Well logs are principal sources of subsurface geological information. They provide significant information on mineralogical composition, texture, sedimentary structures and petrophysical properties such as porosity and permeability. By compiling data from various well logs, one can discriminate sedimentary units with comparable log characteristics. Sedimentary units with similar fluid flow and capacity are named Rock Type. Rock Type determination is the most important task in reservoir characterization of oil bearing formations. Rock Type may be determined using different data sets but their definition on the basis of wire line logs is most common. Multivariate cluster analysis (as the best method of data grouping) is one of the most accurate and effective methods in oil bearing reservoir zonation. The method is applied on both detrital and carbonates Rocks. This method gets more support by improvements in algorithms and statistics. Proper combination of logs and appropriate algorithm will increase the accuracy, reliability and effect of the method. Similar faces may have different log responses due to diverse factors that affect the logs. Since using statistical methods and procedures are mandatory, in clustering procedure data are grouped with minimum distance and maximum homogeneity. It is obvious that distinct geological parameters can be related to a group of data, which are to be used by geologists for further interpretation. For this calculation, all log readings are considered as "observations" and the used logs as the "values of the observations".There are several ways to compute the distance between objects. The "Standardized Euclidean" distance is used here in form the MATLAB software, because more accurate results are obtained with this procedure. By grouping log data in multidimensional space (equal dimensions with number of logs), each point (reading) can be related to a group of data (Rock Type). High resolution Rock typing with reliable conclusions can be inferred with this procedure using pure mathematical formula in which there is no need to regression equations or trainings. In this method, any geological parameter described from other sources such as cores and thin sections can be related to wells with comparable Rock Types. The accuracy and reliability of defined Rock Types can be examined in wells from which suitable cores are available. Results from such a comparison provide a fundamental base for study of wells with poor core and cutting data.Using MATLAB software, this study testifies a new simple method for Rock Type determination of Asmari Formation in Marun Field. The reliability of the method is examined by correlation of the resultant Rock Types with those of inferred from cores. Result from such a correlation indicates the reliability of method in Rock Type determination.

Paratuberculosis caused byMycobacterium avium subsp. paratuberculosis (Map) is a progressive, chronic and noncurable granulomatous enteritis. Paratuberculosis affects ruminants worldwide. The earliest reports on paratuberculosis in Iran date back to 1960’s when the disease was diagnosed in imported animals of the Abadan Oil Company’s farm. In the work presented here 13 field MAP isolates including 4 ovine and 3 caprine isolates from Isfahan, 6 bovine isolates from Zanjan and Alborz provinces plus 2 vaccinal strains of 316F and III & V were subjected to PCR-IS900, PCR-F57 for identification and also PCR-Collins for typing. In consequence, while an identical PCR protocol was successfully developed to simultaneously perform all the three experiments, the 15 tested bacteria were all shown to be MAP Type II (cattle Type). In epidemiological sense, when the size of national goat, sheep and cattle herds of Iran is taken into account, circulation of MAP Type II is intriguing.

The class of strong stochastic Runge{Kutta (SRK) methods for stochas-tic differential equations with a commutative noise proposed by R o ler (2010) is considered. Motivated by Komori and Burrage (2013), we design a class of explicit stochastic orthogonal Runge{Kutta Chebyshev (SRockC2) meth-ods of strong order one for the approximation of the solution of It^o SDEs with an m-dimensional commutative noise. The mean-square and asymptotic stability analysis of the newly proposed methods applied to a scalar linear test equation with a multiplicative noise is presented. Finally, some numer-ical experiments for stochastic models arising in applications are given that con rm the theoretical discussion.

The Garmichay area in the north of Mianeh, NW Iran, features schists, amphibolites, calc-silicates, marbles and granitoids. Granitoids are of S-and A-Type nature. Partial melting is the main generation mechanism, the progenitor is a pelite enriched in muscovite and the major melting reaction is muscovite dehydration. The ASI for the S-Type granite varies between 1. 12-1. 5 indicating peraluminous character and crustal origin. Both granitic Types are enriched in LREE in relation to HREE with negative Eu anomalies. Lan/Ybn ranges are 1. 49-3. 09 and 1. 34-1. 99 for the S-Type and the A-Type granites, respectively. Moreover, Lan/Gdn varies between 2. 45 and 5 for the S-Type granite and 1. 53 and 1. 75 for the A-Type granite. The S-Type granite has been generated in a collisional zone during Assyntic Orogeny. The A-Type granite, however, is formed in a within-plate environment. The A-Type granite belongs to A1 subType formed in a continental rifting setting. The crystallization temperatures for the S-Type and the A-Type granites vary from 650 to 750 º C and 800 to 850 º C، respectively.

Kangan Formation in the Study Area is about 444 meters thick and is mainly composed of Limestone and dolomite with randome interbeds of Anhydrites.In this article, Rock Types are determind by Electro Facies and Hydrolic Flow Units (HFU) method using the Core data, Gamma -Ray, Density and Neutron Porosity petrophysical logs of one of the Field’s wells.Rock Types determination can be a practical solution for categorizing reservoir’s facies aiming assesment of reservoir Rock’s petrophysical properties and diffrentiating high potential producer zones from low potential ones. Hence clustering method based on Multi Resolution Graph-Based Clustering (MRGC) which is a “Non Parametric” statistical method, of umpteenth near value and graphical presentation of data, was implyed on Neutron Porosity, Density and Gamma-Ray logs to re-construct equivalent electro facies to the ones obtained from core. At the end, final clusters are formed by combining small clusters where five facies were recognized. The used method in this research eleminates the need of repeated coring and leads to a major cost and time saving. More over the Rock Types were also determind by Hydrolic Flow Units method. Hydrolic Flow Units is a method for categorizing the variety of Rocks according to their flowing properties based on geological and physical flow in pore scale. Five Rock Types were determind by this method also, which had good conformity with the ones from previous method in many locations. In some locations of course the results were not as good which could be as a result of non uniform sampling and human error.

Electrofacies studies play an important role in the development process of a field. In these studies, poro-perm data of core analysis and well logs data can be used for reservoir simulation. In the present research, core analysis data (such as porosity and permeability) of selected drilled wells in Maroun oil field (SW Iran) divided into four flow units using regional fluid index method. Initial electrofacies (EF) model were determined using well logs data through different methods: SOM, MRGC, and DYNAMIC. Determined facies of these methods were correlated with the flow units. The results indicated that SOM method is in the best concordance and so it was selected for classification of electrofacies. The initially nine electrofacies were reduced to 4 electrofacies Type due to the similarity of some parameters such as effective porosity and shale volume. Reservoir quality was improved from EF-1 to EF-4. To valid the accuracy of the electrical Rock Type by neural networks, these electrofacies was correlated with capillary pressure data. Due to well correlation of determined electrofacies with capillary pressure data, the model was propagated to other wells of this field. This created model was able to separate different parts of the reservoir. In this model, different parts of the reservoir were determined in terms of reservoir quality. The model can be applied for providing of static model of the reservoir.

Despite the fact that kerogen decomposition has not been yet very well understood, it is possible to obtain kinetic parameters which correlate with both empirical and natural geologic data by pyrolysing samples in closed and open systems. In this study, in order to determine hydrocarbon source Rocks kinetic parameters, synthetic maturation by using open pyrolysis system (Rock-Eval) was used. In order to determine the equation of the real pyrolysis temperatures, the Rock-Eval pyrolysis oven was calibrated against three standard samples. Three source Rock samples from three well known Iranian source Rock formations namely Pabdeh, Garu and Kazhdumi were anafyzed at temperature rates of 25, 15, 10 and 5°c/min and their kinetic parameters including activation energy distribution, Arrehnus coefficient and the amount of generated hydrocarbon were determined.