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.

Two-parameter prestack seismic inversion of porosity and pore structure parameter of fractured carbonate reservoirs: Part 1 — Methods

2018 ◽  
Vol 6 (4) ◽  
pp. SM1-SM8 ◽  
Author(s):  
Tingting Zhang ◽  
Yuefeng Sun
Keyword(s):  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
High Porosity ◽  
Structure Parameter ◽  
Pore Structure Parameter ◽  
Fractured Zones ◽  
Physics Model ◽  
Rock Physics Model

Fractured zones in deeply buried carbonate hills are important because they often have better permeability resulting in prolific production than similar low-porosity rocks. Nevertheless, their detection poses great challenge to conventional seismic inversion methods because they are mostly low in acoustic impedance and bulk modulus, hardly distinguishable from high-porosity zones or mudstones. A proxy parameter of pore structure defined in a rock-physics model, the so-called Sun model, has been used for delineating fractured zones in which the pore structure parameter is relatively high, whereas the porosity is low in general. Simultaneous seismic inversion of the pore structure parameter and porosity proves to be difficult and nontrivial in practice. Although the pore structure parameter is well-defined at locations where density, P-, and S-velocity are known from logs, estimation of P- and S-velocity information, especially density information from prestack seismic data is rather challenging. A three-step iterative inversion method, which uses acoustic, gradient, and elastic impedance from angle-stacked seismic data as input to the rock-physics model for calculating porosity and bulk and shear pore structure parameters simultaneously, is proposed and implemented to solve this problem. The methodology is successfully tested with well logs and seismic data from a deeply buried carbonate hill in the Bohai Bay Basin, China.

Estimating the amount of gas hydrate and free gas from marine seismic data

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 565-573 ◽  
Author(s):  
Christine Ecker ◽  
Jack Dvorkin ◽  
Amos M. Nur
Keyword(s):  
Gas Hydrate ◽  
Seismic Data ◽  
Rock Physics ◽  
Free Gas ◽  
Interval Velocity ◽  
Blake Ridge ◽  
Hydrate Saturation ◽  
Physics Model ◽  
Rock Physics Model ◽  
Marine Seismic

Marine seismic data and well‐log measurements at the Blake Ridge offshore South Carolina show that prominent seismic bottom‐simulating reflectors (BSRs) are caused by sediment layers with gas hydrate overlying sediments with free gas. We apply a theoretical rock‐physics model to 2-D Blake Ridge marine seismic data to determine gas‐hydrate and free‐gas saturation. High‐porosity marine sediment is modeled as a granular system where the elastic wave velocities are linked to porosity; effective pressure; mineralogy; elastic properties of the pore‐filling material; and water, gas, and gas‐hydrate saturation of the pore space. To apply this model to seismic data, we first obtain interval velocity using stacking velocity analysis. Next, all input parameters to the rock‐physics model, except porosity and water, gas, and gas hydrate saturation, are estimated from geologic information. To estimate porosity and saturation from interval velocity, we first assume that the entire sediment does not contain gas hydrate or free gas. Then we use the rock‐physics model to calculate porosity directly from the interval velocity. Such porosity profiles appear to have anomalies where gas hydrate and free gas are present (as compared to typical profiles expected and obtained in sediment without gas hydrate or gas). Porosity is underestimated in the hydrate region and is overestimated in the free‐gas region. We calculate the porosity residuals by subtracting a typical porosity profile (without gas hydrate and gas) from that with anomalies. Next we use the rock‐physics model to eliminate these anomalies by introducing gas‐hydrate or gas saturation. As a result, we obtain the desired 2-D saturation map. The maximum gas‐hydrate saturation thus obtained is between 13% and 18% of the pore space (depending on the version of the model used). These saturation values are consistent with those measured in the Blake Ridge wells (away from the seismic line), which are about 12%. Free‐gas saturation varies between 1% and 2%. The saturation estimates are extremely sensitive to the input velocity values. Therefore, accurate velocity determination is crucial for correct reservoir characterization.

Prediction of porosity and gas saturation for deep-buried sandstone reservoirs from seismic data using an improved rock-physics model

2019 ◽  
Vol 67 (2) ◽  
pp. 557-575 ◽  
Author(s):  
Yaneng Luo ◽  
Handong Huang ◽  
Morten Jakobsen ◽  
Yadi Yang ◽  
Jinwei Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Gas Saturation ◽  
Sandstone Reservoirs ◽  
Physics Model ◽  
Rock Physics Model

Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. MR1-MR13 ◽  
Author(s):  
Humberto S. Arévalo-López ◽  
Jack P. Dvorkin
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Rock Properties ◽  
Oil Reservoir ◽  
Controlled Experiments ◽  
Seismic Modeling ◽  
Physics Model ◽  
Rock Physics Model ◽  
Offshore Oil ◽  
Northwest Australia

Interpreting seismic data for petrophysical rock properties requires a rock-physics model that links the petrophysical rock properties to the elastic properties, such as velocity and impedance. Such a model can only be established from controlled experiments in which both groups of rock properties are measured on the same samples. A prolific source of such data is wellbore measurements. We use data from four wells drilled through a clastic offshore oil reservoir to perform rock-physics diagnostics, i.e., to find a theoretical rock-physics model that quantitatively explains the measurements. Using the model, we correct questionable well curves. Moreover, a crucial purpose of rock-physics diagnostics is to go beyond the settings represented in the wells and understand the seismic signatures of rock properties varying in a wider range via forward seismic modeling. With this goal in mind, we use our model to generate synthetic seismic gathers from perturbational modeling to address “what-if” scenarios not present in the wells.

Stochastic perturbation optimization for discrete-continuous inverse problems

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. M73-M83 ◽  
Author(s):  
Leonardo Azevedo ◽  
Dario Grana ◽  
Leandro de Figueiredo
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Stochastic Perturbation ◽  
Rock Physics ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Physics Model ◽  
Rock Physics Model

Accurate subsurface modeling and characterization require the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We have adopted an iterative simultaneous method, namely, stochastic perturbation optimization, to invert seismic reflection data jointly for facies and rock properties. Facies first are simulated according to a Markov chain model, and then rock properties are generated with stochastic sequential simulation and cosimulation conditioned to each facies. Elastic and seismic data are computed by applying a rock-physics model to the realizations of petrophysical properties and a seismic convolutional model. The similarity between observed and synthetic seismic data is used to update the solution by perturbing facies and rock properties until convergence. Coupling the discrete and continuous domains ensures a consistent perturbation of the reservoir models throughout the iterations. We have evaluated the method in a 1D synthetic example for the estimation of facies and porosity from zero-offset seismic data assuming a linear rock-physics model to demonstrate the validity of the method. Then, we apply the method to a real 3D data set from the North Sea for the joint estimation of facies and petrophysical properties from prestack seismic data. The results show spatially consistent rock and fluid inverted models in which the predicted facies reproduce the vertical ordering as observed in the well data.

scholarly journals Exploring trends in microcrack properties of sedimentary rocks: An audit of dry-core velocity-stress measurements

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. E193-E203 ◽  
Author(s):  
Doug A. Angus ◽  
James P. Verdon ◽  
Quentin J. Fisher ◽  
J.-M. Kendall
Keyword(s):  
Aspect Ratio ◽  
Sedimentary Rock ◽  
Crack Density ◽  
Sedimentary Rocks ◽  
Rock Physics ◽  
Initial Crack ◽  
Density Tensor ◽  
Physics Model ◽  
Rock Physics Model ◽  
Stress Dependent

Rock-physics models are used increasingly to link fluid and mechanical deformation parameters for dynamic elastic modeling. We explore the input parameters of an analytical stress-dependent rock-physics model. To do this, we invert for the stress-dependent microcrack parameters of more than 150 sedimentary rock velocity-stress core measurements taken from a literature survey. The inversion scheme is based on a microstructural effective-medium formulation defined by a second-rank crack-density tensor (scalar crack model) or by a second- and fourth-rank crack-density tensor (joint inversion model). Then the inversion results are used to explore and predict the stress-dependent elastic behavior of various sedimentary rock lithologies using an analytical microstructural rock-physics model via the initial modelinput parameters: initial crack aspect ratio and initial crack density. Estimates of initial crack aspect ratio are consistent among most lithologies with a mean of 0.0004, but for shales they differ up to several times in magnitude with a mean of 0.001. Estimates of initial aspect ratio are relatively insensitive to the inversion method, although the scalar crack inversion becomes less reliable at low values of normal-to-tangential crack compliance ratio [Formula: see text]. Initial crack density is sensitive to the degree of damage as well as the inversion procedure. An important implication is that the fourth-rank crack-density term is not necessarily negligible for most sedimentary rocks and evaluation of this term or [Formula: see text] is necessary for accurate prediction of initial crack density. This is especially important because recent studies suggest that [Formula: see text] can indicate fluid content in cracks.

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