Petrofacies-Dependent Cementation Factor Relationship for Low-Resistivity Miocene Carbonates: Mumbai Offshore Basin

IF 2.1 4区 工程技术 Q3 ENERGY & FUELS SPE Reservoir Evaluation & Engineering Pub Date : 2022-11-01 DOI:10.2118/212854-pa
D. Mishra, Suraj Kumar, Vineet Mishra, Mohan Lal, V. Avadhani
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Abstract

In Mumbai offshore, Miocene carbonates are deposited with intermediate clastic inputs under cyclic sea level changes and have undergone diagenesis from time to time. Miocene carbonate layers deposited southwest of Mumbai High are producing a good amount of hydrocarbon from 1 to 2 Ω·m resistivity pays. A total of 58 representative core plugs from four different wells were studied to identify the reason for low resistivity and to classify rock facies types and porosity systems using scanning electron microscopy (SEM), thin-section nuclear magnetic resonance (NMR), and petrophysical core data. It was observed from the core study that Miocene carbonates have complex porosity systems and mud-supported to grain-supported reservoir facies. Dominance of mud-supported matrix is the main reason for low resistivity in Miocene carbonate layers as observed from integrated advanced log and core studies. Conventional petrophysical evaluation using constant petrophysical parameters (a, m, n) or linear correlation of cementation factor with porosity can lead to erroneous results in this scenario. A petrofacies-dependent correlation among cementation factor and porosity is attempted in this study for realistic evaluation of low-resistivity carbonate reservoirs. Different cementation factors vs. porosity relations have been derived for various carbonate formations worldwide. Shell formula demonstrates that cementation factor increases with decreasing porosity while correlation derived by Borai and Rafiee brought out inverse relation among cementation factors with porosity in tight carbonates and is providing almost constant cementation factor beyond 0.2. But, in our study, a core porosity-cementation factor plot of reservoir facies is showing that below 0.1, m values are increasing with increase of porosity, which is contradictory to Shell formula. This trend of cementation factor at low porosities is due to the presence of secondary porosity. In the porosity range 0.1–0.25, cementation factor increases eventually with the increase of porosity, but beyond porosity values 0.25, increase in porosity causes decrease of cementation factor. This is due to increasing content of mud-supported matrix, which is overall increasing the total porosity but eventually decreasing cementation in a rock. A new nonlinear correlation has been established between m and porosity for Miocene carbonates of Mumbai offshore area, by incorporating all the factors affecting cementation factor (m). Finally, saturation estimated using variable m either using newly established core derived correlation or resistivity image data is giving representative and improved saturation against low-resistivity reservoir layers compared with constant m.
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孟买海上盆地中新世低阻碳酸盐岩相胶结系数关系
在孟买近海,中新世碳酸盐沉积于海平面循环变化下的中间碎屑输入,并经历了多次成岩作用。孟买高地西南沉积的中新世碳酸盐岩层在1 ~ 2 Ω·m电阻率层产出大量油气。利用扫描电子显微镜(SEM)、薄层核磁共振(NMR)和岩石物理岩心数据,研究了来自4口不同井的58个代表性岩心桥塞,以确定低电阻率的原因,并对岩石相类型和孔隙系统进行了分类。岩心研究表明,中新世碳酸盐岩具有复杂的孔隙系统,储集相为泥—粒支撑型。综合测井和岩心研究发现,中新世碳酸盐岩地层电阻率低的主要原因是泥质支撑基质。在这种情况下,常规的岩石物理评价采用恒定的岩石物理参数(a、m、n)或胶结系数与孔隙度的线性相关性,可能会导致错误的结果。为了更好地评价低电阻率碳酸盐岩储层,本研究尝试建立胶结系数与孔隙度的相关联关系。对于世界范围内不同的碳酸盐岩地层,已经推导出了不同的胶结系数与孔隙度的关系。Shell公式表明胶结系数随孔隙度的减小而增大,而Borai和Rafiee推导的相关性表明胶结系数与孔隙度呈反比关系,胶结系数在0.2以上基本不变。但是,在我们的研究中,储层相岩心孔隙度-胶结系数图显示,在0.1以下,m值随着孔隙度的增加而增加,这与Shell公式相矛盾。低孔隙度下胶结系数的变化趋势是由于次生孔隙的存在。在孔隙度0.1 ~ 0.25范围内,随着孔隙度的增大,胶结系数最终增大,超过孔隙度0.25,孔隙度增大导致胶结系数减小。这是由于泥浆支撑基质含量的增加,总体上增加了总孔隙度,但最终减少了岩石中的胶结性。通过综合影响胶结系数(m)的所有因素,建立了孟买近海地区中第三系中第三系碳酸盐岩m与孔隙度之间的非线性相关性。最后,利用新建立的岩心推导相关性或电阻率图像数据,利用变量m估算的饱和度与恒定m相比,具有代表性和改善了低电阻率储层的饱和度。
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
68
审稿时长
12 months
期刊介绍: Covers the application of a wide range of topics, including reservoir characterization, geology and geophysics, core analysis, well logging, well testing, reservoir management, enhanced oil recovery, fluid mechanics, performance prediction, reservoir simulation, digital energy, uncertainty/risk assessment, information management, resource and reserve evaluation, portfolio/asset management, project valuation, and petroleum economics.
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