融合深度置信网络与与核极限学习机算法的核磁共振测井储层渗透率预测方法  

Nuclear magnetic resonance logging reservoir permeability prediction method based on deep belief network and kernel extreme learning machine algorithm

作　　者：朱林奇 张冲[1,2] 周雪晴[1,2] 魏旸 黄雨阳 高齐明[1,2] 

机构地区：[1]油气资源与勘探技术教育部重点实验室(长江大学),武汉430100 [2]长江大学地球物理与石油资源学院,武汉430100

出　　处：《计算机应用》2017年第10期3034-3038,共5页journal of Computer Applications

基　　金：湖北省自然科学基金资助项目(2013CFB396);中国石油天然气集团公司重大专项(2013E-38-09);长江大学教育部实验室开放基金资助项目(K2016-09)~~

摘　　要：由于低孔低渗储层孔隙结构较为复杂,现有核磁共振(NMR)测井渗透率模型对于低孔低渗储层预测精度不高。为此,提出一种融合深度置信网络(DBN)算法与核极限学习机(KELM)算法的渗透率预测方法。该方法首先对DBN模型进行预训练,然后将KELM模型作为预测器放置在训练好DBN模型后,利用训练数据进行有监督的训练,最终形成深度置信-核极限学习机(DBKELMN)模型。考虑到该模型需充分利用反映孔隙结构的横向弛豫时间谱信息,将离散化后的核磁共振测井横向弛豫时间谱作为输入,渗透率作为输出,确定NMR测井横向弛豫时间谱与渗透率的函数关系,并基于该函数关系对储层渗透率进行预测。实例应用表明,融合DBN算法与KELM算法的渗透率预测方法是有效的,预测样本的平均绝对误差(MAE)较斯伦贝谢道尔研究中心(SDR)模型降低了0.34。融合DBN算法与KELM算法的渗透率预测方法可提高低孔渗储层渗透率预测精度,可应用于油气田勘探开发。Duing to the complicated pore structure of low porosity and low permeability reservoirs, the prediction accuracy of the existing Nuclear Magnetic Resonance （NMR） logging permeability model for low porosity and low permeability reservoirs is not high. In order to solve the problem, a permeability prediction method based on Deep Belief Network （DBN） algorithm and Kernel Extreme Learning Machine （KELM） algorithm was proposed. The pre-training of DBN model was first carried out, and then the KELM model was placed as a predictor in the trained DBN model. Finally, the Deep Belief Kernel Extreme Learning Machine Network （DBKELMN） model was formed with supervised training by using the training data. Considering that the proposed model should make full use of the information of the transverse relaxation time spectrum which reflected the pore structure, the transverse relaxation time spectrum of NMR logging after discretization was taken as the input, and the permeability was taken as the output. The functional relationship between the transverse relaxation time spectrum of NMR logging and permeability was determined, and the reservoir permeability was predicted based on the functional relationship. The applications of the example show that the permeability prediction method based on DBN algorithm and KELM algorithm is effective and the Mean Absolute Error （MAE） of the prediction sample is 0. 34 lower than that of Schlumberger Doll Researchcenter （SDR） model. The experimental results show that the combination of DBN algorithm and KELM algorithm can improve the prediction accuracy of low porosity and low permeability reservoir, and can be used to the exploration and development of oil and gas fields.

关 键 词：深度学习 核磁共振测井 渗透率 深度置信网络 深度置信-核极限学习机 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P631.1[自动化与计算机技术—控制科学与工程]