A Steam Injection Rate Allocation Method Considering the Dynamic Heterogeneity in Stratified Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract The improvement of production profiles in stratified heavy oil reservoirs holds paramount significance within the domain of improved oil recovery. The separate-layer steam injection technique is an effective approach to minimize the recovery difference among layers. However, the accuracy of steam allocation is hampered by the dynamic heterogeneity after long-time steam flooding. This paper proposes an improved injection rate allocation model for stratified heavy oil reservoirs with a separate-layer steam injection process. Considering the time variable phenomena of temperature and water saturation in reservoirs during a steam flooding process, the classical Buckley-Leverett (BL) displacement theory is extended to establish an injection rate allocation optimization strategy for stratified heterogeneous heavy oil reservoirs. First, the water saturation at an oil-water front and the average water saturation (AWS) in a two-phase region are determined. Then, the layers occurring (hot) water breakthrough are identified. Furthermore, the water saturation at the outlets of the layers occurring water breakthrough is calculated, and the location of an oil-water front in pre-breakthrough layers is obtained. Finally, the injection rates of these two-type layers are programmed by assuming an equivalent water saturation at each outlet. The reservoir properties and injection parameters in an actual heavy oilfield are input into the optimization program. Field data shows that a permeability heterogeneity can highly affect the temperature and water saturation in layers. The results demonstrate that a high-permeability layer (HPL) occurs water breakthrough, in which the current water saturation exceeds the AWS in a two-phase region. Moreover, affected by a variation of temperature, a fractional flow curve in a layer with a low water-oil viscosity ratio (WOVR) is more convex, while a fractional flow curve in a layer with a high WOVR is more concave. As the water saturation in a layer before adopting the separate-layer steam injection technique increases, the optimized injection rate per unit reservoir thickness decreases. The injection rate allocation is found to be a strong function of separate-layer injection time. Furthermore, a decrease in the injection time results in a greater contrast of injection rate allocation. Based on the programmed optimization code, the contrasts of injection rate allocation among layers for 5 years and 10 years are 9.2 m3/(d·m) and 8.3 m3/(d·m), respectively. This research extends the application scope of the Buckley-Leverett displacement theory into a non-isothermal displacement process. It provides valuable insights for designing suitable injection rate allocation in stratified heterogeneous heavy oil reservoirs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".