摘要
鄂尔多斯盆地苏里格气田是低渗透致密砂岩气藏的典型代表,根据毛细管压力曲线形态,基于自组织神经网络技术,建立了孔隙结构测井自动判别和分类渗透率计算模型,有效提高渗透率计算精度;在储层导电机理研究基础上,建立了基于粘土束缚水、微孔隙水和自由水"三水"并联导电的含水饱和度计算模型,显著提高了储层含气性评价精度。实际测井资料处理解释结果与岩芯分析资料对比,验证了方法的可行性和准确性。
Sulige Gas Field in Ordos Basin is the typical representation of tight sand gas reservoir of low permeability.According to the shapes of capillary pressure curves of core samples,based on self-organizing neural network,the pore structure automatic discrimination models and permeability interpretation models were established,which highly improved the permeability calculation precision.According to the research of reservoir conducting mechanism,we adopted the＂three water model＂（clay water,micro pore water and free water）for saturation interpretation model,which effectively improved the gas evaluation precision.Actual log data were processed with this method and its feasibility and veracity are validated by comparing the processed result with the core analysis result.
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2012年第5期71-77,共7页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
国家科技重大专项(2011ZX05044)
中国石油股份公司科技重大专项(2010E–2304)
