河流相油气储层的井震结合相控随机建模约束方法

The constraining method on stochastic modeling for fluvial petroleum reservoir controlled by depositional facies integrating wells with seismic data

河流相油气储层的研究传统上多是只依据井点资料,先在井上进行沉积相的划分,而后进行剖面相的分析,最后再结合平面沉积参数等值线图编制平面相图,这样往往会造成"见砂画河,吾跟勘探走"的局面,这种平面相图在井间可能存在着较大的误差。然而,平面相图的正确与否直接影响着储层建模中相控的结果。为此作者提出了一种井震结合进行沉积相图编制的新方法,即"以河找砂,指导勘探行"的思路,并在此基础上进行分层次地相控约束随机建模。同时提出相控建模的三个基本的约束条件,即首先要保证随机建模模型的"相序"符合地质规律;其次要保证建模实现的微相分布统计概率与单井沉积微相数据离散化至三维网格后的统计概率相一致;第三要确保三维数据中每种微相的变差函数特征与定量地质知识库一致。因而,从沉积形成与演化的成因角度来指导沉积储层随机建模过程,应用多参数协同、分层次约束的方法,以河道的平面展布和垂向演化来控制建模的结果,使其更逼近地下地质的真实。

Most of conventional study on depositional facies of fluvial petroleum reservoir was only on the basis of well data, firstly, the depositional microfacies in wells have been divided, then the sections of depositional microfacies from well to well have been correlated and the map in plane finally has been set up on the contours of sedimentary parameter. This indicates that channel being mapped only depends on whether or not sandstone in each well and the geologists follow the course of petroleum exploration. However, the predication of sand- stone among wells can have more errors. The map of depositional facies whether it is correct directly affects the result of stochastic modeling controlled by facies and petroleum exploration. Therefore a new mapping method of depositional facies to integrate wells with seismic data has been put forward. Namely, to find sand- bodies depends on the distribution of fluvial system and its result can help to guide the course of petroleum ex- ploration. The stochastic modeling controlled by facies on this basis of this map has been constrained step by step in hierarchy. At the same time, three fundamental constraining conditions controlled by facies have seen proposed, those are as follows： Firstly, to ensure ＂the order of facies＂ in stochastic modeling is consistent with geological rule. Secondly, to ensure the statistic probability of each microfacies in the realization of stochastic modeling is consistent with it after the data of microfacies in each well being dispersed into 3D grid. Thirdly, to ensure the variogram of each microfacies in 3D is consistent with the quantitive geological knowledge database. Hence, the modeling constrained by facies is characterized by depositional formation and genetic evolution as guidance of the stochastic modeling, process for depositional reservoir, so that the results of modeling will be obtained to be very close upon the reality of subsurface geology, using the coordination of multiparameter and constraint by hierarchy,, as well as horizontal distribution and vertical evolvement of channel as constraint boundary.