new model, it is easy to get the ROPI of CSS. The research results show that thermal recovery ROPI mainly influenced by heated radius, reservoir thickness and horizontal section length. Case study of CSS horizontal well in N heavy oil field in Bohai shows that, the average oil productivity of first injection cycle is 1.5 ~ 1.6 times of that of cold production, and it is in accordance with that of the prediction model. The new analytical model fills the gap between the complex numerical simulation method and simple experience method, which is of great significance for designing reservoir project of offshore heavy oil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".