Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- southeast general trend. Mishrif Formation is the important middle cretaceous carbonate formation in the stratigraphic column of southern Iraq. The result of applying spatial data analysis showed the nature and quantitative summary of data and so it would be easy to remove the skewness and improve the normality of the petrophysical properties for suitable distribution by the algorithms. It also showed that unit MB1 in Mishrif Fromation contains good properties in which high porosity (0.182) and permeability (7.36 md) with low values of water saturation (0.285) that make it suitable for the accumulation of oil.
Records of two regionalized variables were processed for each of porosity and permeability of reservoir rocks in Zubair Formation (Zb-109) south Iraq as an indication of the most important reservoir property which is the homogeneity,considering their important results in criterion most needed for primary and enhanced oil reservoirs.The results of dispersion treatment,the statistical incorporeal indications,boxes plots,rhombus style and tangents angles of intersected circles indicated by confidence interval of porosity and permeability data, have shown that the reservoir rocks of Zubair units (LS),(1L) and (DJ) have reservoir properties of high quality,in contrast to that of Zubair units (MS) and (AB)which have reservoir properties of less q
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This study aims at making formation evaluation for Mishrif Formation in three wells within Noor Oilfield which are: No-1, No-2 and No-5. The study includes calculations of shale volume and porosity, water saturation using Archie method, measuring the bulk volume of water (BVW) and using Buckle plot, as well as measuring the movable and residual hydrocarbons. These calculations were carried out using Interactive Petrophysics (IP) version 3.5 software as well as using Petrel 2009 software for structural map construction and correlation purposes. It was found that the Mishrif Formation in Noor Oilfield is not at irreducible water saturation, though it is of good reservoir characteristics and hydrocarbon production especially at the upper pa
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Five oil sample of Mashrif and Nahr Umr Formation for Amarah oil field,
southern Iraq, were taken and analyzed in Geo Mark laboratory in USA center in
order to determine the bulk properties of crude oils and carbon isotopes for these
samples in addition to determine biomarker parameters using Gas
Chromatography(GC), and Gas Chromatography Mass Spectrometry )GCMS
(analytical technique. According to these biomarker analyses of the two formation, it
is indicated that they are non-degraded, marine, non-waxy, derived from
carbonate source and deposition in anoxic marine environment. This study also
showed that the bulk properties (terpanes and steranes) of Amarah oil field are one
family, and the source rocks contai
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Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach
Most frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagn
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