Farzi R.,University of Tehran |
Bolandi V.,amran University Of Ahvaz |
Kadkhodaie A.,Curtin University Australia |
Kadkhodaie A.,University of Tabriz |
And 2 more authors.
Journal of Natural Gas Science and Engineering | Year: 2017
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. © 2017
Alizadeh B.,amran University Of Ahvaz |
Sarafdokht H.,amran University Of Ahvaz |
Rajabi M.,University of Tehran |
Opera A.,National Iranian Oil Company |
Janbaz M.,amran University Of Ahvaz
Organic Geochemistry | Year: 2012
There are several source rock units in the Zagros Basin, but the Cretaceous Kazhdumi and Paleogene Pabdeh formations probably have produced the majority of the commercial hydrocarbons in this area. Among the hydrocarbon provinces of Iran, the Dezful Embayment, which is located southwest of Zagros Mountains, is one of the most prolific regions in the Middle East. Numerous studies have been made in the northern part of the Dezful Embayment, but relatively few have been done in its southern part. The present study focuses on organic matter characterization of two potential source rocks (Kazhdumi and Pabdeh formations) in southern part of the Dezful Embayment. Cuttings samples (114) were collected from 10 wells and evaluated using Rock-Eval pyrolysis and organic petrography in order to characterize the content and type of organic matter and thermal maturity. The results showed that the average total organic carbon (TOC) content of Kazhdumi and Pabdeh formations are 2.48 and 1.62wt%, respectively. The highest TOC contents for both formations are found in the northern compartment and decreased gradually toward the south. Pyrolysis data reveal that organic matter has a fair to very good hydrocarbon generation potential and are classified as Type II-III and Type III. Rock-Eval T max and vitrinite reflectance show that the majority of samples are in the early mature to mature stage of the oil generation window. © 2012 Elsevier Ltd.
Alizadeh B.,amran University Of Ahvaz |
Alizadeh B.,Petroleum Geology and Geochemistry Research Center |
Najjari S.,amran University Of Ahvaz |
Kadkhodaie-Ilkhchi A.,University of Tabriz
Computers and Geosciences | Year: 2012
Intelligent and statistical techniques were used to extract the hidden organic facies from well log responses in the Giant South Pars Gas Field, Persian Gulf, Iran. Kazhdomi Formation of Mid-Cretaceous and Kangan-Dalan Formations of Permo-Triassic Data were used for this purpose. Initially GR, SGR, CGR, THOR, POTA, NPHI and DT logs were applied to model the relationship between wireline logs and Total Organic Carbon (TOC) content using Artificial Neural Networks (ANN). The correlation coefficient (R 2) between the measured and ANN predicted TOC equals to 89%. The performance of the model is measured by the Mean Squared Error function, which does not exceed 0.0073. Using Cluster Analysis technique and creating a binary hierarchical cluster tree the constructed TOC column of each formation was clustered into 5 organic facies according to their geochemical similarity. Later a second model with the accuracy of 84% was created by ANN to determine the specified clusters (facies) directly from well logs for quick cluster recognition in other wells of the studied field. Each created facies was correlated to its appropriate burial history curve. Hence each and every facies of a formation could be scrutinized separately and directly from its well logs, demonstrating the time and depth of oil or gas generation. Therefore potential production zone of Kazhdomi probable source rock and Kangan- Dalan reservoir formation could be identified while well logging operations (especially in LWD cases) were in progress. This could reduce uncertainty and save plenty of time and cost for oil industries and aid in the successful implementation of exploration and exploitation plans. © 2011 Elsevier Ltd.
Nourafkan A.,amran University Of Ahvaz |
Kadkhodaie-Ilkhchi A.,University of Tabriz
Journal of Petroleum Science and Engineering | Year: 2015
Characterization of geomechanical parameters of hydrocarbon reservoirs such as compressional and shear wave velocities is a main component of petrophysical, geophysical and geomechanical studies. Compressional wave velocity is derived from sonic log. However, Vs is either obtained from core analysis in the laboratory or dipole sonic imager (DSI) tools which are both very expensive and time consuming. Recently, several methods of artificial intelligence techniques have been used to predict this fundamental parameter by using well log data. In this paper, a new methodology is presented for shear wave velocity estimation by integration of stochastic optimization in the structure of a fuzzy inference system. The proposed model, which is called ant colony-fuzzy inference system (ACOFIS), is based on the integration of fuzzy reasoning and ant colony optimization algorithm. The methodology is illustrated by using a case study from Cheshmeh-Khosh oilfield. Comparison of the results shows that the proposed novel and hybrid scheme can sufficiently improve the performance of the shear wave velocity estimation problem. Meanwhile, the developed ACOFIS model can serve as an effective tool for estimation of other reservoir rock properties. © 2015 Elsevier B.V.
Bahram A.,amran University Of Ahvaz |
Majid A.,amran University Of Ahvaz |
Hossien H.S.,amran University Of Ahvaz |
Abbas J.A.,amran University Of Ahvaz
Organic Geochemistry | Year: 2011
The Upper Triassic-Middle Jurassic sedimentary succession in the Tabas Basin, with a thickness of about 1600. m, provides a case showing geochemical property changes through the Triassic-Jurassic boundary. The studied section (Kamarmacheh Kuh) is composed of the marine Nayband Formation (Norian-Rhaetian) overlain by siliciclastic sediments of Ab-e-Haji Formation (Lower Jurassic-Aalenian). Detailed geochemical analyses were conducted on selected samples from both formations and the results were used to infer paleo-depositional conditions. Most of the studied samples contain <1. wt% TOC composed mostly of oxidized organic matter with insignificant generative potential. Extract analysis of four representative samples indicate that the rocks also contain minor amounts of preserved algal organic matter along with a secondary contribution of higher plant organic matter from the adjacent watershed. Biomarker analyses show subtle variations in the relative contribution of land plant material that are consistent with the widespread occurrence of coal seams in the upper parts of the Nayband and basal parts of the Ab-e-Haji formations. Although the samples from the Kamarmacheh Kuh Section have low source potential, the extractable hydrocarbons indicate that conditions existed that were conducive to organic matter preservation and that regions of the Tabas Basin with higher primary productivity or lower sedimentation rates may have greater potential. © 2011 Elsevier Ltd.