基于伽马射线技术的油井含水率神经网络预测

伽马射线含水分析仪已广泛应用于油井含水率测量.但由于探测器采集到的射线计数与理论值存在一定的误差,使得在实际应用中油井含水率的测量结果也与真实值存在一定的偏差.为了消除该误差提高含水精度,首次通过人工神经网络对含水仪的高、低能伽马计数率与实际化验的含水率数据进行分析与训练,将高、低能伽马计数作为网络的输入数据,含水率与...

ANN W atercut Prediction of Oil Wells Based on Gamma Ray Technology

The watercut meter based on the gamma-ray technology has been widely used in oil wells.However,as the gamma counting collcted by the detector gives a certain deviation with theoretical value,the measured oil well watercut in practical application presents a certain deviation compared with the true readings.To eliminate this error and improve the accuracy of watercut measurement,the measured watercut and sampling watercut are analyzed to build an,artificial neural network(ANN),for the first time.The built ANN is mainly used to predict watercut of the oil wells.The high energy counts and low energy counts of the detector are used as input data of the network,while the watercut and gas volume fraction,GVF,are used as output data of the network.The results show that the prediction data of the proposed ANN model has a good agreement with the oil field sampling data.The deviation between the predicted watercut of the network and the reference is about±2%,and the deviation of the GVF is less than±1%.The new method based on neural network combining high and low energy gamma counts and watercut analysis will play an important role in improving the accuracy of testing watercut in oil fields.