摘要
总有机碳含量(TOC)作为评价烃源岩的重要参数.对于一些勘探开发难度较大的区块,合理预测TOC含量对区块勘探开发具有重要意义.目前预测TOC含量的方法以ΔlogR法为主,但是ΔlogR法对于异常点处理并没有系统的标准,人为主观性较强,同时在伦坡拉地区ΔlogR法预测效果一般.因此本文采取BP神经网络法进行TOC含量的预测.为了确定敏感性测井曲线的选择,将标准归一化后的测井参数与TOC含量进行相关系数分析,选取自然电位(SP)、自然伽马(GR)、声波时差(AC)、体积密度(DEN)及深度作为输入层,以TOC含量为输出层建立一个5×2×1的BP神经网络模型.研究结果表明,训练样本与测试样本的预测值与实测值相关性均超过0.8,模型拟合效果良好.TOC含量预测结果表明在纵向上伦坡拉盆地中牛三段下亚段、牛二段上亚段及牛二段中亚段均发育有较好烃源岩,应为下一步勘探开发的重点;在平面分布上,蒋日阿错凹陷的中部和东部发育最有潜力的烃源岩.
Total Organic Carbon Content(TOC) is an important parameter for evaluating hydrocarbon source rocks. For some blocks with great difficulty in exploration and development, it is of great significance to reasonably predict TOC content for exploration and development of the blocks. At present, ΔlogR method is the main method to predict TOC content, ΔlogR method has no systematic standard for abnormal point processing and is subjective. At the same time, the prediction effect of ΔlogR method is general in the region of Lunpola area. Therefore, this paper adopts BP neural network method to predict TOC content. In order to determine the selection of sensitive logging curves, correlation coefficient analysis is carried out between normalized logging parameters and TOC content, and Spontaneous Potential(SP), Natural Gamma(GR), Acoustic(AC), Volume Density(DEN) and depth are selected as input layers, and TOC content as output layer to establish a 5×2×1 BP neural network model. The results show that the correlation between the predicted and measured values of training samples and test samples exceeds 0.8, the model fitting effect is good. TOC content prediction results show that there are good source rocks in the bottom of the third member of Niubao Formation, the upper of the second member of Niubao Formation and the middle of the second member of Niubao Formation in the vertical upper Lunpola Basin, which should be the focus of the next exploration and development. On the plane distribution, according to the predicted TOC planar distribution, it is believed that the most potential source rocks are developed in the central and eastern parts of Jiangriacuo sag.
作者
卢鹏羽
毛小平
张飞
宿宇驰
毛珂
LU PengYu;MAO XiaoPing;ZHANG Fei;SU YuChi;MAO Ke(School of Energy Resources,China University of Geosciences(Beijing),Beijing 100083,China;Key Laboratory for Marine Reservoir Evolution and Hydrocarbon Abundance Mechanism,Ministry of Education,China University of Geosciences(Beijing),Beijing 100083,China)
出处
《地球物理学进展》
CSCD
北大核心
2021年第1期230-236,共7页
Progress in Geophysics
基金
中国石油化工股份有限公司勘探分公司科研项目“青藏地区油气地质条件与资源潜力分析”(35450003-17-ZC0607-0018)资助。
作者简介
第一作者:卢鹏羽,男,1996年生,中国地质大学(北京)在读硕士研究生,研究方向为盆地模拟与资源评价.E-mail:756858528@qq.com;通讯作者:毛小平,男,1965年生,博士后,副教授,主要从事盆地模拟与资源评价方面的研究工作.E-mail:Maoxp9@163.com。
