基于浅层地震的三维训练图像获取及应用

doi:10.11743/ogg20150619

摘要/Abstract

摘要： 多点地质统计学是当前研究的热点，其中训练图像的获取是关键环节，直接决定了储层模拟的精度和可靠程度。基于浅层高频地震信息，对浅层水道沉积的形态特征及定量规模展开研究，并通过相似性类比作为原型模型，指导深层油田区水道砂体形态规模统计，并在高精度反演数据基础上建立了具有代表性的定量化三维训练图像；以此为基础，借助petrel软件平台，通过设置训练图像不同网格大小，分析了其对多点地质统计模拟结果的影响。研究表明：该方法得到的训练图像真实可靠，模拟结果均忠实于井点数据，且砂体整体分布特征具有受训练图像约束的特点，当三维训练图像与实际模拟区网格大小一致时，模拟结果最能体现不同微相间的空间结构与几何特征。本文提供了一个训练图像获取的新思路，对具有相同地质条件的其他深水沉积微相类型的模拟具有借鉴作用。

关键词: 三维训练图像, 原型模型, 多点地质统计学, 深水水道, 储层模拟

Abstract: Multi-point geological statistics is one of the hot spots in current study,and the creation of training image is a key part determining the accuracy and reliability of reservoir simulation.Based on the shallow high frequency seismic data,the morphology and quantitative aspects of shallow channel deposits are studied and used as prototype models for further investigation of morphology and scale statistics of channel sand body-ies in deeper oil reservoiors.Quantitative three-dimensional training image is established on the basis of high-precision seismic inversion data.Training image grids are selected and Petrel is applied to analyze its impact on multi-point geological statistics.Results show that simulation is consistent with the well point data,and the spatial morphology and sizes of different channels are constrained by the quantitative characteristics of training image.Simulation results can best reveal the geometric characteristics and spatial confi-guration of sedimentary facies when the grid size of 3-D training image is consistent with the actual grid of simulation model.It provides a new method to create the training image,and may serve as a reference example for the simulation of other types of deep-water sedimentary facies.

Key words: three-dimensional training image, prototype model, multi-point geological statistics, deep-water channel, reservoir simulation

中图分类号:

P631

张文彪,段太忠,郑磊,等. 基于浅层地震的三维训练图像获取及应用[J]. 石油与天然气地质, 2015, 36(6): 1030-1037. doi: 10.11743/ogg20150619 Zhang Wenbiao,Duan Taizhong,Zheng Lei,et al. Generation and application of three-dimensional MPS training images based on shallow seismic data[J]. Oil & Gas Geology, 2015, 36(6): 1030-1037. doi: 10.11743/ogg20150619

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