西湖凹陷X气田致密气储层测井评价及产能预测  

Logging Evaluation and Productivity Prediction of Tight Gas Reservoirs in X Gas Field of Xihu Sag

作　　者：王安龙 翁冬子 任培罡 吕鹏 潘潞[1] 吕艾新 魏锋[1] WANG Anlong;WENG Dongzi;REN Peigang;LV Peng;PAN Lu;LV Aixin;WEI Feng(Institute of Exploration and Development,SINOPEC Shanghai Offshore Oil&Gas Company,Shanghai 200120,China;SINOPEC Huadong Petroleum Engineering Limited Company,Nanjing Jiangsu 210098,China)

机构地区：[1]中国石油化工股份有限公司上海海洋油气分公司勘探开发研究院,上海200120 [2]中石化华东石油工程有限公司科技发展分公司,江苏南京210011

出　　处：《海洋石油》2023年第4期77-82,共6页Offshore Oil

摘　　要：海域气田对单井产量要求较高,测井解释不仅要求孔渗饱的精度和解释结论的符合率要高,还要求对储层是否具有动用价值(预测产能)给出测井评价。近年来主要评价对象是致密储层,进一步增加了测井评价的难度。该文针对上述工作中面临的实际问题,针对性采用三种方式解决:首先,通过分沉积相的方式确定定量解释模型和参数;二是利用神经网络方法识别流体性质;三是利用测井参数预测产能。结果表明,通过分沉积相建立有针对性的测井解释模型,可以提高定量解释参数的准确性;神经网络方法可以帮助识别流体特性;在类似的地质和工程条件下,测井数据的孔隙度、渗透率和饱和度可以用来估计储层产能。上述方法在实际应用中效果较好,拓展了测井资料的应用范围。The single well production is required to be high in the offshore gas field.Logging interpretation is only required to have high accuracy of porosity,permeability and saturation and high coincidence rate of interpretation conclusions,but also to provide the logging evaluation of whether the reservoir has production value(predicted productivity).In recent years,the main evaluation object is tight reservoirs,which further increase the difficulty of logging evaluation.In this paper,three methods are used to solve the practical problems in the above work.Firstly,quantitative interpretation models and parameters are determined by dividing sedimentary facies,The second step is to use neural network methods to identify fluid properties.The third step is to use logging parameters to predict production capacity.The results indicate that establishing a targeted logging interpretation model by dividing sedimentary facies can improve the accuracy of quantitative interpretation parameters.Neural network methods can help identify fluid characteristics.Under similar geological and engineering conditions,the porosity,permeability,and saturation of well logging data can be used to estimate reservoir productivity.The above methods have shown good results in practical applications and expand the application range of logging data.

关 键 词：致密气 沉积相约束 定量解释模型 神经网络法 产能预测 

分 类 号：P631.8[天文地球—地质矿产勘探]