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Outline
As oilfield development enters the middle and late stages, formation energy deficit becomes increasingly severe and injection-production conflicts become more prominent. Conventional empirical injection allocation and numerical simulation methods often face challenges such as high computational cost, difficult history matching, and insufficient prediction accuracy when applied to complex reservoirs with strong heterogeneity and rapid dynamic changes. To address these problems, an injection-production optimization method integrating production dynamic data, physical constraints, and adaptive machine learning is proposed. In this method, LSTM and XGBoost are used to construct a parallel prediction framework, and the prediction model is selected through multi-index competition. Kalman filtering and nonlinear diffusion filtering are adopted to reconstruct injection-production response signals, and a physics-constrained ANN combined with gradient sensitivity analysis is used to invert inter-well connectivity coefficients. On this basis, a dual-objective injection allocation optimization model considering both oil production enhancement and water-cut control is established and solved using the differential evolution algorithm. A case study from B Oil Production Plant shows that the physics-constrained LSTM achieves an R2 of
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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