Generation And Optimization Of Training Image For Delta Front Reservoir

After many years of development,multi-point geostatistics has been successfully applied in river facies,which is more effective than traditional methods.In the delta front reservoir,the multi point geological statistical modeling method is less applied,because the multi-point geological statistical modeling needs training image as input,but the method of automatic establishment of the delta front reservoir training image is less.This paper studies the establishment of training image for the delta front reservoir.Taking several typical delta front reservoirs as the object,the reservoir geological knowledge base of delta front edge is established through the data of modern deposition,ancient outcrop,water trough experiment,satellite investigation and so on,and the distribution of mathematical and geometric parameters of different reservoir structure forms is determined.Based on the idea of hierarchical modeling,four methods of automatically establishing delta front reservoir training image are designed to form the training image database and solve the problem of delta front training image establishment.Training image,as an important modeling parameter in the multi-point geostatistics,directly determines the effect of modeling.It’s necessary to evaluate and select the candidate training image before using the multi-point geostatistical modeling.The overall repetition probability is not sufficient to describe the relationship of single data events in the training image,based on this understanding,this paper presents a new method to select the training image,as shown in the basic idea,the repetition probability distribution of a single data event is used to characterize the type and stationarity of the sedimentary pattern in the training image.the repetition probability mean value and deviation of single data event reflects the stationarity of the geological model of the training image,the rate of data event mismatching reflects the diversity of geological patterns in training images.The selection of optimal training image is achieved by combining the probability of repeated events and the probability of overall repetition of single data events.It’s illustrated in the simulation tests that a good training image has the advantages of high repetition probability compatibility,stable distribution of repeated probability of single data event,low probability mean value,low probability deviation and low rate of mismatching.The method can quickly select the training image and provide the basic guarantee for multi-point geostatistical simulations.Through the training image generation method and training image evaluation method,two examples were tested for multi-point effect,which better reproduced the underground geological situation.This paper has basically realized the integration of training image generation and optimal selection and evaluation,perfected and developed the theory and method of multi-point geostatistical modeling.It greatly reduces the difficulty of multi-point geostatistical modeling,improves the accuracy of modeling,and serves oil field production.