摘要
对静态地层系数法和动态方程法等分层注水量计算方法分析表明:现有方法考虑注水量影响因素较少,计算误差较大,适用性较差。应用BP神经网络方法计算分层注量,以砂岩厚度、有效厚度、渗透率和沉积相影响系数等16个影响因素作为模型输入参数,单层吸水量作为模型输出。实例计算结果表明:BP神经网络法计算分层注水量与实测值的最大误差为6.51%,平均误差为3.21%,准确性较好,说明BP神经网络方法在分层注水量计算方面具有较好的应用前景。
Through the analysis of the method of separated layer water-injected volume of static state formation capacity and dynamic equation, it noted that：the current method seldom considering affecting factor of injection flow rate is less, the calculation error is bigger, and the applicability is poorer. The separated layer water-injected volume by using BP neural network was calculated and defined sand thickness, effective thickness, permeability, sedimentary facies influence coefficient and other 16 factors as model input parameters, individual layer water-injec- ted volume was defined as output parameter. Through the calculated results of example showed that： the calculation result error in measured value returned by maxim of 6.51% , and by an average of 3.21% , the calculated result compared with actual injection was accurately, So the method of BP neural network used in splitting calculation of water-injected volume could have excellent aoolication orosoect.
出处
《科学技术与工程》
北大核心
2012年第10期2425-2427,2431,共4页
Science Technology and Engineering
基金
黑龙江省留学归国基金项目(LC2011C28)资助
关键词
分层注水
BP神经网络
预测
separate layer water injection BP neural network forecast
