摘要
碳酸盐岩储层受成岩作用影响大,孔隙-喉道结构复杂,孔隙度—渗透率相关性较低,渗透率预测难度较大,常规以线性关系为主的预测方法结果不理想。提出了基于BP神经网络的储层渗透率综合预测方法,可以有效预测碳酸盐岩储层渗透率。方法主要分3步。首先对岩心数据和测井数据进行质量控制;然后结合地质特征,优选预测测井曲线参数和神经网络模型的参数,建立预测模型;最后综合多来源资料,进行预测结果质量控制。将该方法应用于中东地区碳酸盐岩A油藏,渗透率预测结果较好。碳酸盐岩储集空间复杂,孔、洞、缝均发育,岩心塞的渗透率测量只能代表局部位置,而试井资料的动态有效渗透率测量范围较大,可以体现储集空间特征,加之储层黏土矿物含量低,不存在储层敏感性问题和各向异性较弱等因素,最终导致试井动态渗透率数值一般高于岩心渗透率。
Carbonate reservoirs are significantly impacted by diagenesis,exhibiting complex pore-throat structures and a low correlation between porosity and permeability.Predicting permeability in such reservoirs is challenging,and conventional methods relying on linear relationships often yield unsatisfactory results.This study proposes a comprehensive permeability prediction method based on BP neural network,which proves effective for layered phase controlled carbonate reservoirs.The method comprises three main steps.Firstly,quality control is applied to core and well log data.Subsequently,considering geological features,optimal parameters for predicting well log curves and the neural network model are selected to establish the prediction model.Finally,multiple data sources are integrated,and quality control is performed on the prediction results.The application of this method to carbonate reservoir A in the Middle East region yielded favorable permeability prediction results.Given the spatial complexity of carbonate reservoirs with well-developed pores,cavities,and fractures,the measured permeability of core plugs only represent local locations.In contrast,dynamic effective permeability measurements from well logs cover a larger range,reflecting the characteristics of the reservoir space.Additionally,with low clay minerals content and no sensitivity issues or significant anisotropy,the dynamic permeability values from well logs tend to be higher than those from core measurements.
作者
韩如冰
高严
张元峰
HAN Rubing;GAO Yan;ZHANG Yuanfeng(Research Institute of Petroleum Exploration and Development CNPC,Beijing 100083,China;Shuguang Oil Production Plant of Liaohe Oil field CNPC,Panjin,Liaoning 124109,China)
出处
《中国海上油气》
CAS
CSCD
北大核心
2024年第1期100-108,共9页
China Offshore Oil and Gas
基金
中国石油重大科技专项“海外大型碳酸盐岩油藏高效上产关键技术研究(编号:2023ZZ19)”部分研究成果。
关键词
神经网络
碳酸盐岩
渗透率预测
质量控制
动态渗透率
neural network
carbonate reservoir
permeability prediction
quality control
dynamic permeability
作者简介
第一作者:韩如冰,男,高级工程师,主要从事开发地质及沉积储集层方面研究。地址:北京市海淀区学院路20号(邮编:100083)。E-mail:harbin2018@163.com。
