BP神经网络在富钾卤水中的应用研究被引量：6

Application Research of BP Neural Network in Potassium-Rich Brine

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摘要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 potassiumseeking 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.

作者陈科贵李进黄长兵陈愿愿王刚刘阳 Chen Kegui;Li Jin;Huang Changbing;Chen Yuanyuan;Wang Gang;Liu Yang(School of Geoscience and Technology,Southwest Petroleum University,Chengdu 610500,China;Sinopec Group Zhongyuan Oilfield,Puyang He＇ nan 457000,China;Geophysical Exploration Company,Chuanqing Drilling Engineering Company Limited,Chengdu 610213,China;Research Institute of Exploration and Development,PetroChina Xinjiang Oilfield Company,Karamay Xinjiang 834000,China)

机构地区西南石油大学地球科学与技术学院中国石油化工集团中原油田川庆钻探工程有限公司地球物理勘探公司中国石油新疆油田分公司勘探开发研究院

出处《地球科学进展》 CAS CSCD 北大核心 2018年第6期614-622,共9页 Advances in Earth Science

基金国家自然科学基金项目"四川盆地油钾兼探的地球物理评价方法研究"(编号:41372103)资助~~

关键词富钾卤水深层卤水 BP神经网络模型测井响应 Potassium-rich brine Deep brine BP neural network model Well logging response.

分类号 P619.211 [天文地球—矿床学]

作者简介陈科贵（1959-），男，四川自贡人，教授，主要从事石油地质、测井储层评价技术和四川钾盐普查研究．E-mail：chenkegui@21cn．com;通信作者：李进（1993-），男，四川都江堰人，硕士研究生，主要从事测井解释研究．E-mail：1094129014@qq．com.