富钾卤水是一种重要的液态钾盐资源,是四川盆地主要的找钾方向之一。川东地区三叠纪地层内与含盐系相邻的碳酸盐岩储层卤水矿化度极高,卤水资源丰富,开采潜力巨大,是我国目前钾盐勘探研究的重点区域。针对常规测井解释方法识别卤水层速度慢、准确率不高等特点,提出建立BP神经网络模型开展富钾卤水层的识别与划分。以BP神经网络理论和测井解释原理为基础,对卤水层识别影响最大的测井曲线值作为输入,建立BP神经网络模型,开展深层卤水层和富钾卤水层的识别和划分,并用准确的录井结果验证模型性能。测试发现,模型识别卤水的准确率为85.7%;改进的富钾卤水模型识别准确率为89.1%。结果表明,BP神经网络技术在四川盆地钾盐的勘探开发过程中具有广阔的应用前景。

Potassium-rich brine, an important source of liquid potassium salt, is one of the major potassium-seeking directions in the Sichuan Basin. The potassium-rich brine in Triassic carbonate formation of the eastern Sichuan has a high degree of salinity, high exploitation potential and huge resources. It is one of the key areas of potassium salt exploration in China. Aiming at the characteristics of conventional logging interpretation method to identify deep brine with slow speed and low accuracy, it was proposed to establish BP neural network model to carry out the identification and division of potassium-rich brine in Sichuan Basin. Based on the theory of BP neural network and logging interpretation methods, a neural network model with logging curves as input was built and applied to the deep brine and potassium-rich brine. The discrimination results were compared with logging data. The accuracy rate of the model reaches 85.7%,and the accuracy rate of the improved potassium-rich brine model achieves 89.1%. This study demonstrates that BP neural network has a wide application prospect in the exploration and development of potassium salt in Sichuan Basin.