人工蜂群最优化测井解释方法在致密砂岩储层评价中的应用  

Application of Artificial Bee Colony Optimization Log Interpretation Method to Tight Sandstone Reservoir Evaluation

作　　者：孙茹雪 潘保芝[1] 石玉江[2] 张海涛[2] 杨晓明[2] 李晓[1] 

机构地区：[1]吉林大学地球探测科学与技术学院,吉林长春130061 [2]中国石油长庆油田勘探开发研究院,陕西西安710021

出　　处：《测井技术》2017年第3期320-324,共5页Well Logging Technology

基　　金：国家科技重大专项大型油气田及煤层气开发鄂尔多斯盆地大型低渗透岩性地层油气藏发示范工程(2011ZX05044);国家自然科学基金项目松辽盆地深层火成岩CO2气藏岩石物理参数研究(41174096)

摘　　要：最优化测井解释方法打破了常规解释的局限性,充分利用测井资料及地质信息,可有效地评价复杂岩性储层,而优化算法是最优化测井解释的关键。利用人工蜂群(ABC)最优化方法求取苏里格致密砂岩储层某段的各组分体积含量,并与细菌觅食-复合形混合算法(BFA-CM)和遗传算法(GA)进行对比。结果表明,ABC最优化测井解释方法在处理致密砂岩储层时具有参数设置简单,全局搜索能力强,不需要二次优化就可达到所需精度,为最优化测井解释提供了一种新方法。Optimization log interpretation method can break the limitation of the conventional explanation, and make full use of logging data and geological information, which can effectively evaluate the complex lithology reservoir, and optimization algorithm is the key to optimization log interpretation. With artificial bee colony （ABC） algorithm optimization log interpretation method, we calculate the volume content of each composition of tight sandstone reservoir in a certain period, and compare with bacteria foraging algorithm-complex hybrid algorithm （BFA- CM） and genetic algorithm （GA）. Results show that ABC optimization log interpretation method can achieve the required precision of the edge in dealing with a dense sandstone reservoir, so the parameter is set simply and global search ability is strong, and does not require quadratic optimization. It provides a new method for optimization log interpretation.

关 键 词：最优化测井解释 人工蜂群算法 致密砂岩储层 遗传算法 细菌觅食-复合形混合算法 

分 类 号：P631.84[天文地球—地质矿产勘探]