Determination of TOC is critical to the evaluation of every source rock unit. Methods which are dependent upon extensive laboratory testing are limited by the availability and integrity of the rock samples. Prediction of TOC (Total Organic Carbon) from well Log data being available for the majority of wells being drilled provides rapid evaluation of organic content, producing a continuous record while eliminating sampling issues. Therefore, the ideal method for determining the TOC fraction within source rock units would utilize common well log data. So a model was developed to formulate TOC values in the absence of laboratory TOC measurements from conventional well log data. Consequently, with the assistance of FL (Fuzzy Logic), TOC estimated from well log data with an overall prediction accuracy of 0.9425 for the test set. Following that TOC content of the Kazhdumi formation optimally has been divided into 4 zones using K-means cluster analysis, since searching for patterns is one of the main goals in data mining. There is a general increase in TOC from zone 1 to zone 4. The optimal number of zones has been detected by means of the knee method that finds the

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Permian-Triassic deposited Dalan-Kangan carbonates in the South Pars Field are the host of the world's largest gas field in which, the Silurian shales might have had triggered the majority of the accumulated gas. Although it is believed that Dalan-Kangan has contribution in gas expulsion. One of the most important factors manipulating the source potential evaluation is considered to be total organic carbon (TOC). Since no TOC no source rock will be found in the area. Consequently in present investigation TOC were utilized as a tool to evaluate the organic facies in combination with the intelligent methods. Current study implements ensemble algorithms as a new method in geoscience data appraisal in comparison with conventional intelligent systems. First of all, we applied fuzzy inference system (FIS) and neural network (NN) as traditional intelligent methods and LSBoost (LSB) and Bagging (BG) as ensemble algorithms to estimating the TOC from well log data. In the next step, Savitzky-Golay filter has been exploited for the data smoothing in order to galvanize regression and classification accuracy. Then, organic facies class membership was taken out by cluster analysis of the synthetized TOC values. In the last place, organic facies class membership were predicted using AdaBoost (AB), LogitBoost (LB), GentleBoost (GB) and Bagging (BG) as ensemble algorithms versus FIS and NN as being conventional intelligent systems directly from petrophysical well log data. Experimental results depict that ensemble methods outperform the common intelligent methods in term of regression and classification concepts. Also, Dalan-Kangan contribution in gas expulsion has been proved by detection of high organic rich interval (parts of k3 unit in Upper Dalan) in the field of study.

The Nuclear Magnetic Resonance (NMR) log is amongst the functional techniques in petroleum investigation to segregating the reservoir and non-reservoir horizons precisely; furthermore, the NMR log provides an improved method to determine reservoir petrophysical parameters. Unfortunately, these data are usually sparse since acquiring NMR logs in producing cased wells is not possible and it is one of the most expensive tools in the logging industry thus its associated costs are the major limitation of its usage.Consequently, researchers have recently studied to virtually extract the NMR parameters via other routes.One such route, which we propose here is the possibility of estimating the T2 distribution curve and magnetization decay by establishing a relationship between micro-CT images and NMR parameters by means of artificial neural networks (ANN) and image analysis algorithms. Specifically, two ANN networks were designed, which numerically image features from micro-CT images as inputs, while the amplitude of the magnetization and relaxation time were output parameters. We assessed the procedure by taking the error rate and correlation coefficient into consideration and we conclude that the ANN model is capable of finding logical patterns between image features and NMR responses, and is thus able to reliably predict NMR response behavior. Furthermore, we quantitatively compared ANN and random walk (RW) NMR predictions, and we demonstrate that ANN readily outperforms RW in terms of accuracy.

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