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Jason A. McLennan Weishan Ren Oy Leuangthong C. V. Deutsch 《Natural Resources Research》2006,15(2):119-127
The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in
the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the
stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty
in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture
geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations.
A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the
recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB)
and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB
and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces
to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort
McMurray, Alberta. 相似文献
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Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details. 相似文献
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Transformation of Residuals to Avoid Artifacts in Geostatistical Modelling with a Trend 总被引：1，自引：0，他引：1
Trend modelling is an important part of natural resource characterization. A common approach to account for a variable with a trend is to decompose it into a relatively smoothly varying trend and a more variable residual component. Then, the residuals are stochastically modelled independent of the trend. This decomposition can result in values outside the plausible range of variability, such as grades below zero or ratios that exceed 1.0. We transform the residuals conditional to the trend component to explicitly remove these complex features prior to geostatistical modelling. Back transformation of the modelled residual values allows the complex relations to be reproduced. A petroleum-related application shows the robustness of the proposed transformation. Furthermore, a mining application shows that when this conditional transformation is applied to the original variable, instead of the residual, simulated values are assured to be nonnegative. 相似文献
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Weishan Ren Jason A. Mclennan Oy Leuangthong Clayton V. Deutsch 《Natural Resources Research》2006,15(2):111-117
There is a need to estimate reserve uncertainty for large lease areas. Detailed 3D models of heterogeneity are not necessarily
required, but there is a need to integrate all available data into an in-situ reserve estimate with uncertainty. A 2D mapping
approach is presented to appraise reserves accounting for multiple variables, multiple data sources, and uncertainty. The
approach can be considered in three primary steps: (1) Bayesian updating is used to determine local distributions of uncertainty
for each primary variable while integrating multiple secondary information, (2) matrix simulation is employed to jointly and
simultaneously simulate multiple collocated variables to determine a derived variable such as OOIP, and (3) probability field
simulation then is applied to permit joint simulation of multiple locations. This methodology permits local and global uncertainty
assessment while integrating multiple sources of information. An application to the McMurray Formation in Alberta, Canada
is demonstrated. 相似文献
6.
Minimum Acceptance Criteria for Geostatistical Realizations 总被引：2，自引：0，他引：2
Geostatistical simulation is being used increasingly for numerical modeling of natural phenomena. The development of simulation as an alternative to kriging is the result of improved characterization of heterogeneity and a model of joint uncertainty. The popularity of simulation has increased in both mining and petroleum industries. Simulation is widely available in commercial software. Many of these software packages, however, do not necessarily provide the tools for careful checking of the geostatistical realizations prior to their use in decision-making. Moreover, practitioners may not understand all that should be checked. There are some basic checks that should be performed on all geostatistical models. This paper identifies (1) the minimum criteria that should be met by all geostatistical simulation models, and (2) the checks required to verify that these minimum criteria are satisfied. All realizations should honor the input information including the geological interpretation, the data values at their locations, the data distribution, and the correlation structure, within acceptable statistical fluctuations. Moreover, the uncertainty measured by the differences between simulated realizations should be a reasonable measure of uncertainty. A number of different applications are shown to illustrate the various checks. These checks should be an integral part of any simulation modeling work flow. 相似文献
7.
Nusret Demir Bülent Bayram Dursun Zafer Şeker Selen Oy Abdülkadir İnce Salih Bozkurt 《Marine Geodesy》2019,42(2):166-185
Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study’s authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10?m) by evaluation with the used reference vector data. 相似文献
8.
Martin Ryan Machuca-Mory David Leuangthong Oy Boisvert Jeff B. 《Natural Resources Research》2019,28(2):291-307
Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.
相似文献9.
Detecting and quantifying sources of non-stationarity via experimental semivariogram modeling 总被引：1，自引：0，他引：1
Miguel A. Cuba Oy Leuangthong Julian M. Ortiz 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(2):247-260
Conventional geostatistics often relies on the assumption of second order stationarity of the random function (RF). Generally,
local means and local variances of the random variables (RVs) are assumed to be constant throughout the domain. Large scale
differences in the local means and local variances of the RVs are referred to as trends. Two problems of building geostatistical
models in presence of mean trends are: (1) inflation of the conditional variances and (2) the spatial continuity is exaggerated.
Variance trends on the other hand cause conditional variances to be over-estimated in certain regions of the domain and under-estimated
in other areas. In both cases the uncertainty characterized by the geostatistical model is improperly assessed. This paper
proposes a new approach to identify the presence and contribution of mean and variance trends in the domain via calculation
of the experimental semivariogram. The traditional experimental semivariogram expression is decomposed into three components:
(1) the mean trend, (2) the variance trend and (3) the stationary component. Under stationary conditions, both the mean and
the variance trend components should be close to zero. This proposed approach is intended to be used in the early stages of
data analysis when domains are being defined or to verify the impact of detrending techniques in the conditioning dataset
for validating domains. This approach determines the source of a trend, thereby facilitating the choice of a suitable detrending
method for effective resource modeling. 相似文献
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