scholarly journals A novel downscaling procedure for compositional data in the Aitchison geometry with application to soil texture data

2020 ◽  
Author(s):  
Federico Gatti ◽  
Alessandra Menafoglio ◽  
Niccolò Togni ◽  
Luca Bonaventura ◽  
Davide Brambilla ◽  
...  
Keyword(s):  
Compositional Data ◽  
State Of The Art ◽  
Sequential Gaussian Simulation ◽  
Soil Maps ◽  
Simulation Framework ◽  
Aitchison Geometry ◽  
Realistic Case ◽  
Gaussian Simulation ◽  
Digital Soil Maps

Abstract In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constraints, i.e. unit-sum and positivity, accounting for the scale invariance and relative scale of these data. We show that the method can be used in a block sequential Gaussian simulation framework in order to assess the variability of downscaled quantities. Finally, to validate the method, we test it first in an idealized scenario and then apply it for the downscaling of digital soil maps on a more realistic case study. The digital soil maps for the realistic case study are obtained from SoilGrids, a system for automated soil mapping based on state-of-the-art spatial predictions methods.

Validating digital soil maps using soil taxonomic distance: A case study of Ireland

2015 ◽  
Vol 5 ◽  
pp. 188-197 ◽  
Author(s):  
I. Simo ◽  
R.P.O. Schulte ◽  
R. Corstanje ◽  
J.A. Hannam ◽  
R.E. Creamer
Keyword(s):  
Soil Maps ◽  
Taxonomic Distance ◽  
Digital Soil Maps

Assessing of tight reservoir by combining the porosity of geological units, and simulated images of rock types: A case study

2021 ◽  
Author(s):  
Lamia Boussa ◽  
Amar Boudella ◽  
José Almeida
Keyword(s):  
Stochastic Simulation ◽  
3D Model ◽  
Reservoir Characterization ◽  
Rock Type ◽  
Sequential Gaussian Simulation ◽  
Geostatistical Methods ◽  
Rock Types ◽  
Gaussian Simulation ◽  
The Impact

<p>Reservoir characterization and flow studies require accurate inputs of petrophysical properties such as porosity, permeability, water and residual oil saturation and capillary pressure functions. All these parameters are necessary to evaluate, predict and optimize the production of a reservoir.</p><p>This study is the continuity of a previous work that summarize the construction of a net rock aerial map by combining stochastic simulation of rock types and processed seismic data. In this case study; petrophysical data are integrated to construct a 3D model of porosity corresponding to the 3D model of rock type. This is in order to further understand the intricacies of the geostatistical methods used and the impact of the technique on the resulting uncertainty profile</p><p>For the construction of 3D model of porosity corresponding to the 3D model of rock types, a geostatistical workflow encompassing the modelling of experimental variograms and sequential Gaussian simulation (SGS) were used. The geostatsitical methodologies of stochastic simulation such as SGS enabled the generation of several realistic scenarios of constinuous data, such as porosity, within a volume, thus facilitating the association of local probabilities of occurrence of each rock type.</p><p>The resulting porosity image properly combines the available seismic and well data and balance the local and regional uncertainty of the studied reservoir volume.</p><p><strong>Keywords: </strong>Geostatistics, Sequential Gaussian Simulation (SGS), Rock types, Porosity, Uncertainty, Spatial resolution.</p>

Integrated Physical Property Modeling with 3D Visual Technique – A Case Study in Lishui Depression, East China Sea Basin

2013 ◽  
Vol 421 ◽  
pp. 834-837 ◽  
Author(s):  
Guo Wei Hou ◽  
Xue Li ◽  
Jin Laing Zhang ◽  
Long Long Liu
Keyword(s):  
Physical Property ◽  
Sedimentary Facies ◽  
Sequential Gaussian Simulation ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Visual Technique ◽  
Property Modeling ◽  
Property Model ◽  
Gaussian Simulation

3D geological modeling and visualization are the key technique issues to implement the plan of Digital Earth". However, 3D physical property model varies depending on the technology of 3D geological modeling which will bring about great changes in the reflection of reservoir property. In this paper, Some super voxel models, mathematical models of fault and geometrical models of fold have been contrived so as to show the space geometric configuration of the complicated geologic structures. And the architecture for integrated physical property modeling is established; Based on the physical property model, the spatial distribution and plane spread of reservor property is displayed detailedly with Sequential Gaussian simulation. By integrating geological database, sedimentary facies maps with those property models, geologists will be able to capture the partial characteristics and whole structure embodied in the geological data in a direct-viewing, figurative and accurate manner.

scholarly journals Sequential Gaussian Simulation of Uranium Spatial Distribution – A Transboundary Watershed Case Study

2014 ◽  
Vol 8 ◽  
pp. 2-6 ◽  
Author(s):  
M.T.D. Albuquerque ◽  
I.M.H.R. Antunes ◽  
M.F.M. Seco ◽  
N.M. Roque ◽  
G. Sanz
Keyword(s):  
Spatial Distribution ◽  
Sequential Gaussian Simulation ◽  
Gaussian Simulation

scholarly journals Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable

Minerals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Heber Hernandez Guerra ◽  
Elisabete Alberdi ◽  
Aitor Goti
Keyword(s):  
Target Population ◽  
Auxiliary Variable ◽  
Sequential Gaussian Simulation ◽  
Sampling Density ◽  
Spatially Correlated ◽  
Subsequent Transformation ◽  
The Mean ◽  
Gaussian Simulation ◽  
Primary Variable

In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) ( M G E = 1.2366 · S D P − 0.224 ).

scholarly journals A High Resolution 3D Geomodel for Giant Carbonate Reservoir- A Field Case Study from an Iraqi Oil Field

2019 ◽  
Vol 26 (1) ◽  
pp. 160-173
Author(s):  
Wisam I. Taher Al-Rubaye ◽  
Sameera Mohammed Hamd-Allah
Keyword(s):  
High Resolution ◽  
Scale Up ◽  
Vertical Direction ◽  
Carbonate Reservoir ◽  
Oil Field ◽  
Sequential Gaussian Simulation ◽  
Grid Cells ◽  
Volume Assessment ◽  
Gaussian Simulation

Constructing a fine 3D geomodel for complex giant reservoir is a crucial task for hydrocarbon volume assessment and guiding for optimal development. The case under study is Mishrif reservoir of Halfaya oil field, which is an Iraqi giant carbonate reservoir. Mishrif mainly consists of limestone rocks which belong to Late Cenomanian age. The average gross thickness of formation is about 400m. In this paper, a high-resolution 3D geological model has been built using Petrel software that can be utilized as input for dynamic simulation. The model is constructed based on geological, geophysical, pertophysical and engineering data from about 60 available wells to characterize the structural, stratigraphic, and properties distribution along the reservoir. Fourteen geological surfaces for all Mishrif units have been generated based on well tops data and top Mishrif structural map. The reservoir has been divided into 163 sublayers through the vertical direction and 160*383 grid cells in x-y direction with 9,988,640 total grid cells. A scale up process are performed for well log data, then, Sequential Gaussian Simulation algorithm are applied to fill 3D grid cells with properties values in areas away from wells. Pertophysical properties distribution for all reservoir zones are analyzed. The estimated initial oil in place of Mishrif through this model is close to that calculated in other previous studies.  

Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu, China

2015 ◽  
Vol 66 (6) ◽  
pp. 1012-1022 ◽  
Author(s):  
X.-L. Sun ◽  
Y.-J. Wu ◽  
Y.-L. Lou ◽  
H.-L. Wang ◽  
C. Zhang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Maps ◽  
Digital Soil Maps
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