| BFA-CM最优化测井解释方法 |
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| 引用本文： | 潘保芝,段亚男,张海涛,杨小明,韩雪.BFA-CM最优化测井解释方法[J].地球物理学报,2016,59(1):391-398. |
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| 作者姓名： | 潘保芝 段亚男 张海涛 杨小明 韩雪 |
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| 作者单位： | 1. 吉林大学地球探测科学与技术学院, 长春 130061;2. 辽宁省物测勘查院, 沈阳 110121;3. 中国石油长庆油田勘探开发研究院, 西安 710021;4. 胜利石油工程有限公司测井公司一分公司, 山东东营 257200 |
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| 基金项目： | 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开发示范工程"(2011ZX05044)与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)联合资助. |
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| 摘 要： | 最优化测井解释方法能充分利用各种测井资料及地质信息,可以有效地评价复杂岩性油气藏.优化算法的选择是最优化测井解释方法的关键,影响着测井解释结果的准确性.细菌觅食算法(BFA)是新兴的一种智能优化算法,具有较强的全局搜索能力,但在寻优后期收敛速度较慢.复合形算法(CM)局部搜索能力极强,将其与BFA算法相结合构成BFA-CM混合算法,既提高了搜索精度又提高了搜索效率.利用BFA-CM最优化测井解释方法对苏里格致密砂岩储层实际资料进行了处理,计算结果与岩心及薄片分析资料吻合得很好.
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| 关 键 词： | 细菌觅食算法 最优化 测井解释 复合形算法 混合算法 |
| 收稿时间： | 2014-11-30 |
BFA-CM optimization log interpretation method |
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| PAN Bao-Zhi,DUAN Ya-Nan,ZHANG Hai-Tao,YANG Xiao-Ming,HAN Xue.BFA-CM optimization log interpretation method[J].Chinese Journal of Geophysics,2016,59(1):391-398. |
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| Authors: | PAN Bao-Zhi DUAN Ya-Nan ZHANG Hai-Tao YANG Xiao-Ming HAN Xue |
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| Institution: | 1. College of Earth Exploration Science and Technology, Jilin University, Changchun 130061, China;2. Material Testing Exploration Institute of Liaoning Province, Shenyang 110121, China;3. Exploration and Development Institute of Changqing Oilfield, PetroChina, Xi'an 710021, China;4. First Branch of the Logging Company, Shengli Petroleum Engineering Co. Ltd., Shandong Dongying 257200, China |
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| Abstract: | It is difficult to calculate reservoir parameters of the tight sand reservoirs by conventional interpretation methods, because their lithology is complex and the pore structure is variable. The optimization log interpretation method can take full advantages of log data and geological information, so it is an effective method to evaluate tight sand reservoirs. First, in order to calculate reservoir parameters of the tight sand reservoirs, an appropriate interpretation model need to be established according to reservoir characteristics. Then, the interpretation parameters are chosen and the specific form of the objective function is determined. Next, an optimization algorithm is adopted to search for the optimal solution. The bacterial foraging algorithm(BFA) is a newly developed algorithm which has a strong global search capability. It simulates the behavior of the colon bacillus, which swim with flagella for food in the human gut, but it converges slowly in the latter part of the optimization. So it is combined with complex algorithm(CM) for constituting BFA-CM hybrid algorithm to improve the precision and efficiency of searching. Unknown reservoir parameters of the optimization log interpretation method are respectively determined by the genetic algorithm(GA), particle swarm optimization(PSO), BFA algorithm and BFA-CM hybrid algorithm. The calculation results show that compared with GA and PSO, the errors of the porosity and the component content calculated by BFA are minima, but the calculation result curves are jumpy. By combining BFA algorithm with CM algorithm for constituting the BFA-CM hybrid algorithm to calculate reservoir parameters, the accuracy can be improved and the curves become more stable. The results of the BFA-CM optimization log interpretation method have been verified that the objective function value F≈0, and sonic, neutron, density log theoretical value curves(AC0, CNL0, DEN0) fall within the confidence interval, indicating that the system deviation influence does not exist and the optimization results are reasonable and credible. Compared to other algorithms, the BFA-CM hybrid algorithm shows unique advantages in the process of calculating the unknown parameters with the optimization log interpretation method. Its calculation results are of high accuracy and stability, and the efficiency has also been improved. Experimental results show that the BFA-CM optimization logging interpretation method can accurately calculate tight sandstone reservoir parameters, and can be applied to production practice. |
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| Keywords: | Bacterial foraging algorithm Optimization Log interpretation Complex algorithm Hybrid algorithm |
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