本文選題：風南地區(qū) + 百口泉組�。� 參考：《西南石油大學》2017年碩士論文

【摘要】：準噶爾盆地風南地區(qū)百口泉組是新疆油田重點勘探層位,該區(qū)百口泉組砂礫巖儲集層具有以下特點:①儲集層巖性復雜,巖石粒徑跨度大且類型多樣;②儲集層孔隙結(jié)構(gòu)多樣,不同粒徑巖石的的儲集空間類型、孔滲關系復雜;③儲集層具有較強的非均質(zhì)性;研究區(qū)巖性的復雜性、儲集空間類型的多樣性以及儲集層的非均質(zhì)性造成儲集層測井響應規(guī)律性較差,準確識別巖性、分析儲集層主控因素、參數(shù)建模以及儲集層分類成為砂礫巖測井評價面臨的難題。本論文在分析研究區(qū)砂礫巖儲集層特征的基礎上,綜合運用常規(guī)測井、試油、錄井資料,并結(jié)合砂礫巖配套巖石物理實驗,從巖性識別、儲集層主控因素分析、儲集層參數(shù)建模、儲集層分類四方面進行測井評價。研究認為:①首先要對砂礫巖巖性重新分類,根據(jù)各類巖性巖石學特征差異及測井響應特征,構(gòu)建巖性敏感參數(shù)中子-密度孔隙度差并基于交會圖法聯(lián)合識別巖性;②儲集層巖石類型主要包括:粗砂巖、細礫巖、小中礫巖、大中礫巖;優(yōu)勢巖性為粗砂巖與細礫巖,黏土含量低,孔隙性、滲透性較好,小中礫巖與大中礫巖次之;研究區(qū)巖性分布規(guī)律與扇三角洲地質(zhì)沉積認識相吻合,.③砂礫巖儲集層具有較強的非均質(zhì)性,基于巖心刻度測井的思想,分巖性建立儲集層特征參數(shù)計算模型;運用阿爾奇公式(巖石物理實驗確定m、n值)計算含油飽和度;提取高壓壓汞敏感參數(shù)定量表征孔隙結(jié)構(gòu)優(yōu)劣。④多種理論方法與現(xiàn)場實際相結(jié)合,得出儲集層孔隙度下限是6.93%,滲透率下限為0.155mD;以儲集層單位厚度產(chǎn)量作為目標函數(shù)并結(jié)合孔隙度、滲透率、孔隙結(jié)構(gòu)表征參數(shù)構(gòu)建儲集層品質(zhì)因子,結(jié)合不同巖性特征參數(shù)值并在既定物性下限的基礎上對儲集層進行有效分類,最終得出各類儲集層分類標準。本文的完成為百口泉組砂礫巖儲集層巖性識別、主控因素分析、儲集層參數(shù)建模以及儲集層分類提供了一套行之有效的方法,對提高砂礫巖測井解釋精度具有重要意義。
[Abstract]:Baikouquan formation in Fengnan area of Junggar Basin is the key exploration horizon in Xinjiang Oilfield. The sand gravel reservoir of Baikouquan formation in this area has the following characteristics: complex lithology, large span of rock diameter and diverse pore structure of reservoir No. 2. The reservoir space types of different size rocks have strong heterogeneity of complex porosity and permeability relationship, and the complexity of lithology in the study area. The diversity of reservoir space types and reservoir heterogeneity result in poor logging response regularity, accurate identification of lithology, analysis of reservoir control factors, Parameter modeling and reservoir classification are difficult problems in sand gravel logging evaluation. On the basis of analyzing the characteristics of sand gravel reservoir in the study area, this paper synthetically uses the conventional logging, oil test, logging data, and combined with the physical experiment of sand gravel matching rock, analyzes the lithology identification and reservoir main controlling factors. Reservoir parameter modeling, reservoir classification four aspects of logging evaluation. It is considered that the first step is to reclassify the lithology of sandy conglomerate, and according to the difference of lithologic petrology and logging response, to construct the lithology sensitive parameter, the difference of neutron density porosity, and to identify lithology jointly based on cross plot method. (2) the reservoir rock types mainly include: coarse sandstone, fine conglomerate, small and medium conglomerate, large and medium conglomerate, the dominant lithology is coarse sandstone and fine conglomerate, the clay content is low, porosity, permeability is better, small and medium conglomerate and large and medium conglomerate take second place; The distribution of lithology in the study area coincides with the understanding of fan delta geological deposition. The reservoir has strong heterogeneity. Based on the idea of core calibration logging, the calculation model of reservoir characteristic parameters is established. The oil saturation is calculated by using Archie formula (determination of mnn value by rock physics experiment), and the quantitative characterization of pore structure by extracting sensitive parameters of high pressure mercury pressure is combined with field practice. It is concluded that the lower limit of reservoir porosity is 6.93 and the lower limit of permeability is 0.155mD.The reservoir quality factor is constructed by taking unit thickness of reservoir as objective function and combining porosity, permeability and pore structure characterization parameters. Combined with the values of different lithologic parameters and on the basis of the established lower limit of physical properties, the reservoir is effectively classified, and finally, the classification criteria of various reservoirs are obtained. The completion of this paper provides a set of effective methods for lithology identification, main control factor analysis, reservoir parameter modeling and reservoir classification in Baikouquan formation. It is of great significance to improve the interpretation accuracy of sand gravel logging.
【學位授予單位】：西南石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P618.13;P631.81

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