【摘要】：本文主要研究基于概率統(tǒng)計的儲層表征及其不確定性定量評價方法與儲層建模等相關內(nèi)容。研究內(nèi)容涉及儲層物性參數(shù)反演、儲層巖相識別及其不確定性定量評價分析與儲層地質(zhì)統(tǒng)計學建模等方面。本文針對儲層表征及建模過程中遇到的相關問題,提出了若干新策略,并結合模型數(shù)據(jù)與實際資料驗證方法的有效性。儲層物性信息是儲層評價的重要依據(jù),其通常由儲層彈性信息通過巖石物理關系反演獲得。由于巖石物理模型在數(shù)學關系上的復雜性,其反演目標函數(shù)通常具有較強的非線性,影響儲層物性參數(shù)反演精度;此外當彈性參數(shù)反演精度較低時,儲層含水飽和度信息預測難度通常較大。為此,方法結合蒙特卡洛模擬與智能優(yōu)化算法,通過在儲層物性參數(shù)空間大量隨機抽樣開展巖石物理正演與彈性參數(shù)對比分析實現(xiàn)樣本優(yōu)選,采用高斯模型統(tǒng)計儲層物性參數(shù)后驗概率信息。針對儲層彈性參數(shù)反演精度低導致含水飽和度預測難度較大的問題,通過統(tǒng)計分析儲層含水飽和度與孔隙度、泥質(zhì)含量之間的統(tǒng)計關系,在孔隙度和泥質(zhì)含量反演結果基礎之上利用上述統(tǒng)計關系近一步獲取儲層含水飽和度信息。實際工區(qū)資料應用驗證了方法的有效性。方法在儲層物性參數(shù)反演的基礎上開展了儲層巖相識別。針對通過地震資料預測儲層巖相分布信息不確定性較大的問題,本文采用基于概率統(tǒng)計的多步驟反演方法,在地震資料反演、井震尺度匹配、巖石物理建模以及測井巖相定義等環(huán)節(jié)建立輸入與輸出參數(shù)之間的概率統(tǒng)計關系,融合各環(huán)節(jié)概率信息獲取儲層巖相概率以表征地震巖相識別的不確定性。方法采用屬性交繪特征約束反演參數(shù)空間以降低概率矩陣的規(guī)模,從而提高算法運算效率并降低反演的不確定性。引入熵函數(shù)實現(xiàn)地震巖相識別不確定性的定量評價分析,通過求取各環(huán)節(jié)條件信息約束下的巖相概率及其熵值信息,方法量化分析其不確定性的傳遞規(guī)律與構成特征。針對儲層建模預測井間油藏屬性不確定性較大的問題,本文將地震巖相識別獲取的儲層巖相概率作為儲層巖相建模的約束信息,采用Tau模型融合測井與地震巖相概率等信息結合地質(zhì)統(tǒng)計學中的序貫指示模擬方法開展儲層巖相建模。通過在建模中引入地震巖相概率信息,儲層巖相建模結果的精度和穩(wěn)定性都獲得一定程度的提高,降低了建模的不確定性,為儲層精細表征提供了重要參考信息。
[Abstract]:In this paper, the reservoir characterization based on probability statistics and the quantitative evaluation method of uncertainty and reservoir modeling are studied. The research involves inversion of reservoir physical parameters, reservoir lithofacies identification, quantitative evaluation and analysis of uncertainty, and reservoir geostatistical modeling. In view of the problems encountered in reservoir characterization and modeling, some new strategies are proposed in this paper, and the validity of the verification method based on the model data and the actual data is presented. Reservoir physical property information is an important basis for reservoir evaluation, which is usually obtained by inversion of reservoir elastic information through rock physical relations. Because of the complexity of mathematical relation in rock physical model, the inversion objective function usually has strong nonlinearity, which affects the inversion accuracy of reservoir physical parameters. In addition, when the inversion accuracy of elastic parameters is low, it is usually difficult to predict reservoir water saturation information. For this reason, combining Monte Carlo simulation and intelligent optimization algorithm, a large number of random sampling in reservoir physical parameter space is carried out to carry out rock physics forward modeling and elastic parameter contrast analysis to realize sample optimal selection. Gao Si model is used to calculate the posterior probability information of reservoir physical parameters. In view of the difficulty of prediction of water saturation due to the low inversion accuracy of reservoir elastic parameters, the statistical relationship between water saturation and porosity, muddy content of reservoir is analyzed statistically. Based on the inversion results of porosity and muddy content, the information of reservoir water saturation is obtained by using the above statistical relation. The effectiveness of the method is verified by the application of practical area data. Methods based on the inversion of reservoir physical parameters, reservoir lithofacies identification was carried out. In order to solve the problem of uncertainty in predicting reservoir lithofacies distribution information by seismic data, a multi-step inversion method based on probability and statistics is used in this paper. The relationship between the input and output parameters is established in the links of rock physical modeling and log lithofacies definition. The reservoir lithofacies probability is obtained by combining the probabilistic information of each link to characterize the uncertainty of seismic lithofacies identification. In order to reduce the scale of probability matrix, attribute mapping feature constrained inversion parameter space is used to improve the efficiency of the algorithm and reduce the uncertainty of inversion. The entropy function is introduced to quantitatively evaluate and analyze the uncertainty of seismic lithofacies identification. By obtaining the probability and entropy information of lithofacies under the constraint of each link condition information, the transmission law and composition characteristics of uncertainty are quantitatively analyzed. In this paper, the probability of reservoir lithofacies obtained by seismic lithofacies identification is regarded as the constraint information of reservoir lithofacies modeling. Reservoir lithofacies modeling is carried out by using Tau model fusion logging and seismic lithofacies probability information combined with sequential indicator simulation method in geostatistics. By introducing seismic lithofacies probability information into the modeling, the accuracy and stability of reservoir lithofacies modeling results are improved to a certain extent, and the uncertainty of modeling is reduced, which provides important reference information for reservoir fine characterization.
【學位授予單位】：中國石油大學(北京)
【學位級別】：博士
【學位授予年份】：2016
【分類號】：P618.13

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