Optimisation Study of Production-Injection Ratio for Steam Flooding
Bibliographic record
Abstract
Abstract Production-injection ratio (PIR) is one of the most important factors to affect steam flooding performance. Previous research and field applications of steam flooding have confirmed a common conclusion that the production- injection ratio should not be less 1.2 to achieve higher oil recovery for a conventional heavy oil reservoir. This paper presents the optimization study of production-injection ratio for an unconventional heavy oil reservoir with edge/bottom water. Aimed to Jin 45 Block of Liaohe Oilfield, experimental and numerical simulations are carried out and sensibility analyses are focused on the effect of aquifer size. Compared to the conventional heavy oil reservoir, the results of this study have validated as follows:The optimum production-injection ratio (OPIR) of Jin 45 block steam flooding is significantly dependent on the size of its aquifer. Actually, if only the reservoir pressure is available to steam flooding, the OPIR will reduce with aquifer increasing.With the aquifer increasing, the reservoir pressure before steam flooding will relatively increases, thus steam specific volume decreasing and production-injection balance point removing.The difference of production-injection ratio reflects a balance relationship between production and injection underground. For a heavy oil reservoir with edge water, production-injection balance should be completely maintained to prevent water invading which will seriously lead to bad thermal efficiency of steam flooding.Note that the OPIR should be understood as an average concept. Essentially, the production-injection ratio is differently implemented at different stages of steam flooding. Hence, the interval ratio will be corresponding different. This paper suggests that actual reservoir condition and production performance should be globally taken into account to optimize the production-injection ratio at every stage of steam flooding. It is a considerable strategy to satisfy the management requirement of steam flooding program.
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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".