以伊朗Mansuri油田50口井的常规测井资料为基础,优选人工智能算法,对Mansuri油田白垩系Ilam组4个层的孔隙度和渗透率分布进行模拟。首先利用5口有岩心物性分析资料的井,遴选出常规测井的声波时差、密度和中子孔隙度作为输入参数,采用反向传播人工神经网络（BP神经网络）和支持向量回归方法进行储集层孔隙度和渗透率计算,根据计算结果与岩心实测结果的相关性,选择采用BP神经网络法进行物性计算。然后,利用克里金地质统计算法,对Mansuri油田Ilam组4个层的孔隙度和渗透率分布进行模拟,结果表明,层2.1和层2.2为高孔隙度层,层1、层2.1和和层2.2高渗透层,层3为非储集层;储集层孔隙度和渗透率分布总体呈北部高、南部低的特点。图4表2参10
ALI Dashti
,
EBRAHIM Sefidari
. 伊朗扎格罗斯地区Mansuri油田储集层物性模拟[J]. 石油勘探与开发, 2016
, 43(4)
: 559
-563
.
DOI: 10.11698/PED.2016.04.07
The porosity and permeability distribution in four layers of the Cretaceous Ilam Formation was simulated using optimized artificial intelligent algorithms based on conventional logging data of 50 wells in Mansuri oil field in Iran. First, the neutron porosity, interval transit time and density wireline logs in five key wells with core data were used as input parameters to calculate porosity and permeability of the reservoirs using backpropagation artificial neural network (BP neural network) and Support Vector Regression methods, and based on the correlation between the calculated results and the core tested results, BP neural network method was taken to do the physical property calculation. Then, the porosity and permeability distribution of the four layers were modeled using kriging geostatistical algorithms. The results show that Layers 2.1 and 2.2 are high in porosity, Layers 1, 2.1 and 2.2 are high in permeability, while Layer 3 is not reservoir; and the porosity and permeability are higher in the north and lower in the south on the whole.
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