SINGLE WELL PRODUCTIVITY EVALUATION AND PREDICTION IN TIGHT CLASTIC RESERVOIR ROCK OF SHAXIMIAO FORMATION IN THE XINCHANG GAS FIELD,WEST SICHUAN
-
摘要: 川西新场气田侏罗系沙溪庙组气藏是典型的低渗透致密碎屑岩气藏,由于地质条件复杂,非均质性极强,储层单井产能预测已成为难点之一.通过分析测井信息和储层物性,提取声波时差、裂缝张开度、裂缝渗透率等11个与产能相关的参数,并运用相关系数法确定裂缝张开度、裂缝渗透率、产能系数、裂缝孔隙度、裂缝发育指数和综合评价指数6个指标作为产能控制特征参数;然后选取33个已测试层位作为已知样本,利用支持向量机构建适合该区的产能预测方程,预测标准误差为0.0661,平均绝对误差仅为0.0182,预测精度较高;最后,利用所构建的产能方程对24个未测试层位的产能进行预测,完成沙溪庙组气藏单井产能评价.Abstract: The gas reservoirs of Jurassic Shaximiao Formation in the Xinchang Gas Field of West Sichuan are typical low-permeability tight clastic ones.Due to complex geological conditions and strongly heterogeneous characteristics in this area,it is difficult to predict single well productivity.By analyzing logging information and reservoir physical property,we first extract 11 parameters related to productivity,such as interval transit time,fracture aperture,fracture permeability,etc.Second,with correlation coefficient method,6 productivity controlling characteristic parameters are determined,including fracture aperture,fracture permeability,productivity coefficient,fracture porosity,fracture developing index and composite evaluation index.Third,we select 33 tested wells as known samples,construct productivity forecast equation with the theory of support vector machine,and then get the standard error of 0.066 1 and the mean absolute error of 0.018 2.In the end,with the equation,we predict 24 untested wells to complete single well productivity evaluation of Shaximiao Formation.
-
[1] 黄天虎,段永刚.气井产能预测综述[J].石油地质与工程,2007,21(2):43-47 [2] 朱彤,叶军.川西坳陷致密碎屑岩气藏类型划分及特征[J].石油实验地质,2004,26(6):537-541,546 [3] 匡建超,徐国盛,王允诚等.致密碎屑岩储层裂缝和产能预测的单井建模[J].矿物岩石,2001,21(2):62-67 [4] 匡建超,王允诚,胡远来等.致密碎屑岩储层裂缝和产能的横向识别[J].矿物岩石,2001,21(4):75-78 [5] 吕正祥,卿淳,曾小英.川西新场气田下沙溪庙组气藏预测评价研究[J].石油实验地质,2001,23(4):400-402,407 [6] 杨长保,聂兰仕,孙鹏远等.改进的模糊神经网络模型在储层产能预测中的应用研究[J].吉林大学学报(地球科学版),2003,33(1):48-55 [7] 刘宗彦,石燕伟.关于测井储层的产能预测研究[J].国外测井技术,2007,22(5):35-36,44 [8] 王昀,张津林.基于地震资料的储层产能预测方法[J].石油天然气学报,2007,29(3):87-89 [9] 杨志明,刘广利.不确定性支持向量机原理及应用[M].北京:科学出版社,2007 [10] Vapnik V N.The nature of statistical learning theory[M].New York:Springer-Verlag,2000.193-245 [11] 乐友喜,袁全社.支持向量机方法在油气储层参数预测中的应用[J].天然气工业,2005,25(12):45-47 [12] 张彦周,刘叶玲,谢宝英.支持向量机在储层厚度预测中的应用[J].勘探地球物理进展,2005,28(6):423-424,431 -
计量
- 文章访问数: 1530
- HTML全文浏览量: 104
- PDF下载量: 894
- 被引次数: 0
下载:
苏公网安备32021102000780号