Comparison between Sequential Gaussian Simulation and Kriging Interpolation on Soil Heavy Metal Pollution

[Objective] This study was to explore the difference of kriging interpolation and sequential Gaussian simulation on analyzing soil heavy metal pollution with a view to provide references for analyzing the heavy metal pollution of soil. [Method] The sampling data of soil copper from a county of Liaocheng, Shandong Province was set as the study objective. Kriging interpolation and sequential Gaussian simu- lation were used to simulate the spatial distribution of soil copper. And 30 sampling points were selected as the cross-validation data set to compare the two interpola- tion methods. [Result] Kriging method and Gaussian sequential simulation have their own advantages on simulating mean segment and extreme segment, therefore, re- searchers should choose the proper method based on the characteristics of test data and application purposes. [Conclusion] Analysis of soil heavy metal pollution is the prerequisite for soil management and ecological restoration. The result of this study is of important significance for choosing different interpolating and simulating methods to analyze soil heavy metal pollution based on different purposes.

Risk quantification in grade variability of gold deposits using sequential Gaussian simulation

Risk quantification in grade is critical for mine design and planning.Grade uncertainty is assessed using multiple grade realizations,from geostatistical conditional simulations,which are effective to evaluate local or global uncertainty by honouring spatial correlation structures.The sequential Gaussian conditional simulation was used to assess uncertainty of grade estimates and illustrate simulated models in Sivas gold deposit,Turkey.In situ variability and risk quantification of the gold grade were assessed by probabilistic approach based on the sequential Gaussian simulations to yield a series of conditional maps characterized by equally probable spatial distribution of the gold grade for the study area.The simulation results were validated by a number of tests such as descriptive statistics,histogram,variogram and contour map reproductions.The case study demonstrates the efficiency of the method in assessing risk associated with geological and engineering variable such as the gold grade variability and distribution.The simulated models can be incorporated into exploration,exploitation and scheduling of the gold deposit.

Analysis of the Lost Circulation Problem

The well-known“lost circulation”problem refers to the uncontrolled flow of whole mud into a formation.In order to address the problem related to the paucity of available data,in the present study,a model is introduced for the lost-circulation risk sample profile of a drilled well.The model is built taking into account effective data(the Block L).Then,using a three-dimensional geological modeling software,relying on the variation function and sequential Gaussian simulation method,a three-dimensional block lost-circulation risk model is introduced able to provide relevant information for regional analyses.

Application of Seismic Data to Reservoir Modeling of the Chegu 201 Block

Great uncertainty exists in reservoir models built for blocks where well spacing is uneven or large. The uncertainty in reservoir models can be significantly reduced by using Coordinate Cokriging Sequential Gaussian Simulation technology, in combination with the restriction of seismic characteristic data. Satisfactory reservoir parameter interpolation results, which are more accurate than those derived only from borehole data, are obtained, giving rise to a reasonable combination of widespread and dense-sampled seismic (soft data) data with borehole data (hard data). A significant effect has been made in reservoir parameter modeling in the Chegu 201 block of the Futai Oilfield by using this technology.

The simulation of ultrasonic beams with a Gaussian beam equivalent point source model

Point Sources and Gaussian beams are used frequently as fundamental building blocks for developing ultrasonic beam models. Both these models have different weaknesses that limit their effectiveness. Here, we will show that one can develop a Gaussian Beam Equivalent Point Source （GBEPS） model that removes those weaknesses and combines the accuracy and versatility of the point source models with much of the speed and well-behaved nature of Gaussian beam models. We will demonstrate the efficiency and versatility of this new GBEPS model in simulating the beams generated from ultrasonic phased arrays, using as few as one Gaussian beam per element of the array. A single element GBEPS model will be shown to be as accurate as a point source model even when substantial beam focusing or steering is present in the array or where the array beam is transmitted through an interface. At the same time the GBEPS model will be shown to be several orders of magnitude faster than the point source model.

Statistical distribution of geomechanical properties and‘Sweet Spots’identification in part of the upper Bakken

Completions and Reservoir Quality are two key attributes that are used to characterize nonconventional hydrocarbon assets.This is because,for optimum exploitation of these unconventional assets,horizontal wells need to be drilled in“Sweet Spots”(i.e.,regions where Completions and Reservoir Quality are both superior).One way to quantify these qualities is to use reservoir and geomechanical properties.These properties can be estimated on a location basis from well logs,and then mapped over terrain using geostatistical modeling.This study presents a‘Sweet Spots’identification workflow based on three performance indexes(Storage Potential Index,Brittleness Index,and Horizontal Stress Index)that can be used to quantify CQ and RQ.The performance indexes are computed from petrophysical property volumes(of Young's Modulus,Bulk Modulus,Shear Modulus,Poisson's Ratio,Minimum Horizontal Stress,Volume of Shale,Total Organic Carbon,Thickness,and Porosity)which are in turn computed from well logs and geostatistical simulation.In the end,the study offers a method to compare the predicted“Sweet Spots”against available production data via their correlation coefficient.The resulting reasonable formation property maps,the successful identification of‘Sweet Spots’,and a correlation coefficient of 0.88(between the predicted“Sweet Spots”and well production data)point to the potential of the proposed effort.