Numerical Simulation Studies on Development of an Offshore Heavy Oil Field by Early-stage Chemical Flooding
Bibliographic record
Abstract
Abstract As one of the major enhanced oil recovery mechanisms, chemical flooding procedure has been widely and successfully used in matured fields and the overall sweep efficiency has been improved between 5–12% due to various chemical flooding treatments. China National Offshore Oil Company (Cnooc) started its pilot offshore chemical flooding projects to evaluate the chemical flooding opportunities at early development phase. In this paper, we present numerical simulation studies on overall evaluation of a Cnooc’s offshore heavy oil chemical flooding project using chemical flooding simulator. In the numerical simulation studies, we have developed a numerical model with the focus on various complex chemical flooding procedures. Moreover, we have also developed a dynamic well model that is capable of modeling multi-phase flow inside complex multi-lateral wellbores. An algebraic multi-grid linear solver has been developed and implemented into the simulator. As the simulator has been developed following the advanced software architecture design and it can be easily expanded other field applications. The targeted field in this paper is an offshore heavy-oil field with hundreds of wells and a complex fault system. The production started in 1999, and waterflooding in 2000. In 2007, all water injectors have been switched to polymer injection for better conformance control. In this field-scale reservoir simulation study, the polymer solution, reservoir brine and the injected water are represented as miscible components of the aqueous phase. Key factors such as inaccessible pore volume, polymer shear thinning effect, polymer adsorption, and relative permeability reduction factors have been taken into account for the construction of the mathematical model. Simulations have been run for evaluation on optimal polymer injection timing, amount and pattern.
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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".