Porosity and permeability are the most difficult properties to determine in subsurface reservoir characterization. The difficulty of estimating them arising from the fact that porosity and permeability may vary significantly over the reservoir volume, and can only be sampled at well location. Secondly, the porosity values are commonly evaluated from the well log data, which are usually available from most wells in the reservoir, but permeability values, which are generally determined from core analysis, are not usually available. The aim of this study is: First, to develop correlations between the core and the well log data which can be used to estimate permeability in uncored wells, these correlations enable to estimate reservoir permeability at the "flow unit" scale. Second, generate spatial distributions of reservoir properties (porosity and permeability). These distributions of reservoir properties are the basis for a geological model that can be used to perform reservoir modeling and reservoir management tasks. The Alternating Conditional Expectation (ACE) technique has been used and tested in this study. ACE is classified as non-parametric method against the parametric methods which are represented by the traditional multiple regression. A comparison between these two methods shows the superiority of the ACE method correlations for four wells in an Iraqi oil field. General correlations for unit (a) and (b) are also presented. These correlations can be used to estimate permeability in uncored wells with a good approximation
Shehadt 683 fish of different fish present in the tank Haditha Dam during the period of February 1993 and the end Maes 1994 to identify the nutritional content of the digestive tract or the stomach for these fish and their impact on the environment of the tank and found that Fish fine linen and Alhalg animal feeding predators feed on small fish, mainly the fish brown plantnutrition
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Geologic modeling is the art of constructing a structural and stratigraphic model of a reservoir from analyses and interpretations of seismic data, log data, core data, etc. [1].
A static reservoir model typically involves four main stages, these stages are Structural modeling, Stratigraphic modeling, Lithological modeling and Petrophysical modeling [2].
Ismail field is exploration structure, located in the north Iraq, about 55 km north-west of Kirkuk city, to the north-west of the Bai Hassan field, the distance between the Bai Hassan field and Ismael field is about one kilometer [3].
Tertiary period reservoir sequences (Main Limestone), which comprise many economica
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Natural fractures provide an important reservoir space and migration channels for oil and gas reservoirs and control the reservoir potential. Therefore, it is essential to understand the methods for identifying accurate reservoir permeability and characterizing reservoir fractures. In particular, using conventional measurements to identify permeability and characterize fractures is very expensive. While using conventional logging data is very challenging, and an efficient characterization correlation method is urgently needed. In this paper, we have evaluated reservoir potential based on the sensitivity of sonic scanner tools to fluid mobility, maximum stress direction, and fractures presence. This tool provides a continuous estimat
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In this paper, we introduce three robust fuzzy estimators of a location parameter based on Buckley’s approach, in the presence of outliers. These estimates were compared using the variance of fuzzy numbers criterion, all these estimates were best of Buckley’s estimate. of these, the fuzzy median was the best in the case of small and medium sample size, and in large sample size, the fuzzy trimmed mean was the best.
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Gumbel distribution was dealt with great care by researchers and statisticians. There are traditional methods to estimate two parameters of Gumbel distribution known as Maximum Likelihood, the Method of Moments and recently the method of re-sampling called (Jackknife). However, these methods suffer from some mathematical difficulties in solving them analytically. Accordingly, there are other non-traditional methods, like the principle of the nearest neighbors, used in computer science especially, artificial intelligence algorithms, including the genetic algorithm, the artificial neural network algorithm, and others that may to be classified as meta-heuristic methods. Moreover, this principle of nearest neighbors has useful statistical featu
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A new approach for baud time (or baud rate) estimation of a random binary signal is presented. This approach utilizes the spectrum of the signal after nonlinear processing in a way that the estimation error can be reduced by simply increasing the number of the processed samples instead of increasing the sampling rate. The spectrum of the new signal is shown to give an accurate estimate about the baud time when there is no apriory information or any restricting preassumptions. The performance of the estimator for random binary square waves perturbed by white Gaussian noise and ISI is evaluated and compared with that of the conventional estimator of the zero crossing detector.
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