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目前国内外常用的方法尚不能准确反映油藏范围内剩余油 (尤其是可动油 )的真实分布和动态分布。经研究 ,在前人工作的基础上 ,提出了用注水井及见水油井现代试井解释剩余油分布的两种新方法。对于见水油井 ,利用具有移动流体界面的多区油藏近似的方法 ,建立了见水后油井影响区域内的数学模型 ,并求出了井底压力的解析式。通过分解 ,得到了地层不稳定渗流的影响因子 ,并建立和利用神经网络的逼近映射求取地层的相对渗透率曲线。针对见水油井区域内的特征 ,推导了相应的饱和度推进方程 ,结合所求的相对渗透率曲线 ,得到了见水油井区域内的剩余油饱和度的分布。并通过实例 ,利用智能化的软件技术验证了这一方法。
At present, the methods commonly used at home and abroad can not accurately reflect the true distribution and dynamic distribution of remaining oil (especially movable oil) in the reservoir range. After research, based on the work of predecessors, two new methods to explain the distribution of remaining oil by modern well testing of water injection well and water seepage well are proposed. For the see-through wells, the mathematical model in the area affected by see-after wells is established by using the method of approximating a multi-zone reservoir with a mobile fluid interface. An analytical formula of bottom hole pressure is obtained. Through the decomposition, the influencing factors of instability seepage of formation are obtained, and the relative permeability curve of formation is established and approximated by neural network. In view of the characteristics of water and oil wells, the corresponding equations of saturation are deduced. Based on the relative permeability curves obtained, the distribution of remaining oil saturation in water and oil wells is obtained. And through examples, the use of intelligent software technology to verify this method.