A shale rock physics model and its application in the prediction of brittleness index, mineralogy, and porosity of the Barnett Shale

2012 ◽  
Author(s):  
Zhiqi Guo ◽  
Mark Chapman ◽  
Xiangyang Li
Keyword(s):  
Rock Physics ◽  
Brittleness Index ◽  
Barnett Shale ◽  
Shale Rock ◽  
Physics Model ◽  
Rock Physics Model

scholarly journals A shale rock physics model for analysis of brittleness index, mineralogy and porosity in the Barnett Shale

2013 ◽  
Vol 10 (2) ◽  
pp. 025006 ◽  
Author(s):  
Zhiqi Guo ◽  
Xiang-Yang Li ◽  
Cai Liu ◽  
Xuan Feng ◽  
Ye Shen
Keyword(s):  
Rock Physics ◽  
Brittleness Index ◽  
Barnett Shale ◽  
Shale Rock ◽  
Physics Model ◽  
Rock Physics Model

scholarly journals The construction of shale rock physics model and brittleness prediction for high-porosity shale gas-bearing reservoir

2020 ◽  
Vol 17 (3) ◽  
pp. 658-670 ◽  
Author(s):  
Xin-Peng Pan ◽  
Guang-Zhi Zhang ◽  
Jiao-Jiao Chen
Keyword(s):  
Shale Gas ◽  
Rock Physics ◽  
High Porosity ◽  
Shale Rock ◽  
Physics Model ◽  
Rock Physics Model ◽  
Gas Bearing

Analyzing the anisotropic characteristics of shale brittleness based on shale rock physics model

2017 ◽  
Author(s):  
Keran Qian ◽  
Zhiliang He ◽  
Xiwu Liu ◽  
Yequan Chen ◽  
Xiangyang Li
Keyword(s):  
Rock Physics ◽  
Shale Rock ◽  
Physics Model ◽  
Rock Physics Model

Brittleness index and seismic rock physics model for anisotropic tight-oil sandstone reservoirs

2015 ◽  
Vol 12 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Xin-Rui Huang ◽  
Jian-Ping Huang ◽  
Zhen-Chun Li ◽  
Qin-Yong Yang ◽  
Qi-Xing Sun ◽  
...  
Keyword(s):  
Rock Physics ◽  
Brittleness Index ◽  
Tight Oil ◽  
Sandstone Reservoirs ◽  
Physics Model ◽  
Rock Physics Model

scholarly journals Construction of a novel brittleness index equation and analysis of anisotropic brittleness characteristics for unconventional shale formations

2019 ◽  
Vol 17 (1) ◽  
pp. 70-85
Author(s):  
Ke-Ran Qian ◽  
Tao Liu ◽  
Jun-Zhou Liu ◽  
Xi-Wu Liu ◽  
Zhi-Liang He ◽  
...  
Keyword(s):  
Random Distribution ◽  
Rock Physics ◽  
Brittleness Index ◽  
Anisotropic Rock ◽  
Shale Formations ◽  
Shale Formation ◽  
Dip Angle ◽  
Physics Model ◽  
Rock Physics Model ◽  
Two Parameters

Abstract The brittleness prediction of shale formations is of interest to researchers nowadays. Conventional methods of brittleness prediction are usually based on isotropic models while shale is anisotropic. In order to obtain a better prediction of shale brittleness, our study firstly proposed a novel brittleness index equation based on the Voigt–Reuss–Hill average, which combines two classical isotropic methods. The proposed method introduces upper and lower brittleness bounds, which take the uncertainty of brittleness prediction into consideration. In addition, this method can give us acceptable predictions by using limited input values. Secondly, an anisotropic rock physics model was constructed. Two parameters were introduced into our model, which can be used to simulate the lamination of clay minerals and the dip angle of formation. In addition, rock physics templates have been built to analyze the sensitivity of brittleness parameters. Finally, the effects of kerogen, pore structure, clay lamination and shale formation dip have been investigated in terms of anisotropy. The prediction shows that the vertical/horizontal Young’s modulus is always below one while the vertical/horizontal Poisson’s ratio (PR) can be either greater or less than 1. Our study finds different degrees of shale lamination may be the explanation for the random distribution of Vani (the ratio of vertical PR to horizontal PR).

Multivariate probabilistic rock physics models using Kumaraswamy distributions

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Dario Grana
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Random Variables ◽  
Rock Physics ◽  
Petrophysical Properties ◽  
Kumaraswamy Distribution ◽  
Model Predictions ◽  
Physics Model ◽  
Rock Physics Model

Rock physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock physics models to deterministic measurements, I propose to apply the models to the probability density function of the measurements. This approach has been previously adopted in literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the non-symmetric behavior. The proposed approach is based on the Kumaraswamy probability density function for continuous random variables, which allows modeling double bounded non-symmetric distributions and is analytically tractable, unlike the Beta or Dirichtlet distributions. I present a probabilistic rock physics model applied to double bounded continuous random variables distributed according to a Kumaraswamy distribution and derive the analytical solution of the posterior distribution of the rock physics model predictions. The method is illustrated for three rock physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksoz inclusion model.

Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter – An application to a tight-sand reservoir

2021 ◽  
pp. 1-59
Author(s):  
Kai Lin ◽  
Xilei He ◽  
Bo Zhang ◽  
Xiaotao Wen ◽  
Zhenhua He ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Structure Parameters ◽  
Pore Structure Parameter ◽  
Elastic Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Porosity Estimation

Most of current 3D reservoir’s porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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