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基于BP神经网络方法的分层注水量计算

Guo-qing LI,En-yuan ZHAO,Suai-qin REN,Chun-yu JIN

Science Technology and Engineering（2012）

Cited 1|Views18
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Abstract
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.
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separate layer water injection BP neural network forecast
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