Geological Modeling Research On Oil And Gas Reservoir

Geological modeling is the core content of reservoir description technology and the keytechnology for establishing a quantitative geological model to improve the oil and gasproduction. At present, domestic workers are mainly used foreign commercial software tomodeling,but have very little study on the basic theory of modeling algorithms and thegeological interpretation of core parameters. On the basis of principles of cokriging,sequential Gaussian algorithm, truncated Gaussian algorithm and sequential indicatoralgorithm, this paper gives implementation steps of the proposed algorithm, dissectsgeological interpretation of crucial parameter in modeling and explores the applicability ofeach algorithms so as to improve the modeling accuracy of geological variables effectively.The main contents and conclusions of this paper are as follows:(1) On the basis of the calculation and fitting of variogram in experiments, the basicprinciples of cokriging in deterministic modeling are analyzed detailedly, and modeling stepsbased on covariogram are also given. Good experimental results and the improved estimationaccuracy are achieved by combining wells data with seismic data.(2) The continuous geological variables are modeled with sequential Gaussian algorithmin stochastic modeling. By comparing the modeling results with different parameters, thesetting method and geological interpretation of key parameters is given. The comparingresults with ordinary kriging show that this algorithm not only has less requirement formodeling parameters, but also can better reflect the distribution of reservoirs’ properties.(3) Because of the parameter distribution of reservoir is restricted by the spatialdistribution of sedimentary facies, truncated Gaussian simulation technique used for modelingdiscrete variables, is explored to get more elaborate attribute models. The implementationsteps of sedimentary facies modeling are abstracted from the point of view of mathematics,and the setting methods for the crucial parameters of models are summarized. Results showthat this algorithm is especially suitable for the geological environment with obvious characteristics of sedimentary facies, while the modeling results are easy distorted in localwith complex relationship of phase sequence.(4) The specific steps for modeling different microfacies with sequential indicatoralgorithm are explored. The modeling results of sedimentary facies are analyzed. Althoughthis algorithm needs to separately calculate the variogram of different facies which lead toslightly large amount of calculation, it can show the boundary of various types of facies forsedimentary facies with complex phase sequence and reflect the distribution of sedimentaryfacies clearly.