Sequential Gaussian Simulation Algorithm Research Articles

Spatial distribution of soil organic matter in a coal mining subsidence area

ABSTRACTUnderground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the three-dimensional (3D) spatial distribution of soil nutrients in mining area farmland is crucial for agricultural production and environmental management. However, few studies have reported the 3D spatial distribution of soil organic matter (SOM) in coal mining subsidence area. In our study, a sequential Gaussian simulation (SGS) algorithm was used to analyse the spatial distribution of SOM based on observations of 180 soil samples in the Zhaogu mine in China. The results showed that the SOM content had considerable variation in spatial distribution at different soil depths (0–20, 20–40, 40–60 cm) and decreased with the increase in soil depth. The spatial variability of surface organic matter was the largest, and the coefficient of variation was 29.38%, which was moderately mutated. The spatial distribution of SOM also varied among slope locations. The SOM content was higher upslope and downslope than on the middle slope. In addition, given a threshold, SGS can be used to calculate the probability that the organic matter content at any position is lower or higher than the given value. The research results provide a reference for land reclamation and precision agriculture.

Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial Nonstationary Approach

AbstractDigital elevation model (DEM) as an essential input to flood risk analyzers is subject to a certain level of uncertainty, which increases as the resolution becomes coarser. To quantify this uncertainty, a probabilistic framework incorporating 2‐D hydrodynamic flood mapping by LISFLOOD‐FP along with a sequential Gaussian simulation (SGS) model is presented in this paper. Based on ordinary kriging (OK) interpolation techniques, spatial uncertainty is modeled through SGS by generating several equiprobable realizations. Furthermore, regression kriging (RK) is used in the SGS algorithm utilizing its ability to explore additional information such as terrain characteristics to estimate the elevation error. A new technique called nonstationary sequential Gaussian simulation is also proposed by introducing the spatial nonstationarity of DEM error into SGS framework based on two approaches of uniform and nonuniform moving window. Different DEM resolutions resampled from a 1‐m light detection and ranging‐derived DEM are used in the hydrodynamic model to derive the accuracy‐efficiency trade‐offs and select the most suitable spatial resolution for probabilistic analysis. A detailed comparison among OK, RK, nonstationary OK, and nonstationary RK models is made considering probabilistic floodplain characteristics. The subsequent risk curves are then derived under floods of different return periods. The methodology is implemented in the coastal areas of Lower Manhattan and Brooklyn in New York City. Results show that the proposed methodology could reduce the uncertainty in risk estimation imposed by the stationarity assumption. The methodology could help decision‐makers to more accurately utilize the existing tools and data for flood risk analysis and preparedness.

Modeling of Petrophysical Properties and Reserve Estimation of Mishrif Formation-Garraf Oil Field

In this study a build-up a 3D geologic model both depend on the information of field and subsurface is a usual task in geologic studies including resource evaluation and hazard assessment. During this study a 3D geologic model for Mishrif Formation in Garraf oil field has been set-up by using petrel 2015 software system. Mishrif Formation represents the foremost important reservoir in Garraf oil field. Four vertical oil wells and one directional well were chosen in Garraf Oil Field so as to set-up structural and petrophysical models delineated by a 3D static geologic model in 3 dimensions. The structural closure is shifted from GA-3 well to GA-A1P well. Mishrif Formation was divided into 10 zones. Sequential Gaussian Simulation algorithm was used to build-up Petrophysical model (NTG, permeability, water saturation and porosity) for Mishrif Formation keeping with information analyses and also the results from modeling the units (M1.2, L1 and L1.2) are thought-about as prime quality reservoir units because of the high PHIE and low water saturation. Units (L2, L2.2, L2.3 and L2.4) are thought-about as poor reservoirs because of low PHIE and high water saturation, and non-reservoir units include (Top Mishrif, M1 and M2) thus, they represent cap units. Cross sections of petrophysical model were conducted to Illustrate the vertical and horizontal distribution of porosity, permeability, Net to gross and water saturation between wells within the field. This study calculated the reserves of the Mishrif formation of Garraf oilfield by petrel, that alter the issue that the reserves parameters are difficult to properly acquire when estimate complex reservoir’s reserves in terms of ancient volume technique.

Geological modeling and upscaling of the Ronier 4 block in Bongor basin, Chad

The study focuses on building a 3D-dimensional model for using well log data of 3 wells to enable a better understanding of the reservoir characteristics and evaluation of the hydrocarbon potential in the field of Ronier in the Bongor Basin, Chad. These data are used to build a Lithofacies model, property model, the permeability model and the saturation model which are the parameters that describe the reservoir. Indeed, the accuracy to quantify the geological reserve of Ronier 4 reservoir block depends on the estimation of these values. The result of geological modeling and upscaling of the Ronier 4 block of Ronier oilfield is presented systematically. In this work, a Sequential Gaussian Simulation (SGS) algorithm under the phase control based on the collocated co-Kriging is adopted and the models of porosity, permeability and saturation are established. Lithofacies model has sand (0) content of 66.69% mostly the first layer, while the code (2) shale content 12.15% mostly concentrated in the center, north and south. Finally, the code (3) sand content is 21.16%. The NTG distribution is highly concentrated in the north, center and south (1) and 0.7 on average, while the lowest range is 0.1. The study shows a good permeability range between 460 to 1200 and 500 mD on average. The porosity distribution mainly distributed between 17 to 27 and 19% on average. These indicate that it has good reservoir properties. The 3D developed geological model provides the basis for calculating the Oil Initially in Place (OIIP) volumes of hydrocarbons.

Bayesian Stochastic Inversion (A case study from an Iranian oil field)

We have implemented an estimation procedure whereby wireline data can be extrapolated away from existing well using geostatistical inversion of post-stack 3D seismic data. This procedure works directly in a fine-scale stratigraphic grid, and is conditioned by well and seismic data. It uses a Bayesian framework and a linearized, weak contrast approximation of the Zoeppritz equation to construct a joint log-Gaussian posterior distribution for Pwave impedances. Variograms are also estimated from well-log, seismic data and acoustic inversion results that define the expected degree of lateral smoothness away from the well. A sequential Gaussian Simulation algorithm is applied to sample the posterior PDF and generates multiple, high-resolution realizations of the acoustic impedance which can be utilized to generate stochastic realizations of petrophysical variables that not only honor the well-log data, but most importantly, that fully honor the 3D seismic. Sensitivity analysis was also performed to find the optimum values for S/N ratio, Variogram ranges and prior standard deviation. We had just one well in the area and preferred to keep it to perform quality control of the inversion results. Without constraining with well, geostatistical inversion still can be used to estimate acceptable static reservoir model for the subsequent simulation and planning of in-fill drilling and/or enhanced-oil-recovery operations.

Modelling local uncertainty in relations between birth weight and air quality within an urban area: combining geographically weighted regression with geostatistical simulation.

In this study, we combine known methods to present a new approach to assess local distributions of estimated parameters measuring associations between air quality and birth weight in the urban area of Sines (Portugal). To model exposure and capture short-distance variations in air quality, we use a Regression Kriging estimator combining air quality point data with land use auxiliary data. To assess uncertainty of exposure, the Kriging estimator is incorporated in a sequential Gaussian simulation algorithm (sGs) providing a set of simulated exposure maps with similar spatial structural dependence and statistical properties of observed data. Following the completion of the simulation runs, we fit a geographically weighted generalized linear model (GWGLM) for each mother's place of residence, using observed health data and simulated exposure data, and repeat this procedure for each simulated map. Once the fit of GWGLM with all exposure maps is finished, we take the distribution of local estimated parameters measuring associations between exposure and birth weight, thus providing a measure of uncertainty in the local estimates. Results reveal that the distribution of local parameters did not vary substantially. Combining both methods (GWGLM and sGs), however, we are able to incorporate local uncertainty on the estimated associations providing an additional tool for analysis of the impacts of place in health.

Soil moisture estimation using tomographic ground penetrating radar in a MCMC\u2013Bayesian framework

In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.

3D Spatial Distribution of Reservoir Parameters for Prospect Identification in ''Bizzy'' Field, Niger Delta

This study focuses on the use of some geostatistical tools to spatially distribute reservoir parameters in order to identify the bypassed prospects from the earlier seismic interpretation that was carried out in the field using 3D seismic data. Four wells and seismic data were used to generate the interpreted input horizon grids, fault polygons and to carry out detailed petrophysical analysis. Structural and property modeling which include; facies, net to gross, porosity and water saturation were distributed stochastically within the constructed 3D grid using Sequential Gaussian Simulation (SGS) algorithm. The reservoir structural model show system of different oriented growth faults F1 to F9. Faults F1, F2, F3 and F4 were the major growth faults, dipping towards south-west and are quite extensive almost across all the seismic section. A rollover anticline formed as a result of deformation of the sediments deposited on the downthrown block of fault F1. The other faults were minor fault (synthetic and antithetic). The trapping mechanism is a fault assisted anticlinal closure. Results from well log analysis and petrophysical models shows Godwin reservoir to be a moderate to good reservoir in terms of facies, with good net to gross, porosity, permeability and low water saturation. This study has also demonstrated the effectiveness of 3D geostatical modeling technique as a tool for better understanding the distribution with respect to space of continuous reservoir properties. It will also provide a framework for the future prediction of reservoir qualities and yield rate of the reservoirs.

Multivariate Geostatistical Simulation on Block-Support in the Presence of Complex Multivariate Relationships: Iron Ore Deposit Case Study

A reliable and realistic subsurface resource spatial modeling is a common and critical task for geosciences projects (mining, petroleum and environmental) because the models are then integrated with downstream processes to evaluate process performance. Coregionalized variables considered in resource modeling are often related through compositional constraints and complex dependence relationships. Satisfying these constraints and relationships in spatial modeling is a practical requirement to obtain accurate predictions of mineral resources in the subsurface. This paper presents a multistage method that addresses these issues and allows the multivariate simulation of cross-correlated variables with complex features directly on block-support, conditionally to the information of drill hole data at a quasi-point support. At a first stage, a chained transformation is used for removal multivariate complexities and decorrelation of the variables. Then, this chained transformation is adapted within the direct block-support sequential Gaussian simulation algorithm. The back-transformation of the chained transformation reintroduces complex features and correlations. A proof of the concept using a data set from the Gole-Gohar iron ore deposit in Iran demonstrates the performance of the proposed approach, the results of which are then compared against a common modeling approach.

Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

Abstract A geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the spatial distribution of porosity and characterize the degree of heterogeneity of underlying formation. Porosity data from twenty-two wells were loaded into SGeMS software. Univariate statistical analysis, experimental semivariogram and Sequential Gaussian Simulation (SGS) were applied on the data. The data was close to normal approximation of Gaussian based of the results of univariate statistics. However, to construct and model horizontal and vertical semivariograms, the data was log-normalized to reduce the coefficient of variation and to get good fit of the model. Parametric semivariogram model shows the range of 72–6480 m, nugget effect of 0.006 and sills of 0.0095, 0.0099 and 0.0111. Six realizations were generated using SGS algorithm and the results suggest that any one of the realizations can independently represents the true picture of the subsurface geology within the study area. Ranking of realizations shows realization 6 as the best and realization 2 as the lowest. This model could be used as an initial condition for simulation of flow.

Using sequential Gaussian simulation to assess the uncertainty of the spatial distribution of soil salinity in arid regions of Northwest China

ABSTRACTAccurately mapping and monitoring the spatial distribution pattern of soil salinity is essential for sustainable soil management and decision-making. The kriging-based interpolation technique is generally used to map the spatial distribution of soil salinity; however, this technique neglects the variation caused by interpolation for each unsampled location. The sequential gaussian simulation (SGS) is an effective tool to collect mapping uncertainties at several locations simultaneously, which is not possible in the kriging-based technique. Soil electrical conductivity has been widely used as an index for soil salinity. Based on 0–100 cm soil profile from 117 locations in the Manas River basin, Northwest China, the SGS algorithm was used to assess the uncertainty of the spatial distribution of soil electrical conductivity. It was found that the SGS algorithm was reliable in reproducing the spatial distribution of soil electrical conductivity. The SGS algorithm reproduced the sample statistics reasonably well. The standard deviations of the samples generated by the SGS algorithm (0.463–0.508 (dS m−1)) were closer to the actual samples (0.675 (dS m−1)) than those generated by kriging (0.454 (dS m−1)). Most of the study area was lightly affected by salinity. Around 30% of the study area was moderately affected, and the heavily affected areas were sporadically scattered across the study area. The spatial uncertainty at multiple point presented a declining trend as the critical probability at a single point increased. The spatial estimation of the soil electrical conductivity in multiple point was more robust than that in the local location because of the low uncertainty.

Spatial Distribution and Associated Spatial Uncertainty of Potential Toxic Elements - The Lake Kalimanci Case Study (Republic of Macedonia)

Pollution from mining activities is a significant problem in various regions of the Republic of Macedonia. A geochemical survey of the surface deposits of Lake Kalimanci in the easterly region of the Republic of Macedonia was carried out before (2001) and later (2007) the dam failure and thus allowing the measurement of the tailings’ material from Sasa Pb-Zn Mine-Osogovo Mountains (Eastern Macedonia). The concentrations of potentially toxic elements (PTE): Ag, Au, Cd, Co, Cu, Pb, Sb, Th, S, U and Zn were obtained by ICP Mass Spectrometry (ACME Laboratories in Vancouver, Canada). Data analysis, through Principal Component Analysis (PCA), was performed in order reduce the space of analyses by the construction of synthesis variables (Principal Factors). Geostatistical modeling was used, throughout conventional variography and the Sequential Gaussian Simulation algorithm (SGS), to model the new factors’ spatial distribution. A hundred simulations, differing in their initial random-number seed, were performed and a Mean Image (MI) obtained. Spatial uncertainty evaluation (simulation's Standard Deviation) allowed the definition of future monitoring and sampling strategies as well as the measurement of remediation possibilities.

Evaluation of reservoir compartmentalization and property trends using static modelling and sequence stratigraphy

In the context of harder-to-find reserves and rise in development costs, it is vital that reservoir heterogeneities and compartmentalization be accurately predicted ahead of the drill bit. There are many situations where unexpected compartmentalization negatively impacts reservoir development. This paper used an integration of 3D seismic, well logs, and biostratigraphic data analysis to evaluate compartmentalization in a low well density reservoir (Z-2), onshore Niger Delta. The aim was to identify areas of bypassed hydrocarbon accumulations during production due to compartmentalization. Structural modelling of the Z-2 reservoir identified three intra-reservoir faults that could lead to possible compartmentalization of the reservoir. Z-2 reservoir was interpreted as early transgressive systems tract normal regressive sediments based on sequence stratigraphic techniques used in the modelling. Z-2 reservoir is bounded below and above by layers of shale about 180–200 ft thick, which provides a good seal for the reservoir. Sequential Gaussian simulation algorithm was used to distribute the modelled petrophysical properties in the static model. Modelled porosity, permeability, and NTG ranges are 5–30 %, 1–10,000 mD, and 0.10–0.98, respectively, through all layers. Z-2 reservoir was divided into two flow units separated by approximately 12-ft-thick shale unit, which could act as a barrier to flow between the zones. Fault analysis was done using Shell structural and fault analysis plug-in in Petrel to determine the shale gauge ratio, fault permeability, and fault zone thickness of the relevant intra-reservoir faults. Fault juxtaposition analysis shows sand-on-sand juxtaposition at the fault tips. Further analysis shows that fault thickness is within the gas crossflow range of (0–0.6 ft) and shale gouge ratio for all three faults falls within the ranges of 0–100 % with a significantly higher percentage of the areas below 35 % in fault 3. Fault 1 will not allow gas crossflow, while <20 % of the juxtaposed areas in fault 2 are within the range to permit gas crossflow. Fault 3 which has a low SGR and high permeability relative to the other faults is not interpreted to be sealing. Fault zone permeability for parts of fault 1 is <1 mD while parts of faults 2 and 3 are >1 mD. The Z-2 reservoir stands the risk of being compartmentalized into two hydrocarbon accumulations (‘X’ and ‘Y’) during production. The total GIIP for Z-2 is 1668 Bscf and with the present well positions and configurations; the production of about 20 % of the GIIP is at risk of being bypassed. Future wells should be planned to appraise ‘X’ and ‘Y’ accumulations.

Integrated workflow approach to static modeling of Igloo R3 reservoir, onshore Niger Delta, Nigeria

We constructed a 3D static model of the R3 reservoir at the Igloo Field, Onshore Niger Delta, by integrating the 3D seismic volume, geophysical well logs, and core petrophysical data. In this model, we used a combined petrophysical-based reservoir zonation and geostatistical inversion of seismic attributes to reduce vertical upscaling problems and improve the estimation of reservoir properties between wells. The reservoir structural framework was interpreted to consist of three major synthetic faults; two of them formed northern and southern boundaries of the field, whereas the other one separated the field into two hydrocarbon compartments. These compartments were pillar gridded into 39,396 cells using a [Formula: see text] dimension over an area of [Formula: see text]. Analysis of the field petrophysical distribution showed an average of 21% porosity, 34% volume of shale, and 680-mD permeability. Eleven flow units delineated from a stratigraphic modified Lorenz plot were used to define the reservoir’s stratigraphic framework. The calibration of acoustic impedance using sonic- and density-log porosity showed a 0.88 correlation coefficient; this formed the basis for the geostatistic seismic inversion process. The acoustic impedance was transformed into reservoir parameters using a sequential Gaussian simulation algorithm with collocated cokriging and variogram models. Ten equiprobable acoustic impedance models were generated and further converted into porosity models by using their bivariate relationship. We modeled the permeability with a single transform of core porosity with a correlation coefficient of 0.86. We compared an alternative model of porosity without seismic as a secondary control, and the result showed differences in their spatial distributions, which was a major control to fluid flow. However, there were similarities in their probability distribution functions and cumulative distribution functions.

Error modeling based on geostatistics for uncertainty analysis in crop mapping using Gaofen-1 multispectral imagery

With the development of remote sensing technology, its applications in agriculture monitoring systems, crop mapping accuracy, and spatial distribution are more and more being explored by administrators and users. Uncertainty in crop mapping is profoundly affected by the spatial pattern of spectral reflectance values obtained from the applied remote sensing data. Errors in remotely sensed crop cover information and the propagation in derivative products need to be quantified and handled correctly. Therefore, this study discusses the methods of error modeling for uncertainty characterization in crop mapping using GF-1 multispectral imagery. An error modeling framework based on geostatistics is proposed, which introduced the sequential Gaussian simulation algorithm to explore the relationship between classification errors and the spectral signature from remote sensing data source. On this basis, a misclassification probability model to produce a spatially explicit classification error probability surface for the map of a crop is developed, which realizes the uncertainty characterization for crop mapping. In this process, trend surface analysis was carried out to generate a spatially varying mean response and the corresponding residual response with spatial variation for the spectral bands of GF-1 multispectral imagery. Variogram models were employed to measure the spatial dependence in the spectral bands and the derived misclassification probability surfaces. Simulated spectral data and classification results were quantitatively analyzed. Through experiments using data sets from a region in the low rolling country located at the Yangtze River valley, it was found that GF-1 multispectral imagery can be used for crop mapping with a good overall performance, the proposal error modeling framework can be used to quantify the uncertainty in crop mapping, and the misclassification probability model can summarize the spatial variation in map accuracy and is helpful for satisfying the need for reliable local estimates for detailed precision management planting.

Application of Direct Sampling multi-point statistic and sequential gaussian simulation algorithms for modelling uncertainty in gold deposits

Application of Direct Sampling multi-point statistic and sequential gaussian simulation algorithms for modelling uncertainty in gold deposits

Monthly karstic spring flow forecasting using a sequential gaussian simulation technique

Analyzing groundwater hydrologic equations related to karstic aquifers and spring hydrograph simulation have become the focus of many researches. Having double or triple porosity structure, mixed flow nature, and varying conduit permeability have made these formations become complex heterogenic systems with great temporal and spatial hydrodynamic variability. In this paper, a conditional sequential gaussian simulation (SGS) is used to simulate monthly flow data of five karstic springs with different hydrogeological properties, located in Zagros Mountain Chain, in western Iran. To evaluate the performance of the SGS algorithm, the results are compared with those of an autoregressive integrated moving average (ARIMA) model. The results demonstrate the efficiency of the SGS model in simulation of monthly flows compared to the ARIMA model. They also show the suitability of this model for handling uncertainty associated with karstic spring flows through generation of several equally probable stochastic realizations.

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

The main purpose of this work is the uranium spatial distribution patterns in groundwater, within the Águeda river transboundary watershed (Portugal-Spain). Mineral resources occur distributed throughout the watershed, mainly sulphide and uranium minerals. Sixty-five groundwater samples were analyzed. Geostatistical modeling was used, throughout conventional variography and Sequential Gaussian Simulation algorithm, to model the groundwater uranium spatial distribution. A hundred simulations, differing in their initial random-number seed, were performed. Spatial uncertainty evaluation allowed the definition of future monitoring and sampling strategies as well as the measurement of remediation possibilities. Uranium hot spots are strongly embedded in the central area (Ciudad Rodrigo).

Static Reservoir Modeling Using Well Log and 3-D Seismic Data in a KN Field, Offshore Niger Delta, Nigeria

This study focuses on the application of 3D static model using 3-D seismic and well log data for proper optimization and development of hydrocarbon potential in KN field of Niger Delta Province. 3D Seismic data were used to generate the input interpreted horizon grids and fault polygons. The horizon which cut across the six wells was used for the analysis and detailed petrophysical analysis was carried out. Structural and property modeling (net to gross, porosity, permeability, water saturation and facies) were distributed stochastically within the constructed 3D grid using Sequential Gaussian Simulation and Sequential Indicator Simulation algorithms. The reservoir structural model show system of different oriented growth faults F1 to F6. Faults 1 and Fault 4 are the major growth faults, dipping towards south-west and are quite extensive. A rollover anticline formed as a result of deformation of the sediments deposited on the downthrown block of fault F1. The other faults (2, 3, 5 and 6) are minor fault (synthetic and antithetic). The trapping mechanism is a fault assisted anticlinal closure. Results from well log analysis and petrophysical models classified sand 9 reservoir as a moderate to good reservoir in terms of facies, with good porosity, permeability, moderate net to gross and low water saturation. The volumetric calculation of modeled sand 9 horizon reveals that the (STOIIP) value at the Downthrown and Ramp segment are 15.7 MMbbl and 3.8 MMbbl respectively. This implies that the mapped horizon indicates hydrocarbon accumulation in economic quantity. This study has also demonstrated the effectiveness of 3-D static modeling technique as a tool for better understanding of spatial distribution of discrete and continuous reservoir properties, hence, has provided a framework for future prediction of reservoir performance and production behavior of sand 9 reservoir. However, more horizontal wells should be drilled to enhance optimization of the reservoir.

Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation

Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps.