Petrophysical Modeling in Reservoir Simulation Quiz

Petrophysical modelling concerns the distribution of reservoir properties, such as porosity and water saturation, which are essential for calculating volumetrics and for input into reservoir simulation for permeability assessment.

The 'Distributions' tab includes components such as user mode, Kriging info, modelling mode, declustering, and transformation types, including basic and skewness transformations. It also incorporates geological trends, transformation sequences, and other simulation settings.

The 'Variograms' tab involves features like variogram definition, previewer, anisotropy, standard deviation (Variogram Sill), variogram model, anisotropy directions, range, and the use of data analysis to define the variogram, including estimation of azimuth and variogram modelling.

<p>The 'Water Saturation Modelling' involves the general and variables tabs, with functions and variables for defining water saturation. It is an important aspect for reservoir engineering as it contributes to the accurate assessment of reservoir properties and simulation.</p> Signup and view all the answers

<p>The Box-Cox transformation is used to stabilize the variance and make the data more closely follow a normal distribution.</p> Signup and view all the answers

<p>The Normal score transformation maps the data onto a Gaussian cumulative probability function by subtracting the mean and normalizing by the standard deviation, making the data closely resemble a normal distribution.</p> Signup and view all the answers

<p>The Logarithm transformation should be used when the distribution of the data has a lognormal skew, such as in the case of permeability data.</p> Signup and view all the answers

<p>The Compactional depth trend transformation accounts for petrophysical properties changing with depth due to diagenetic processes related to TVD, commonly seen in porosity and permeability decreasing with depth due to compaction and cementation.</p> Signup and view all the answers

<p>The Depositional depth trend transformation accounts for trends resulting from deposition and their relationship to stratigraphic depth, such as fining upward sequences.</p> Signup and view all the answers

<p>An intrabody trend defines a change in petrophysical property within an individual facies body, often used to honor good porosity close to sediment source and poorer porosity with increasing distance from the source.</p> Signup and view all the answers

<p>The General 3D trend transformation uses an existing continuous parameter to describe the expected value in each cell, often using a scaling coefficient to re-scale the parameter to match the value range of the parameter to be modeled.</p> Signup and view all the answers

<p>The Cloud transform calculates the 2D probability density function for the 3D parameter and the well log parameter, providing an easy-to-use solution for establishing the relationship between parameters and modeling the 3D parameter.</p> Signup and view all the answers

<p>The Normal score transformation should be used when the data should be mapped onto a Gaussian cumulative probability function, typically when the data contains at least 200 values and has no large peaks or holes.</p> Signup and view all the answers

<p>The 1D Lateral trend transformation accounts for trends in world coordinates, such as UTM coordinates.</p> Signup and view all the answers

<p>Skewness refers to the lack of symmetry in a distribution, and certain data transformation options are available to address skewness and make the data more symmetrical.</p> Signup and view all the answers

<p>The Square root transformation is used when the data distribution exhibits a specific type of skewness, helping to make the distribution more symmetrical.</p> Signup and view all the answers

<p>Manually, estimated from input data, or copied from a previously defined specification.</p> Signup and view all the answers

<p>They allow grouping of zones, reuse of data, and inclusion of analogue data, but may create workflow confusion.</p> Signup and view all the answers

<p>It affects the quality and speed of the simulation.</p> Signup and view all the answers

<p>It transforms input data into a Gaussian distribution for simulation and back to the original data after simulation. It is crucial for transforming data for simulation.</p> Signup and view all the answers

<p>They affect the choice of Kriging and modeling modes.</p> Signup and view all the answers

<p>It details the type of kriging used, automatically selected by RMS based on user mode and other factors.</p> Signup and view all the answers

<p>The modeling mode can be switched to 'Constant' or used for prediction or simulation based on the General tab selection.</p> Signup and view all the answers

<p>It is used to account for spatial correlation and weighting of well data in concentrated areas.</p> Signup and view all the answers

<p>There are six transformation categories, including basic transformations like truncating data and realizations.</p> Signup and view all the answers

<p>It needs to account for non-normal distributions and spatial trends.</p> Signup and view all the answers

<p>It is available only if seismic cosimulation is selected, and incomplete seismic parameters can be simulated or overwritten.</p> Signup and view all the answers

<p>It is crucial for transforming data into a Gaussian distribution for simulation and back to the original data after simulation. It facilitates the transformation of data for simulation.</p> Signup and view all the answers

<p>The petrophysical model aims to add detail to the facies model in a geologically realistic manner, characterizing reservoir quality.</p> Signup and view all the answers

<p>Stochastic simulation is generally considered best practice for distributing petrophysical properties to achieve more realistic reservoir simulation results.</p> Signup and view all the answers

<p>The basic workflow for modeling a petrophysical parameter includes analyzing well logs, removing trends, defining correlations, and variogram models.</p> Signup and view all the answers

<p>The petrophysical modeling dialog comprises several tabs, such as general settings, distributions, correlations, variograms, and local updates.</p> Signup and view all the answers

<p>A facies model is essential for a petrophysical model in a heterogeneous reservoir, allowing geological concepts to be imparted in 3D space.</p> Signup and view all the answers

<p>Different facies have different mean values, distributions, variability, and variograms, influencing the distribution of petrophysical properties.</p> Signup and view all the answers

<p>Seismic cosimulation involves using a seismic parameter to condition the petrophysical model, with a correlation coefficient set between the variables.</p> Signup and view all the answers

<p>Two algorithms, prediction (kriging) and simulation, are available for modeling petrophysical properties, each with distinct characteristics and applications.</p> Signup and view all the answers

<p>The simulation algorithm, with conditioning to wells handled by kriging as part of the algorithm, is generally preferred in practice.</p> Signup and view all the answers

<p>Proper data analysis is crucial for setting the parameters in the distributions, correlations, and variograms tabs based on input data or geological knowledge.</p> Signup and view all the answers

<p>The petrophysical modeling workflow aims to create a detailed, geologically realistic model of petrophysical properties, essential for accurate reservoir simulation and dynamic modeling.</p> Signup and view all the answers

<p>The petrophysical modeling workflow involves fitting data to wells, using facies parameters, and considering seismic cosimulation for more accurate reservoir simulation results.</p> Signup and view all the answers