List of Articles اﻧﺘﺮوﭘﯽ

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  1 - Investigation of Gilan index dams' water quality using multivariate methods
  Hanieh Mirbolooki Babak Razdar Matin Mohafezatkar
  10.29252/.5.8.25
  Nowadays, the importance of water is known more than before as a life factor and the axis of sustainable development that to protect and manage it, it needs to be controlled using laboratory tests and various water quality indexes. The purpose of this study was to inves More

  Nowadays, the importance of water is known more than before as a life factor and the axis of sustainable development that to protect and manage it, it needs to be controlled using laboratory tests and various water quality indexes. The purpose of this study was to investigate water quality in diversion dams in Guilan province in which the dams have been ranked using Shannon and TOPSIS entropy methods. The dams included Pasikhan, Shakhzar, Polrud and Tarik and the measured indicators included Ec, pH, TDS, Temperature, SO4, HCO3, Cl, Ca, Mg, Na, TSS, DO, BOD5 and COD. Shannon entropy results showed that among the indicators, the highest index weight is related to TSS with the amount of 0.1973 and the lowest one is related to pH with the amount of zero. Topsis tests results showed that based on the weights derived from entropy and water quality indicators, Pasikhan dam is in the first rank, Polrud dam is in the second rank, Shakhzar dam is in the third rank and Tarik dam is in the last rank. So, according to multivariate selection methods, water quality in different dams with similar conditions can be investigated. Manuscript profile
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  2 - Using information entropy theory and bayesian decision method to identify appropriate parameters for evaluating and discriminating oil facies (mansuri oil field, south of Iran)
  حسین معماریان
  Due to subsurface heterogeneity and existing vagueness in geophysical interpretation, identifying and interpretation of facies in wellbores is always prone to uncertainty and risk. Nowadays several methods have developed for quantitative facies interpretation. These met More

  Due to subsurface heterogeneity and existing vagueness in geophysical interpretation, identifying and interpretation of facies in wellbores is always prone to uncertainty and risk. Nowadays several methods have developed for quantitative facies interpretation. These methods are generally divided into deterministic and stochastic categories. Deterministic methods, in spite of their simple modeling procedure, cannot expose the amount of error or accuracy of the model. On the other hand, stochastic methods, in addition to quantifying the error of the model, can provide the probability of the model’s accuracy in each point of the reservoir. The Bayesian approach is one of the stochastic methods that use conditional probabilities for modeling. This approach, as well as probabilistic modeling of hydrocarbon facies, quantitatively computes the effect of additional data in decreasing the error of the classification. Information entropy theory, by quantifying the intrinsic uncertainty in each model input parameter, can easily provide the selection of valuable parameters. The present study was carried out on one of the wells of Mansuri oil field, south of Iran. After generation of training data by using rock physics techniques and Gassmann’s relation, the value of each input parameter was identified by entropy analysis. Then, by use of Bayesian analysis and valuable parameters, oil facies classification and discrimination was implemented. The five optimum parameters were elastic impedance, compressional wave velocity, shear wave velocity, density and porosity .The amount of error in this method is approximated to be 11 percent. This investigation also showed that gamma ray parameter does not have a drastic positive effect on identification and discrimination procedure of oil facies, which has a good agreement with the results of entropy analysis . Manuscript profile