引入小种群的遗传算法求解阿尔奇公式参数与彰武地区油井水饱和度的分析
发布时间：2018-08-08 13:31
【摘要】：测井解释的方程中一般都会存在一些定量的参数,譬如作为计算地层含水饱和度并估算油气藏的阿尔奇公式,它的公式中的a、m、n值就很难在实际中被同时地准确确定,通常情况下这些参数都会被单个,或者两个一起来测定,然后当把获取的参数真正地带入实际生产中的计算时,结果往往不如人意,而这些参数的正确选取又密切关系着最后解释成果的精确度。遗传算法具有全局寻优的特点,是一种新型的最优化方法,它适合于求解非线性优化问题,近年来已经在复杂函数的优化求解结构优化设计、自适应控制、系统控制、模式识别等领域取得成功应用。本文主要阐述了一般遗传算法的基本原理,概念以及运算步骤,其中具体的步骤主要有：1,收集彰武地区九佛堂组的油井测井资料,这些资料包括有涉及阿尔奇公式中的同一地层不同井位中的孔隙度,地层水饱和度,地层电阻率,以及岩心实测得到的含水饱和度等数据；2,确定阿尔奇参数组合它们各种的大致取值范围；3,根据参数的取值范围,建立关于公式中参数a、m、n由十进制到二进制的编码；4,确定算法的适应度函数即建立所求含水饱和度与岩心实测含水饱和度差值的绝对值最小值函数；5,随机生成50组由二进制代表的阿尔奇参数组合；5,确定遗传算子中关于交叉概率,变异概率的值；6,运用遗传算子对阿尔奇参数组合进行遗传操作,并根据事先设置好的终止进化代数结束算法的迭代过程,从而选择出最优的阿尔奇参数组合；7,将选择出的参数组合解码为我们熟知的十进制,完成算法择优过程。然而一般的遗传算法往往存在容易陷入局部最优解的缺陷,本文提出了对算法的改进步骤,即在第6步中,当完成一次遗传算子操作后,重新计算一次种群中个体的适应度,然后选取种群中适应度值排名前70%的个体,用随机新生成的30%的个体来代替种群中剩余的那些个体。这样做的的目的可以在保证遗传算法效率的同时,及时地引入新的个体,避免因为部分优势局部最优解个体占据种群使算法收敛于局部最佳的情况。极大的提高了算法的准确性。运用该算法能同时求得阿尔奇公式中的a、m、n值,根据公式中其他已知参数可以计算出该地区地层的的含水饱和度,通过把使用这些参数计算出的含水饱和度与传统图版的方法进行对比,证明该方法准确性更高,另外一方面,对比传统的遗传算法,发现改进的算法收敛于最佳时的遗传代数明显小与传统遗传算法,而相对误差也好于传统遗传算法。提示这种改进的遗传算法求解阿尔奇公式的a、m、n值准确性好且可行性高。适用于该地区的含水饱和度的预测。
[Abstract]:Generally, there are some quantitative parameters in the equation of log interpretation, such as the Archie formula for calculating formation water saturation and estimating oil and gas reservoir. Typically, these parameters are measured individually, or together, and when the obtained parameters are actually taken into actual production calculations, the results are often unsatisfactory. The correct selection of these parameters is closely related to the accuracy of the final interpretation. Genetic algorithm (GA) is a new optimization method with the characteristics of global optimization. It is suitable for solving nonlinear optimization problems. In recent years, genetic algorithm has been used to solve structural optimization design, adaptive control and system control in the optimization of complex functions. Pattern recognition and other fields have been successfully applied. This paper mainly describes the basic principle, concept and operation steps of the general genetic algorithm. The specific steps are: 1, collecting well logging data of Jiufutang formation in Zhangwu area. These data include porosity, formation water saturation, formation resistivity, and water saturation obtained from core measurements in different well locations of the same formation in the Archie formula. (2) determine the approximate range of values of the Archie parameter combinations, and establish the encoding of the parameter aqmn from decimal to binary according to the range of values of the parameters; 4. Determining the fitness function of the algorithm is to establish the absolute minimum value function of the difference between the calculated water saturation and the measured water saturation of the core, and randomly generate 50 sets of Archie parameter combinations represented by binary. 5. The value of crossover probability and mutation probability in genetic operator is determined. Genetic operator is used to perform genetic operation on the combination of Archie parameters, and the iterative process of the ending algorithm based on the pre-set termination of evolutionary algebra is given. Thus, the optimal Archive parameter combination is selected, and the selected parameter combination is decoded into the well-known decimal system to complete the optimization process of the algorithm. However, the general genetic algorithm often has the defect that it is easy to fall into the local optimal solution. In this paper, the improvement steps of the algorithm are put forward, that is, in step 6, the fitness of the individual in a population is re-calculated after the completion of a genetic operator operation. Then, the individuals with the first 70% fitness in the population were selected, and the remaining individuals in the population were replaced by the randomly generated 30% individuals. The purpose of this method is to ensure the efficiency of genetic algorithm and to introduce new individuals in time, so as to avoid the situation that the local optimal solution occupies the population and the algorithm converges to the local best. The accuracy of the algorithm is greatly improved. By using this algorithm, we can simultaneously get the value of Amim ~ n in Archie's formula. According to the other known parameters in the formula, we can calculate the water saturation of the strata in this area. By comparing the water saturation calculated by these parameters with the traditional chart plate method, it is proved that the method is more accurate. On the other hand, compared with the traditional genetic algorithm, It is found that the improved algorithm converges to the optimal genetic algebra obviously smaller than the traditional genetic algorithm, and the relative error is better than the traditional genetic algorithm. It is suggested that the improved genetic algorithm has good accuracy and feasibility in solving the Archie formula. It is suitable for the prediction of water saturation in this area.
【学位授予单位】：长江大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P618.13;P631.81
本文编号：2171929
[Abstract]:Generally, there are some quantitative parameters in the equation of log interpretation, such as the Archie formula for calculating formation water saturation and estimating oil and gas reservoir. Typically, these parameters are measured individually, or together, and when the obtained parameters are actually taken into actual production calculations, the results are often unsatisfactory. The correct selection of these parameters is closely related to the accuracy of the final interpretation. Genetic algorithm (GA) is a new optimization method with the characteristics of global optimization. It is suitable for solving nonlinear optimization problems. In recent years, genetic algorithm has been used to solve structural optimization design, adaptive control and system control in the optimization of complex functions. Pattern recognition and other fields have been successfully applied. This paper mainly describes the basic principle, concept and operation steps of the general genetic algorithm. The specific steps are: 1, collecting well logging data of Jiufutang formation in Zhangwu area. These data include porosity, formation water saturation, formation resistivity, and water saturation obtained from core measurements in different well locations of the same formation in the Archie formula. (2) determine the approximate range of values of the Archie parameter combinations, and establish the encoding of the parameter aqmn from decimal to binary according to the range of values of the parameters; 4. Determining the fitness function of the algorithm is to establish the absolute minimum value function of the difference between the calculated water saturation and the measured water saturation of the core, and randomly generate 50 sets of Archie parameter combinations represented by binary. 5. The value of crossover probability and mutation probability in genetic operator is determined. Genetic operator is used to perform genetic operation on the combination of Archie parameters, and the iterative process of the ending algorithm based on the pre-set termination of evolutionary algebra is given. Thus, the optimal Archive parameter combination is selected, and the selected parameter combination is decoded into the well-known decimal system to complete the optimization process of the algorithm. However, the general genetic algorithm often has the defect that it is easy to fall into the local optimal solution. In this paper, the improvement steps of the algorithm are put forward, that is, in step 6, the fitness of the individual in a population is re-calculated after the completion of a genetic operator operation. Then, the individuals with the first 70% fitness in the population were selected, and the remaining individuals in the population were replaced by the randomly generated 30% individuals. The purpose of this method is to ensure the efficiency of genetic algorithm and to introduce new individuals in time, so as to avoid the situation that the local optimal solution occupies the population and the algorithm converges to the local best. The accuracy of the algorithm is greatly improved. By using this algorithm, we can simultaneously get the value of Amim ~ n in Archie's formula. According to the other known parameters in the formula, we can calculate the water saturation of the strata in this area. By comparing the water saturation calculated by these parameters with the traditional chart plate method, it is proved that the method is more accurate. On the other hand, compared with the traditional genetic algorithm, It is found that the improved algorithm converges to the optimal genetic algebra obviously smaller than the traditional genetic algorithm, and the relative error is better than the traditional genetic algorithm. It is suggested that the improved genetic algorithm has good accuracy and feasibility in solving the Archie formula. It is suitable for the prediction of water saturation in this area.
【学位授予单位】：长江大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P618.13;P631.81
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