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PETE 404 Exam 2

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RAQ 7-14
Aiman Zamri
Quiz by Aiman Zamri, updated more than 1 year ago
Aiman Zamri
Created by Aiman Zamri over 6 years ago
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Question 1

Question
Which of the following are valid guidelines, or rules of thumb, for constructing variograms?
Answer
  • a. it is best to start with isotropic variograms before proceeding to investigation of anisotropic variograms
  • b. estimation of variograms should begin with large tolerances, and should be decreased as needed to achieve a clearly defined structure
  • c. use half the maximum possible distance within a region of interest as the maximum lag distance at the which the variogram is calculated
  • d. at least 15 to 20 data pairs are needed for a reliable estimate of the variogram for a given lag distance

Question 2

Question
Which of the following are true statements about variogram models?
Answer
  • a. the spherical model is probably the most commonly used model
  • b. any linear combination of nugget, exponential, spherical and Gaussian models is a valid variogram model
  • c. the simplest model is the exponential model
  • d. the Gaussian model is more robust than the exponential model

Question 3

Question
To model the condition of geometric anisotropy, we have to use the same combination of linear models in both directions except with different sills.
Answer
  • True
  • False

Question 4

Question
Which of the following are reasons why we restrict the number of samples to a smaller neighborhood for estimation using kriging?
Answer
  • a. Because kriging requires inverting a matrix, using a large number of data points can require an excessive amount of memory and computation time requirements
  • b. If too many sample points are used, there is a possibility the matrix will become close to singular
  • c. If we use data points at large distances, we may have to extrapolate beyond the available data in the variogram model
  • d. Restricting the search to closer samples results in a more representative estimate, because of local variations due to a lack of stationarity in practice
  • e. Use of sample points farther away tend to screen sample points that are closer, reducing the accuracy of the estimation

Question 5

Question
The maximum size of the search neighborhood for kriging should be based on the range of the variogram model.
Answer
  • True
  • False

Question 6

Question
Which of the following are true statements about kriging cross validation?
Answer
  • a. Cross validation involves the estimation of values at unsampled locations so they can be compared with values at sampled locations
  • b. Cross validation can identify glaring errors in estimation, but it does not guarantee a successful kriging operation
  • c. "Jackknifing" is the most common version of cross validation
  • d. Heteroscedasticity of error variance is a desirable outcome of a cross validation exercise

Question 7

Question
Which of the following are true statements about kriging?
Answer
  • a. The maximum kriging error variance is the data variance
  • b. Simple kriging is the most popular kriging procedure
  • c. Kriging is a weighted average of values at sampled locations
  • d. Kriging weights assigned to sample values are directly proportional to the covariances among the sample points
  • e. Kriging weights assigned to sample values are directly proportional to the covariances between sample points and the unsampled location

Question 8

Question
Ordinary kriging overcomes which of the following problems that can occur with simple kriging?
Answer
  • a. the true global mean is rarely known
  • b. all of the other choices
  • c. the local mean within the search neighborhood may vary over the region of interest
  • d. the assumption of first-order stationarity may not be strictly valid

Question 9

Question
In ordinary kriging, the sum of the weights is forced to be zero.
Answer
  • True
  • False

Question 10

Question
Because λ0 is forced to be zero in ordinary kriging, there are only n unknowns to solve for instead of n+1 unknowns as for simple kriging.
Answer
  • True
  • False

Question 11

Question
Weights can be negative in ordinary kriging.
Answer
  • True
  • False

Question 12

Question
The presence of a high nugget effect reduces spatial information, which results in higher error variance.
Answer
  • True
  • False

Question 13

Question
One common application of cross variograms is using high-density seismic data to help estimate permeability at undrilled well locations.
Answer
  • True
  • False

Question 14

Question
Which of the following are true statements about cross variograms?
Answer
  • a. the cross covariance is symmetric.
  • b. the cross variogram is symmetric.
  • c. the cross variogram is always non-negative.
  • d. estimation of the cross variogram requires that both variable values be available at locations ui and ui+L.

Question 15

Question
In variogram modeling for two variables, the x variogram, y variogram, and x-y cross variogram must all have the same linear combination of structures and must all have the same sill.
Answer
  • True
  • False

Question 16

Question
In variogram modeling with multiple variables and anisotropy, if all the variograms cannot be modeled well, it is critical to model the cross variograms well while the other models can be sacrificed somewhat.
Answer
  • True
  • False

Question 17

Question
The cross variogram provides a quantitative measure of the spatial relationship between two variables.
Answer
  • True
  • False

Question 18

Question
Additional information from the covariable in cokriging should reduce the error variance as compared to just kriging of the primary variable.
Answer
  • True
  • False

Question 19

Question
Simple cokriging with one secondary variable requires the inversion of a (n+m+2)-size matrix, where n is the number of samples of the primary variable and m is the number of samples of the covariable.
Answer
  • True
  • False

Question 20

Question
Collocated cokriging requires the covariable sample to be available at every location where the primary variable is to be estimated, which increases the matrix size compared to regular (non-collocated) cokriging.
Answer
  • True
  • False

Question 21

Question
Which of the following factors, if favorable, support use of cokriging?
Answer
  • a. there is a physical basis for the relationship between the primary variable and covariable
  • b. the primary variable is considerably undersampled
  • c. the relationship between the primary variable and covariable is strong
  • d. the covariable has been used successfully in the past for estimation of the primary variable
  • e. the primary variable and covariable are linearly related

Question 22

Question
The kriging error variance is a good measure of the local uncertainty at the unsampled location.
Answer
  • True
  • False

Question 23

Question
The estimated value from kriging is dependent on the values of the surrounding samples, while the error variance is independent.
Answer
  • True
  • False

Question 24

Question
Estimation by kriging does not reproduce extreme values observed in the sample data because the weights associated with individual samples are nearly always less than one, thus reducing the effects of extreme values.
Answer
  • True
  • False

Question 25

Question
Which of the following characterize the differences between conventional estimation and conditional simulation techniques?
Answer
  • a. Conventional estimation does not reproduce extreme values in the sample data, while conditional simulation is able to.
  • b. Conventional estimation does not provide a good estimation of local uncertainty, while conditional simulation is able to.
  • c. Conventional estimation preserves the spatial relationship among the estimated values while conditional simulation does not.

Question 26

Question
A variogram based on estimated values from kriging will have higher sill than the variogram based on sample data.
Answer
  • True
  • False

Question 27

Question
One of the advantages of conditional simulation is that if we create multiple equiprobable realizations and these realizations correctly represent the multivariate distribution, they will bound the true realization.
Answer
  • True
  • False

Question 28

Question
A major disadvantage of grid-based simulation methods is that they do not honor the spatial relationships of reservoir properties.
Answer
  • True
  • False

Question 29

Question
A major disadvantage of object-based simulation methods is that it is difficult to condition data at individual well locations.
Answer
  • True
  • False

Question 30

Question
Estimation using conventional kriging techniques is dependent on the order in which unsampled locations are visited, while simulation using sequential conditional simulation methods is independent of the order in which unsampled locations are visited.
Answer
  • True
  • False

Question 31

Question
In the sequential simulation technique, in addition to selecting the sampled points within the search neighborhood, previously simulated points within the search neighborhood are also selected.
Answer
  • True
  • False

Question 32

Question
The ability to closely reproduce the basic univariate statistics of the conditioning data is one of the best properties of sequential Gaussian simulation.
Answer
  • True
  • False

Question 33

Question
The five-step sequential simulation process is as follows: (1) model variograms, (2) transform the original data into a new domain, (3) determine a random path to visit all the unsampled locations, (4) sequentially estimate values at the unsampled locations, and (5) back-transform the values to the original domain.
Answer
  • True
  • False

Question 34

Question
Grid-based conditional simulation methods employ kriging as part of the simulation process.
Answer
  • True
  • False

Question 35

Question
If full cokriging is used in sequential cosimulation, the following variograms are required for modeling:
Answer
  • a. cross-variograms for all pairs of attributes in the original domain
  • b. variograms for each attribute in the transformed domain
  • c. cross-variograms for all pairs of attributes in the transformed domain
  • d. cross-variograms for all pairs of attributes for which there are dependencies in the original domain
  • e. cross-variograms for all pairs of attributes for which there are dependencies in the transformed domain
  • f. variograms for each attribute in the original domain

Question 36

Question
In a typical reservoir characterization involving cosimulation, geological facies are dependent on porosity and permeability while seismic attributes are dependent on facies and porosity.
Answer
  • True
  • False

Question 37

Question
The primary advantage of sequential cosimulation is its ability to honor the local relationships between multiple attributes, as well as the individual spatial relationships of the multiple attributes.
Answer
  • True
  • False

Question 38

Question
In sequential cosimulation of multiple attributes, at each unsampled location, all the unknown attributes are simulated in sequential order from least independent to most independent to preserve their relationships.
Answer
  • True
  • False

Question 39

Question
If cokriging is used in cosimulation, then the local relationships among the attributes must be linear in the transformed domain.
Answer
  • True
  • False

Question 40

Question
Geostatistical estimation, or kriging, is based on minimizing the variance between the estimation point and the available samples.
Answer
  • True
  • False

Question 41

Question
The variability of a regionalized variable is always zero for distance zero.
Answer
  • True
  • False

Question 42

Question
Estimating the values at unsampled points requires knowledge about the relationship between sampled and unsampled locations.
Answer
  • True
  • False

Question 43

Question
Geological features are randomly distributed in a spatial context.
Answer
  • True
  • False

Question 44

Question
Reservoirs are heterogeneous and have directions of continuity because of their specific depositional, structural, and digenetic histories.
Answer
  • True
  • False

Question 45

Question
Two different reservoir models with similar statistics can be very different in geological features.
Answer
  • True
  • False

Question 46

Question
For a given direction, spatial covariance depends only on the distance and not location.
Answer
  • True
  • False

Question 47

Question
By assuming stationarity, we can use observations from one part of the reservoir to construct variograms for other parts.
Answer
  • True
  • False

Question 48

Question
A Variogram is a measure of similarity between two random variables.
Answer
  • True
  • False

Question 49

Question
The range in variogram is the distance at which the variogram value becomes constant with respect to lag.
Answer
  • True
  • False

Question 50

Question
Kriging allows for production uncertainty analysis.
Answer
  • True
  • False

Question 51

Question
Both Kriging and simulation methods can honor hard data.
Answer
  • True
  • False

Question 52

Question
Both Kriging and simulation methods can honor the local variogram model.
Answer
  • True
  • False

Question 53

Question
Relative to Kriging, the sequential Gaussian simulation is locally accurate.
Answer
  • True
  • False

Question 54

Question
If the random path in sequential Gaussian simulation is not changed, the generated realizations will be identical.
Answer
  • True
  • False

Question 55

Question
Similar to variance, covariance is defined in units that depend on the units of x and y but the correlation coefficient is dimensionless, and its value always falls between the limits of 1 and -1.
Answer
  • True
  • False

Question 56

Question
When the square of the correlation coefficient is used to describe the relationship between two variables, whether the two variables are negatively or positively related cannot be exhibited, but it is a common way in describing the "goodness of fit" in a linear regression between two variables.
Answer
  • True
  • False

Question 57

Question
A Q-Q plot is a scattered plot based on ranked pair data, so two samples with equal size are always required.
Answer
  • True
  • False

Question 58

Question
SGEMS Objects are files with numerical information and there are two types, Data and Grid, both of which specify the location in the file.
Answer
  • True
  • False

Question 59

Question
Geostatistics represents a set of mathematical tools which have deterministic or stochastic components, may represent different types of data at different scales, but cannot fill the interwell space properly.
Answer
  • True
  • False

Question 60

Question
The SGEMS grid(s) has to be specified before attempting any processing that will result in the generation of a grid variable such as Kriging.
Answer
  • True
  • False

Question 61

Question
In SGEMS, grids can only be displayed in graphical form and grid values are stored in binary form.
Answer
  • True
  • False

Question 62

Question
The semivariogram is related to the covariance by the difference between the variance and the spatial covariance regardless of the stationarity of the mean.
Answer
  • True
  • False

Question 63

Question
The spatial covariance always starts with a zero value and increases as the lag distance between the two values increases.
Answer
  • True
  • False

Question 64

Question
Kriging is a form of linear regression and works best for estimation inside a convex hull of the data.
Answer
  • True
  • False

Question 65

Question
The most frequently used types of semi-variogram models are Gaussian, Spherical, and Exponential models.
Answer
  • True
  • False

Question 66

Question
A semivariogram model is:
Answer
  • A polynomial expression representing sample variance
  • A mathematical approximation of sample variability
  • A determinate trend in sample values
  • None of the above

Question 67

Question
Assume we have two porosity models generated with the same parameters using Gaussian and exponential models. Identify the variogram model for porosity map given in Figure (1).
Image:
1 (binary/octet-stream)
Answer
  • Gaussian Model
  • Exponential Model

Question 68

Question
Assume we have two porosity models generated with the same parameters using Gaussian and exponential models. Identify the variogram model for porosity map given in Figure (2).
Image:
1 (binary/octet-stream)
Answer
  • Gaussian Model
  • Exponential Model

Question 69

Question
For the following three basic variogram models, which one has the highest growth at the origin? (i.e. the biggest slope at origin)
Image:
2 (binary/octet-stream)
Answer
  • 1
  • 2
  • 3

Question 70

Question
For the following three basic variogram models, which one has the lowest growth at the origin? (i.e. the smallest slope at origin)
Image:
2 (binary/octet-stream)
Answer
  • 1
  • 2
  • 3

Question 71

Question
Conditional simulation reproduces the value and location of observations.
Answer
  • True
  • False

Question 72

Question
Kriging method accounts for local variations.
Answer
  • True
  • False

Question 73

Question
Kriging estimation method is appropriate for flow simulation.
Answer
  • True
  • False

Question 74

Question
Sequential Gaussian simulation requires transformation of data to normal scores.
Answer
  • True
  • False

Question 75

Question
Geo-statistical simulation method cannot use secondary data.
Answer
  • True
  • False

Question 76

Question
For collocated cokriging method, the secondary data points have to be at the same locations as the primary data points.
Answer
  • True
  • False

Question 77

Question
We have the following variogram model, what is C(0)?
Image:
3 (binary/octet-stream)
Answer
  • 5.87
  • 3.14
  • 2.73
  • None of the above

Question 78

Question
We have the following variogram model, what is C(3.7)?
Image:
4 (binary/octet-stream)
Answer
  • 3.64
  • 2.07
  • 2.23
  • 3.78

Question 79

Question
In the following drawing, we know the attributes at S1, S2, and S3. What is Z(S0)? As shown, we know the distances in between, e.g. the distance between S1 and S2 is 20. We also know the variogram model.
Image:
5 (binary/octet-stream)
Answer
  • 21.69
  • 21.83
  • 22.83
  • 23.34
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