Frage 1
Frage
Which of the following are valid guidelines, or rules of thumb, for constructing variograms?
Antworten
-
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
Frage 2
Frage
Which of the following are true statements about variogram models?
Antworten
-
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
Frage 3
Frage
To model the condition of geometric anisotropy, we have to use the same combination of linear models in both directions except with different sills.
Frage 4
Frage
Which of the following are reasons why we restrict the number of samples to a smaller neighborhood for estimation using kriging?
Antworten
-
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
Frage 5
Frage
The maximum size of the search neighborhood for kriging should be based on the range of the variogram model.
Frage 6
Frage
Which of the following are true statements about kriging cross validation?
Antworten
-
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
Frage 7
Frage
Which of the following are true statements about kriging?
Antworten
-
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
Frage 8
Frage
Ordinary kriging overcomes which of the following problems that can occur with simple kriging?
Antworten
-
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
Frage 9
Frage
In ordinary kriging, the sum of the weights is forced to be zero.
Frage 10
Frage
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.
Frage 11
Frage
Weights can be negative in ordinary kriging.
Frage 12
Frage
The presence of a high nugget effect reduces spatial information, which results in higher error variance.
Frage 13
Frage
One common application of cross variograms is using high-density seismic data to help estimate permeability at undrilled well locations.
Frage 14
Frage
Which of the following are true statements about cross variograms?
Antworten
-
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.
Frage 15
Frage
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.
Frage 16
Frage
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.
Frage 17
Frage
The cross variogram provides a quantitative measure of the spatial relationship between two variables.
Frage 18
Frage
Additional information from the covariable in cokriging should reduce the error variance as compared to just kriging of the primary variable.
Frage 19
Frage
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.
Frage 20
Frage
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.
Frage 21
Frage
Which of the following factors, if favorable, support use of cokriging?
Antworten
-
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
Frage 22
Frage
The kriging error variance is a good measure of the local uncertainty at the unsampled location.
Frage 23
Frage
The estimated value from kriging is dependent on the values of the surrounding samples, while the error variance is independent.
Frage 24
Frage
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.
Frage 25
Frage
Which of the following characterize the differences between conventional estimation and conditional simulation techniques?
Antworten
-
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.
Frage 26
Frage
A variogram based on estimated values from kriging will have higher sill than the variogram based on sample data.
Frage 27
Frage
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.
Frage 28
Frage
A major disadvantage of grid-based simulation methods is that they do not honor the spatial relationships of reservoir properties.
Frage 29
Frage
A major disadvantage of object-based simulation methods is that it is difficult to condition data at individual well locations.
Frage 30
Frage
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.
Frage 31
Frage
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.
Frage 32
Frage
The ability to closely reproduce the basic univariate statistics of the conditioning data is one of the best properties of sequential Gaussian simulation.
Frage 33
Frage
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.
Frage 34
Frage
Grid-based conditional simulation methods employ kriging as part of the simulation process.
Frage 35
Frage
If full cokriging is used in sequential cosimulation, the following variograms are required for modeling:
Antworten
-
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
Frage 36
Frage
In a typical reservoir characterization involving cosimulation, geological facies are dependent on porosity and permeability while seismic attributes are dependent on facies and porosity.
Frage 37
Frage
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.
Frage 38
Frage
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.
Frage 39
Frage
If cokriging is used in cosimulation, then the local relationships among the attributes must be linear in the transformed domain.
Frage 40
Frage
Geostatistical estimation, or kriging, is based on minimizing the variance between the estimation point and the available samples.
Frage 41
Frage
The variability of a regionalized variable is always zero for distance zero.
Frage 42
Frage
Estimating the values at unsampled points requires knowledge about the relationship between sampled and unsampled locations.
Frage 43
Frage
Geological features are randomly distributed in a spatial context.
Frage 44
Frage
Reservoirs are heterogeneous and have directions of continuity because of their specific depositional, structural, and digenetic histories.
Frage 45
Frage
Two different reservoir models with similar statistics can be very different in geological features.
Frage 46
Frage
For a given direction, spatial covariance depends only on the distance and not location.
Frage 47
Frage
By assuming stationarity, we can use observations from one part of the reservoir to construct variograms for other parts.
Frage 48
Frage
A Variogram is a measure of similarity between two random variables.
Frage 49
Frage
The range in variogram is the distance at which the variogram value becomes constant with respect to lag.
Frage 50
Frage
Kriging allows for production uncertainty analysis.
Frage 51
Frage
Both Kriging and simulation methods can honor hard data.
Frage 52
Frage
Both Kriging and simulation methods can honor the local variogram model.
Frage 53
Frage
Relative to Kriging, the sequential Gaussian simulation is locally accurate.
Frage 54
Frage
If the random path in sequential Gaussian simulation is not changed, the generated realizations will be identical.
Frage 55
Frage
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.
Frage 56
Frage
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.
Frage 57
Frage
A Q-Q plot is a scattered plot based on ranked pair data, so two samples with equal size are always required.
Frage 58
Frage
SGEMS Objects are files with numerical information and there are two types, Data and Grid, both of which specify the location in the file.
Frage 59
Frage
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.
Frage 60
Frage
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.
Frage 61
Frage
In SGEMS, grids can only be displayed in graphical form and grid values are stored in binary form.
Frage 62
Frage
The semivariogram is related to the covariance by the difference between the variance and the spatial covariance regardless of the stationarity of the mean.
Frage 63
Frage
The spatial covariance always starts with a zero value and increases as the lag distance between the two values increases.
Frage 64
Frage
Kriging is a form of linear regression and works best for estimation inside a convex hull of the data.
Frage 65
Frage
The most frequently used types of semi-variogram models are Gaussian, Spherical, and Exponential models.
Frage 66
Frage
A semivariogram model is:
Antworten
-
A polynomial expression representing sample variance
-
A mathematical approximation of sample variability
-
A determinate trend in sample values
-
None of the above
Frage 67
Frage
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).
Antworten
-
Gaussian Model
-
Exponential Model
Frage 68
Frage
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).
Antworten
-
Gaussian Model
-
Exponential Model
Frage 69
Frage
For the following three basic variogram models, which one has the highest growth at the origin? (i.e. the biggest slope at origin)
Frage 70
Frage
For the following three basic variogram models, which one has the lowest growth at the origin? (i.e. the smallest slope at origin)
Frage 71
Frage
Conditional simulation reproduces the value and location of observations.
Frage 72
Frage
Kriging method accounts for local variations.
Frage 73
Frage
Kriging estimation method is appropriate for flow simulation.
Frage 74
Frage
Sequential Gaussian simulation requires transformation of data to normal scores.
Frage 75
Frage
Geo-statistical simulation method cannot use secondary data.
Frage 76
Frage
For collocated cokriging method, the secondary data points have to be at the same locations as the primary data points.
Frage 77
Frage
We have the following variogram model, what is C(0)?
Antworten
-
5.87
-
3.14
-
2.73
-
None of the above
Frage 78
Frage
We have the following variogram model, what is C(3.7)?
Frage 79
Frage
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.