{"title":"Permeability Upscaling Conversion Based on Reservoir Classification","authors":"Jiali Li, Chuqiao Gao, Bin Zhao, Xincai Cheng","doi":"10.3390/pr12081653","DOIUrl":null,"url":null,"abstract":"Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and low-permeability formations present highly discrete and variable core data points for porosity and permeability, rendering single-variable regression models ineffective. Consequently, accurately representing permeability in heterogeneous reservoirs proves challenging. In the following study, lithological and physical property data are integrated with mercury injection data to analyze pore structure types. The formation flow zone index (FZI) is utilized to differentiate reservoir types, and permeability is calculated based on core porosity–permeability relationships from logging data for each flow unit. Subsequently, the average permeability for each flow unit is computed according to reservoir classification, followed by a weighted average according to effective thickness. This approach transforms logging permeability into drill stem test permeability. Unlike traditional point-by-point averaging methods, this approach incorporates reservoir thickness and heterogeneity, making it more suitable for complex reservoir environments and resulting in more reasonable conversion outcomes.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Processes","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/pr12081653","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Deep and ultra-deep reservoirs are characterized by low porosity and permeability, pronounced heterogeneity, and complex pore structures, complicating permeability evaluations. Permeability, directly influencing the fluid production capacity of reservoirs, is a key parameter in comprehensive reservoir assessments. In the X Depression, low-porosity and low-permeability formations present highly discrete and variable core data points for porosity and permeability, rendering single-variable regression models ineffective. Consequently, accurately representing permeability in heterogeneous reservoirs proves challenging. In the following study, lithological and physical property data are integrated with mercury injection data to analyze pore structure types. The formation flow zone index (FZI) is utilized to differentiate reservoir types, and permeability is calculated based on core porosity–permeability relationships from logging data for each flow unit. Subsequently, the average permeability for each flow unit is computed according to reservoir classification, followed by a weighted average according to effective thickness. This approach transforms logging permeability into drill stem test permeability. Unlike traditional point-by-point averaging methods, this approach incorporates reservoir thickness and heterogeneity, making it more suitable for complex reservoir environments and resulting in more reasonable conversion outcomes.
深层和超深层储层的特点是孔隙度和渗透率低、异质性明显、孔隙结构复杂,这使得渗透率评估变得复杂。渗透率直接影响储层的产液能力,是储层综合评估的关键参数。在 X 凹陷,低孔隙度和低渗透率地层的孔隙度和渗透率的岩心数据点高度离散且多变,使得单一变量回归模型无法发挥作用。因此,在异质储层中准确表示渗透率具有挑战性。在以下研究中,将岩性和物性数据与注汞数据相结合,分析孔隙结构类型。利用地层流动带指数(FZI)来区分储层类型,并根据测井数据中每个流动单元的岩心孔隙度-渗透率关系来计算渗透率。随后,根据储层分类计算每个流动单元的平均渗透率,再根据有效厚度进行加权平均。这种方法将测井渗透率转化为钻杆测试渗透率。与传统的逐点平均法不同,这种方法包含了储层厚度和异质性,因此更适合复杂的储层环境,转换结果也更合理。
期刊介绍:
Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.