Enabling Pore Pressure Prediction in Reservoirs by Replacing the Normal Compaction Trend by a Full Rock Physics Model

2017 ◽  
Author(s):  
Pascal Debec
Keyword(s):  
Pore Pressure ◽  
Rock Physics ◽  
Pore Pressure Prediction ◽  
Physics Model ◽  
Rock Physics Model

Improving hydrocarbon exploration with pore pressure assisted earth-model building

2018 ◽  
Vol 6 (3) ◽  
pp. SG41-SG47
Author(s):  
Yangjun (Kevin) Liu ◽  
Michael O’Briain ◽  
Cara Hunter ◽  
Laura Jones ◽  
Emmanuel Saragoussi
Keyword(s):  
Pore Pressure ◽  
Initial Velocity ◽  
Model Building ◽  
Rock Physics ◽  
Velocity Model ◽  
Hydrocarbon Exploration ◽  
Earth Model ◽  
Well Control ◽  
Physics Model ◽  
Rock Physics Model

In shale-dominated clastic lithology environments, a rock-physics model relating velocity and pore pressure (PP) can be calibrated and used to convert velocity to PP properties. The crossvalidation between velocity and overpressure, which follows the geology, can be used to better understand the model, help to build an initial velocity model, and allow selecting tomography solutions with more confidence. The velocity model developed using this approach is more plausible and more suitable for subsequent PP analysis. We highlight the application of this method in areas with poor seismic illumination and insufficient well control.

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.

scholarly journals The rock physics model in the unconsolidated methane hydrate bearing sediments

2010 ◽  
Vol 75 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Takao Inamori ◽  
Masami Hato ◽  
Kiyofumi Suzuki ◽  
Tatsuo Saeki
Keyword(s):  
Methane Hydrate ◽  
Rock Physics ◽  
Physics Model ◽  
Rock Physics Model ◽  
Hydrate Bearing Sediments

In-situ stress calculation and application on the basis of anisotropic rock physics model

2016 ◽  
Author(s):  
Wang Changsheng* ◽  
Shi Yujiang ◽  
Wang Daxing ◽  
Zhang Haitao
Keyword(s):  
Rock Physics ◽  
In Situ Stress ◽  
Anisotropic Rock ◽  
Stress Calculation ◽  
Physics Model ◽  
Rock Physics Model

Rock physics model‐based seismic inversion

2006 ◽  
Author(s):  
Kyle Spikes ◽  
Jack Dvorkin ◽  
Gary Mavko
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Model Based ◽  
Physics Model ◽  
Rock Physics Model
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