Nine targets which stand both for the static characteristic of produced formations and the dynamic parameter of wells including the average permeability,variation coefficient of permeability,moving capability,remainin...Nine targets which stand both for the static characteristic of produced formations and the dynamic parameter of wells including the average permeability,variation coefficient of permeability,moving capability,remaining recoverable reserves,coefficient of flooding,daily oil production,increasing rate of water cut,cumulative liquid production per unit meter and efficiency index of oil production are selected as the evaluation indexes,a novel model to evaluate the porous formations in long-term waterflooding sand reservoir was established by using the support vector machine and clustering analysis. Data of 57 wells from Shentuo 21 block Shengli oilfield was analyzed by using the model. Four kinds of formation groups were gained. According to the analysis result,different adjustment solutions were put forward to develop the relevant formations. The Monthly oil production increased 7.6 % and the water cut decreased 8.9 % after the adjusted solutions. Good results indicate that the learning from this method gained will be valuable adding to other long-term waterflooding sand reservoirs in Shengli oilfield and other similar reservoirs worldwide.展开更多
基金supported by funds from the Key Pro-ject of Chinese National Programs for Fundamental Research and Development (863 Program) under thenumber 2007AA090701the Young and Mid-dle-aged Researchers Innovation and Technology Foun-dation of CNPC under the number 04E7029
文摘Nine targets which stand both for the static characteristic of produced formations and the dynamic parameter of wells including the average permeability,variation coefficient of permeability,moving capability,remaining recoverable reserves,coefficient of flooding,daily oil production,increasing rate of water cut,cumulative liquid production per unit meter and efficiency index of oil production are selected as the evaluation indexes,a novel model to evaluate the porous formations in long-term waterflooding sand reservoir was established by using the support vector machine and clustering analysis. Data of 57 wells from Shentuo 21 block Shengli oilfield was analyzed by using the model. Four kinds of formation groups were gained. According to the analysis result,different adjustment solutions were put forward to develop the relevant formations. The Monthly oil production increased 7.6 % and the water cut decreased 8.9 % after the adjusted solutions. Good results indicate that the learning from this method gained will be valuable adding to other long-term waterflooding sand reservoirs in Shengli oilfield and other similar reservoirs worldwide.
