【摘要】：大牛地氣田位于鄂爾多斯盆地,屬于典型的低孔、低滲致密砂巖氣藏,其勘探及開發(fā)越來越受到重視,致密砂巖氣藏儲層參數(shù)的測井解釋模型建立一直是測井解釋的難點。由于致密砂巖氣藏與常規(guī)砂巖相比有很大的不同,孔隙度和滲透率相對較低,孔隙結(jié)構(gòu)復(fù)雜,儲層的儲集性能也相對較差,這些特點都會給致密含氣砂巖儲層的測井解釋方法研究帶來困難。本文首先進行巖心歸位,充分利用已有的測井資料、巖心資料、物性等資料,在對儲層的巖石學(xué)、物性、電性特征進行深入研究的基礎(chǔ)上,針對研究區(qū)致密砂巖儲層低孔、低滲的特征,系統(tǒng)的對儲層四性關(guān)系進行了研究,明確了儲層物性的主要控制因素,為測井解釋模型的建立打下了基礎(chǔ)。為了更加準(zhǔn)確的求取儲層參數(shù),本文采用分層位、分巖性來建立儲層參數(shù)測井解釋模型,對于砂巖類型的劃分,采用了神經(jīng)網(wǎng)絡(luò)判別來進行巖性識別,取得了較好的分類效果。對于儲層參數(shù)的求取,通過使用巖心資料采用了多種統(tǒng)計方法建立了物性參數(shù)模型,包括線性一元、多元回歸以及非線性的BP神經(jīng)網(wǎng)絡(luò)和支持向量機。通過對多種模型的效果對比,選出適合研究區(qū)的測井解釋方法,最后對解釋模型效果進行驗證,證明能夠挖掘致密砂巖儲層電性和物性間非線性關(guān)系的支持向量機方法預(yù)測儲層物性參數(shù)有較好的預(yù)測效果,提高了大牛地致密砂巖氣藏儲層參數(shù)測井解釋模型的精度。
[Abstract]:Daniudi gas field is located in Ordos Basin, which belongs to typical low porosity and low permeability tight sandstone gas reservoirs. More and more attention has been paid to its exploration and development. The establishment of logging interpretation model of tight sandstone gas reservoir parameters has always been a difficult point in logging interpretation. Because the tight sandstone gas reservoir is very different from the conventional sandstone, the porosity and permeability are relatively low, the pore structure is complex, and the reservoir performance of the reservoir is relatively poor. These characteristics will bring difficulties to the study of logging interpretation methods for tight gas-bearing sandstone reservoirs. First of all, this paper makes full use of the existing logging data, core data, physical properties and other data, on the basis of in-depth study of the petrology, physical properties and electrical characteristics of the reservoir, aiming at the low porosity of tight sandstone reservoir in the study area. The characteristics of low permeability and the systematic study of the four characteristics of reservoir are carried out, and the main controlling factors of reservoir physical properties are clarified, which lays a foundation for the establishment of logging interpretation model. In order to obtain reservoir parameters more accurately, this paper uses stratification and lithology to establish logging interpretation model of reservoir parameters. For the classification of sandstone types, neural network discrimination is used to identify lithology, and good classification effect is obtained. For the calculation of reservoir parameters, various statistical methods are used to establish physical parameter models, including linear univariate, multiple regression, nonlinear BP neural network and support vector machine. Through the comparison of the effects of various models, the logging interpretation method suitable for the study area is selected, and finally the effect of the interpretation model is verified. It is proved that the support vector machine method, which can excavate the nonlinear relationship between electrical property and physical properties of tight sandstone reservoir, has a good prediction effect on reservoir physical parameters, and improves the accuracy of logging interpretation model of reservoir parameters in Daniudi tight sandstone gas reservoir.
【學(xué)位授予單位】：中國石油大學(xué)(華東)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P618.13;P631.81

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