{"title":"利用井眼电图像测井为致密碳酸盐岩储层开发新型渗透率预测方法","authors":"Kun Meng, Hongyan Yu, Liyong Fan, Zhanrong Ma, Xiaorong Luo, Binfeng Cao, Yihuai Zhang","doi":"10.1190/geo2023-0609.1","DOIUrl":null,"url":null,"abstract":"Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates, resulting from sedimentation, diagenesis, and tectonic activity, present significant challenges. Based on borehole electrical image logging and fractal theory, we developed a method to calculate the fractal dimension of the porosity spectrum to characterise the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra were studied and fractal parameters were calculated, such as the left ( D f_left), middle ( D f_middle), and right fractal dimension ( D f_right). A permeability prediction model was proposed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that D f_left and permeability have a coefficient of determination (R2) of 0.78, whereas R2 between porosity and permeability is only 0.03. D f_middle and D f_right have little correlation with core permeability. The prediction results of the D f_left -based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates since D f_left corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing a novel permeability prediction method for tight carbonate reservoirs using borehole electrical image logging\",\"authors\":\"Kun Meng, Hongyan Yu, Liyong Fan, Zhanrong Ma, Xiaorong Luo, Binfeng Cao, Yihuai Zhang\",\"doi\":\"10.1190/geo2023-0609.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates, resulting from sedimentation, diagenesis, and tectonic activity, present significant challenges. Based on borehole electrical image logging and fractal theory, we developed a method to calculate the fractal dimension of the porosity spectrum to characterise the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra were studied and fractal parameters were calculated, such as the left ( D f_left), middle ( D f_middle), and right fractal dimension ( D f_right). A permeability prediction model was proposed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that D f_left and permeability have a coefficient of determination (R2) of 0.78, whereas R2 between porosity and permeability is only 0.03. D f_middle and D f_right have little correlation with core permeability. The prediction results of the D f_left -based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates since D f_left corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.\",\"PeriodicalId\":509604,\"journal\":{\"name\":\"GEOPHYSICS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GEOPHYSICS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1190/geo2023-0609.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GEOPHYSICS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1190/geo2023-0609.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
准确预测渗透率对于有效开采碳氢化合物至关重要,但由于沉积、成岩和构造活动,致密碳酸盐岩的孔隙结构错综复杂,这给我们带来了巨大挑战。基于井眼电图像测井和分形理论,我们开发了一种计算孔隙度谱分形维度的方法,以描述储层孔隙结构的复杂性。研究了孔隙度谱的分形特征,并计算了分形参数,如左分形维度(D f_left)、中分形维度(D f_middle)和右分形维度(D f_right)。通过研究分形参数与岩心渗透率之间的线性关系,提出了基于分形参数的渗透率预测模型。结果表明,D f_left 与渗透率的决定系数 (R2) 为 0.78,而孔隙度与渗透率之间的 R2 仅为 0.03。D f_middle 和 D f_right 与岩心渗透率的相关性很小。在现场应用中,基于 D f_left 的渗透率模型的预测结果与实验数据非常吻合,皮尔逊积矩相关系数为 0.93。我们的研究结果表明,大孔隙是致密碳酸盐岩渗透率的主要成因,因为 D f_left 相当于孔隙率谱的大孔隙部分。这项研究加深了我们对影响渗透率因素的理解,为预测致密碳酸盐岩储层的渗透率提供了有用的工具。
Developing a novel permeability prediction method for tight carbonate reservoirs using borehole electrical image logging
Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates, resulting from sedimentation, diagenesis, and tectonic activity, present significant challenges. Based on borehole electrical image logging and fractal theory, we developed a method to calculate the fractal dimension of the porosity spectrum to characterise the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra were studied and fractal parameters were calculated, such as the left ( D f_left), middle ( D f_middle), and right fractal dimension ( D f_right). A permeability prediction model was proposed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that D f_left and permeability have a coefficient of determination (R2) of 0.78, whereas R2 between porosity and permeability is only 0.03. D f_middle and D f_right have little correlation with core permeability. The prediction results of the D f_left -based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates since D f_left corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.