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大民屯凹陷沙四段致密油测井评价方法

发布时间:2018-05-17 02:01  文章来源:笔耕文化传播

  本文选题:致密储层 + 岩性识别 ; 参考:《东北石油大学》2015年硕士论文


【摘要】:随着油田勘探开发程度的不断深入,复杂油气藏逐渐成为油田增储上产的主要来源。大民屯凹陷沙四段致密油储层属于低孔隙度、低渗透率的致密储层,岩性多样,孔隙结构复杂,泥质含量高,束缚水饱和度高。为后续的岩性划分及饱和度求取增加了难度。本文在岩电实验数据和测井资料的基础上,采用统计方法对大民屯凹陷沙四段致密油储层的岩性、物性和孔隙结构等特征进行了研究,表明该区块为岩性多样,物性差,孔隙结构复杂的致密储层。基于该区块不同岩性的地质及测井响应特征,将该区块致密油储层岩性划分为油页岩、粉砂岩和泥质云岩3类。通过统计方法对3类岩性测井曲线的敏感性进行分析,优选出声波时差、密度和自然伽马等测井曲线。基于敏感测井响应,分别建立了测井响应交会图岩性识别方法以及决策树和量子神经网络岩性识别模型,结果显示基于数据挖掘方法的决策树模型和量子神经网络模型的岩性识别效果优于传统的测井响应交会图法。基于岩心分析资料和测井资料,分别建立了致密砂岩储层、泥质云岩储层和油页岩的泥质含量、孔隙度等参数解释模型。基于岩电实验数据,结合致密砂岩储层、泥质云岩储层和油页岩特征研究成果,采用改进的等效岩石元素理论结合通用阿尔奇理论建立了大民屯凹陷沙四段致密砂岩储层导电模型,应用连通导电方程结合混合导电定律建立了大民屯凹陷沙四段泥质云岩储层导电模型,利用有效介质对称导电理论建立了大民屯凹陷沙四段油页岩导电模型。对模型中参数的敏感性进行了分析,利用岩电实验数据和测井资料确定了模型中的参数,并给出了模型求取含水饱和度的方法。利用本文所建立的参数解释模型和导电模型对该区块致密砂岩储层、泥质云岩储层和油页岩的实际测井资料进行了处理,结果表明,该模型能够很好地用于大民屯凹陷沙四段致密油储层的测井解释评价。
[Abstract]:With the deepening of exploration and development, complex reservoirs are becoming the main source of increasing reservoir production. The tight oil reservoir of the fourth member of Shahejie formation in Damingtun depression belongs to the tight reservoir with low porosity and low permeability. The lithology is diverse, the pore structure is complex, the content of mud is high, and the irreducible water saturation is high. It increases the difficulty for the subsequent lithologic division and saturation calculation. Based on the experimental data of rock and electricity and logging data, the lithology, physical properties and pore structure of the tight oil reservoir of the fourth member of Shahejie formation in Damingtun depression are studied by using statistical method. The results show that the block is of diverse lithology and poor physical property. Tight reservoir with complex pore structure. Based on the geological and logging response characteristics of different lithology in this block, the lithology of tight oil reservoir in this block is divided into three categories: oil shale, siltstone and shaly dolomite. The sensitivity of three kinds of lithologic logging curves is analyzed by statistical method, and acoustic moveout, density and natural gamma logging curves are selected. Based on the sensitive logging response, the lithologic identification method of cross plot, decision tree and quantum neural network lithology identification model are established, respectively. The results show that the lithology recognition effect of decision tree model and quantum neural network model based on data mining method is better than that of traditional well logging response cross plot method. Based on the core analysis data and logging data, the interpretation models of shale content and porosity of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale are established respectively. Based on the experimental data of rock and electricity, combined with the research results of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale, Based on the improved equivalent rock element theory and the universal Archie theory, a conductive model for the tight sandstone reservoir of the fourth member of Sha in the Damingtun Sag is established. The conductive model of shaly dolomite reservoir in the fourth member of Shahejie formation in Damingtun sag is established by using the connected conductive equation and the mixed conductive law. The oil shale conductive model of the fourth member of Shahejie formation in Damingtun sag is established by using the theory of symmetric conductivity of effective medium. The sensitivity of the parameters in the model is analyzed, the parameters in the model are determined by using the experimental data of rock electricity and logging data, and the method of calculating the saturation of water in the model is given. The actual logging data of tight sandstone reservoir, argillaceous dolomite reservoir and oil shale in this block are processed by using the parameter interpretation model and conductive model established in this paper. The results show that, The model can be applied to the logging interpretation and evaluation of the tight oil reservoirs in the fourth member of Shahejie formation in Damingtun Sag.
【学位授予单位】:东北石油大学
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:P618.13;P631.81

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