Improving Petrophysical Analysis and Rock Physics Parameters Estimation Through Statistical Analysis of Basal Sands, Lower Indus Basin, Pakistan

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• 作者：Mureed Hussain ; Wang Yan Chun…
• 关键词：Statistical analysis ; Basal sand ; Percentage error ; Formation evaluation ; Petrophysical parameters ; Lower Indus Basin
• 刊名：Arabian Journal for Science and Engineering
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：42
• 期：1
• 页码：327-337
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Engineering, general; Science, Humanities and Social Sciences, multidisciplinary;
• 出版者：Springer Berlin Heidelberg
• ISSN：2191-4281
• 卷排序：42

文摘

Petrophysical parameters of a petroleum reservoir rock, used in formation evaluation, are generally estimated from wireline logs and core plugs. However, uncertainty or percentage error in quantitative interpretation of wireline logs and petrophysical parameters is generally not well quantified. Formation evaluation analysis and rock physics modeling are very useful techniques to link physical properties of reservoir interval extracted from wireline logs at well location with petrophysical, elastic and seismic properties of the reservoir interval far away from the well location. However, several types of uncertainty in the estimation of these parameters can affect this link seriously. In this work, a complete workflow is proposed to find out various petrophysical and rock physics parameters and to estimate percentage error in the extraction of these parameters. This work flow is based on traditional wireline log interpretation and is successfully applied on Basal sands of four wells drilled in the lower Indus Basin of Pakistan. An uncertainty analysis using statistical techniques is performed on different petrophysical parameters such as volume of shale, porosity, permeability estimated from wireline logs of these wells. Based on our statistical approach, linear and nonlinear regression relationships are developed between different petrophysical parameters. Furthermore, these parameters are used as input in rock physics model to predict elastic moduli and seismic velocities of the reservoir interval. The rock physics parameters such as velocities, densities and elastic moduli derived from well data are in close agreement to those predicted from rock physics model.