Abstract Porosity is one of the main variables needed for reservoir characterization. For this volumetric variable, there are many methods to simulate spatial distribution. In article, porosity was analyzed and modeled in local global simulation, Sequential Gaussian simulation (SGS) Random Function (GRFS) were applied. Also, kriging used estimate at specific locations. The purpose work investig...

An understanding of the Earth’s climate system benefits all sectors of the economy and environment. Several challenges faced when modeling the Earth’s climate system include: estimating geographical features of global datasets, making inferences from multiple data-products, and providing diagnostic tools for complex Earth models. Existing geostatistical approaches address these challenges by mo...

The presented paper describes the geostatistical analysis of PSInSAR data. This analysis was preceded by short description of PSInSAR technique. The geostatistical computations showed in this article were performed with the R open-source software containing the gstat package. The analysis contains variograms computing (directional variograms) and ordinary kriging interpolation. The computationa...

A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644–651. A distance matrix that contains the hydrologic distance between any two sites in a study area is needed to fit a geostatistical model using the tail-up and tail-down autocovariance functions. However, the hydrologic distance information needed to model the covariance between flow-connected a...

This paper demonstrates the use and value of stochastic differential equations for modeling space-time data in two common settings. The first consists of point-referenced or geostatistical data where observations are collected at fixed locations and times. The second considers random point pattern data where the emergence of locations and times is random. For both cases, we employ stochastic di...

Seismic reservoir characterization aims to transform obtained seismic signatures into reservoir properties such as lithofacies and pore fluids. We propose a Markov chain Monte Carlo (McMC) workflow consistent with geology, well‐logs, seismic data and rock‐physics information. The workflow uses a multiple‐point geostatistical method for generating realizations from the prior distribution...

The 2006-2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwi...

To provide accurate predictions of flow performance, the numerical model of permeability used by flow simulators must be consistent with all available geological and engineering data. The available data includes core permeability measurements, relevant permeability statistics (particularly, histograms, measures of spatial variability, and correlation with secondary variables such as porosity), ...

Multiple-point statistics are used in geostatistical simulation to improve forecasting of responses that are highly dependent on the reproduction of complex features of the phenomenon that cannot be captured by conventional two-point simulation methods. Inference of multiple-point statistics is often based on a training image that depicts the features that provide the character to the geologica...