CAJ | 학술논문

冲积扇储层具有典型的非平稳分布特征，传统的基于平稳假设的地质统计建模方法无法准确模拟其沉积微相。在充分分析其沉积微相分布特征的基础上，以克拉玛依油田三叠系克下组冲积扇储层为例，综合现代沉积、密井网、露头等信息，应用基于地质矢量信息的多点地质统计学方法建立了精细、合理的储层沉积微相三维模型。首先，综合现代沉积特征与地下储层沉积微相解剖成果，应用“模式指导、规模约束”的方法，建立符合研究区沉积模式和定量规模的训练图像和基于冲积扇沉积体系的矢量坐标系统，表征了训练图像的地质矢量信息，提出基于预模拟实现的模拟域地质矢量信息表征方法，建立了待模拟地质体的三维矢量信息模型；最后，采用基于矢量信息的多点地质统计方法建立了冲积扇储层沉积微相模型，并采用多种标准对模拟实现进行可靠性分析。模拟结果表明冲积扇相带分布符合地质认识，沉积微相的形态、展布特征、组合样式、规模与训练图像、地下沉积微相解剖认识相符。
충적선저층구유전형적비평은분포특정，전통적기우평은가설적지질통계건모방법무법준학모의기침적미상。재충분분석기침적미상분포특정적기출상，이극랍마의유전삼첩계극하조충적선저층위례，종합현대침적、밀정망、로두등신식，응용기우지질시량신식적다점지질통계학방법건립료정세、합리적저층침적미상삼유모형。수선，종합현대침적특정여지하저층침적미상해부성과，응용“모식지도、규모약속”적방법，건립부합연구구침적모식화정량규모적훈련도상화기우충적선침적체계적시량좌표계통，표정료훈련도상적지질시량신식，제출기우예모의실현적모의역지질시량신식표정방법，건립료대모의지질체적삼유시량신식모형；최후，채용기우시량신식적다점지질통계방법건립료충적선저층침적미상모형，병채용다충표준대모의실현진행가고성분석。모의결과표명충적선상대분포부합지질인식，침적미상적형태、전포특정、조합양식、규모여훈련도상、지하침적미상해부인식상부。
As alluvial fan reservoir is typical in non-stationary distribution, the traditional modeling algorithm based on the steady-state assumption cannot simulate it accurately. On the basis of full analysis of the reservoir distribution, a case study of alluvial fan reservoir on the Triassic Lower Karamay Formation, Yizhong Area, Karamay Oilfield was carried out to integrate modern sediments, underground dense well group, outcrops and other information, using the latest Vector Information based Multiple-Point Simulation to reproduce a fine and reasonable three-dimensional geological model of reservoir architecture, which can be used as a reference for similar reservoir stochastic simulations. First, based on modern sediment and underground alluvial fan reservoir architecture analysis, an alluvial fan training image in line with the study area was constructed under the instruction of pattern guiding and scale constraints. Second, the vector coordinate system of an alluvial fan was constructed. Then the vector information of both training image and the target modeling area was characterized in this system. Finally, the reservoir architecture was reproduced by using the Vector information based on the Multiple-Point Simulation method. The reliability analysis of the realizations was performed. The result shows that the realizations are faithful to well data. Alluvial fan facies distribution in the model is suitable to geological model. Architecture element characteristics including shape configuration, distribution, combination style and scales matches training image and understanding reservoir architecture acknowledgment perfectly.