Stratigraphy and Palaeontology
Mahmoud sharafi; Nasim Mousavi; Mehran Moradpour; Bijan Biranvand; ebrahin Abdollahi; Hossein Soltani
Abstract
Based on lithostratigraphy analysis, Cheleken Formation in the studied section of the Gorgan plain, subdivided into lower sandstone and upper mudstone/marl units. Based on petrographic analysis, the sandstone sediments include low textural and compositional maturity litharenite and sublitharenite. Conglomerates ...
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Based on lithostratigraphy analysis, Cheleken Formation in the studied section of the Gorgan plain, subdivided into lower sandstone and upper mudstone/marl units. Based on petrographic analysis, the sandstone sediments include low textural and compositional maturity litharenite and sublitharenite. Conglomerates are polymictite orthoconglomerate with variables carbonate and chert grains. High percentage of the porosity as vuggy, channels and fractures in the sandstone and conglomerates and even mudstone deposits displays high reservoir potentional for the studied sediments and hence necessitates the exploration studies in the Iranian part of the SCB. Based on the youngest nannofossil species, a late Miocene to middle Pliocene (?) age is defined for the Cheleken Fm. in the studied area. Nannofossils distribution of the studied succession displays the SCB was connected to the Black Sea and Mediterranean Basin in the late Miocene- early Pliocene and the Pleistocene and was isolated in the main part of the Pliocene.
Petroleum geology
Seyed Mahmoud Fatemi Aghda; Mashaallah Taslimi; Ahmad Fahimifar
Abstract
The main aim of this study is to examine the feasibility of estimation of flow zone indicator in carbonate rocks by integration of hydraulic flow unit concept a nuclear magnetic resonance technology. The two main permeability models Timur-Cotes and mean T2 models, because of worldwide usage of these ...
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The main aim of this study is to examine the feasibility of estimation of flow zone indicator in carbonate rocks by integration of hydraulic flow unit concept a nuclear magnetic resonance technology. The two main permeability models Timur-Cotes and mean T2 models, because of worldwide usage of these models, were used for evaluating the ability of nuclear magnetic resonance to estimate the flow zone indicator. One of the most important points in this study is the use of the experimental results of the nuclear magnetic resonance in laboratory on core that is never done in Iran. In this study, 24 carbonate samples were selected, and porosity, permeability and nuclear magnetic resonance experiments were performed. Then, using the results of the porosity and permeability tests, the flow zone indicator was determined and was considered as an index for evaluating the accuracy of the nuclear magnetic resonance method. Using the parameters obtained from the nuclear magnetic resonance test and nuclear magnetic resonance permeability models, flow zone indicator was estimated and compared with the core flow zone indicator. According to the results, it seems that the nuclear magnetic resonance permeability models, with the routine coefficients, do not have the proper ability to estimate the flow zone indicator, and it is necessary to correct the coefficients according to the lithology of rocks.
Mostafa Moradi; Asadollah Mahboubi; Mohammad Khanehbad; Ali Ghabieshavi
Abstract
The Asmari Formation is the main reservoir rock for the Aghajari oilfieild. It is composed of about 400-meter limestone, dolostone and interlayers of sandstones. Study of 1200 meters drilling cores, 2800 microscopic thin sections, 12 SEM analyses along with 125 routine core tests in 5 cored wells indicate ...
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The Asmari Formation is the main reservoir rock for the Aghajari oilfieild. It is composed of about 400-meter limestone, dolostone and interlayers of sandstones. Study of 1200 meters drilling cores, 2800 microscopic thin sections, 12 SEM analyses along with 125 routine core tests in 5 cored wells indicate that the Asmari Formation was effected by various diagenetic processes such as micritization, compaction, cementation, fracturing, dissolution and dolomitization. Some of these processes (e.g. dissolution, dolomitization and fracturing) have constructive effects on the reservoir quality and created wide variety of porosity types including vuggy, intercrystaline and channel in upper parts of the Asmari Formation. Destructive diagenetic processes (micritization, compaction and cementation) have destroyed pore spaces and make the lower parts of the Asmari (specially zone 5) to a non-reservoir unit. Porosity-permeability plots on the Lucia's diagram show sandstones and carbonates rocks with interparticle porosities have good reservoir qualities and always plot on upper parts of classes 1-3. Samples with fracture porosity mainly plot on upper part of class 1. This shows fractures has no considerable role in promoting the porosity, but they strongly increase permeability. Dolostones and the rocks with vuggy porosity have plotted on classes 2 and 3 (high porosity, relatively high permeability). Paragenetic secession of the Asmari Formation shows the diagenetic processes occurred syn-sedimentary on sea floor, after sedimentation during the low-deep burry and uplift. The results of this study can be useful in detection of reservoir zones, increasing of hydrocarbon production and enhanced recovery of this oilfield.
E. Asadi Mehmandosti; Sh. Alivand; H. Ghalavand; A. R. Rostami
Abstract
Nuclear magnetic resonance (NMR) is a test method that has had basic influence on the accuracy of reservoir parameters prediction. In this study, petrophysical properties of the Ilam Formation in one oil field of the Abadan plain were investigated by Nuclear magnetic resonance method, and core analysis ...
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Nuclear magnetic resonance (NMR) is a test method that has had basic influence on the accuracy of reservoir parameters prediction. In this study, petrophysical properties of the Ilam Formation in one oil field of the Abadan plain were investigated by Nuclear magnetic resonance method, and core analysis and thin sections data were used to validate the test results for the first time. The Nuclear magnetic resonance method was used to determine reservoir parameters such as porosity, permeability and rebuilding of capillary pressure curve in studied oil field. Correlation between petrophysical results of Nuclear magnetic resonance method, core data and microscopic thin sections indicates effectiveness of this method in determining the reservoir parameters. In addition, evaluation of the Ilam Formation carbonate with NMR method indicates that the studied reservoir at depths of 2890 to 2846 m has suitable reservoir quality in terms of hydrocarbon storages in studied oil field in Abadan plain.
A Abbaszadeh shahri; R Hosseini; F Rezaei; K Mehdizadeh; N Panaei
Abstract
Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human ...
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Artificial Neural Network methods (ANN) are computational methods, which capable to predict a specific log or classify different data. Unlike the digital computers, which require the completely definite and distinguished rules, the ANN methods do not need a pure mathematical model; rather like the human brain has the ability to learn by recognized and determined examples. The target of the present paper is to establish and prove the Petrophysical Analysis as powerful approach in prediction and diagnosis of rock reservoir porosity by use of petrophysical logs, in which by a high accuracy suggested Petrophysical Analysis based solution the porosity can be estimated using conventional logging data. On the basis of the available petrophysical data, the proposed method was examined in one of the southwest oil field of Iran. The obtained results of network analysis conditioning to reliability to data with different tests such as regression, root mean square and SPLine showed that the amount of network error in terms of available data in engineering range with a high acceptable safety factor could be used to predict and estimate porosity. This method with ability of cost reduction and viability can help and provide a large variety in this field for further extended research.
S Parham; M.R Kamali
Abstract
The Dalan Upper member (Permian) with carbonates and evaporite interlayer is one of the most important gas reservoirs in the folded Zagros area and Persian Gulf. In this investigation porosity types, their genesis and controlling factors have been studied at Surmeh surface section and a subsurface section ...
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The Dalan Upper member (Permian) with carbonates and evaporite interlayer is one of the most important gas reservoirs in the folded Zagros area and Persian Gulf. In this investigation porosity types, their genesis and controlling factors have been studied at Surmeh surface section and a subsurface section in Persian Gulf. Based on the new genetic classification of Ahr (2008) for carbonate porosity, porosity is created or altered by hybrids of depositional processes, diagenetic processes and mechanical fracturing in the studied intervals. In subsurface samples, porosity is hybrid of depositional and diagenetic types. In grain-supported microfacies, like ooidgrainstone related to the shoal environment, interparticle porosity is created which is a type of depositional porosity. Moldic, vuggy and intercrystallineporosity, which are diagenetic types of porosity,were formed in the later stages of diagenesis. Therefore, porosity in this microfacies is facies-selective and facies map can be used as a proxy for porosity distribution map. In surface section, besides depositional and diagenetic porosity, fracturing and brecciation are also significant. Brecciationoccured as a result of dissolution of anhydrite of Nar Member and formed the solution collapse breccias. Active tectonic in the folded Zagros belt and folding are another possible sources of fracturing in the surface samples. The properm data of routine analysis shows that the reservoir characteristic of the studied interval is heterogeneous.So, it has been compartmentalized into six zones with different reservoir qualities from poor to very good.
A. Kamkar Rouhani; M. Zakeri
Abstract
In order to obtain more accurate results from application of the method of artificial neural networks, instead of selection of the best network determined by trial and error process, we suitably combine the results of several networks that is called committee machine, to reduce the error, and thus, increasing ...
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In order to obtain more accurate results from application of the method of artificial neural networks, instead of selection of the best network determined by trial and error process, we suitably combine the results of several networks that is called committee machine, to reduce the error, and thus, increasing the accuracy of the output results. In this research, ensemble combination of single artificial neural networks has been used in order to estimate the effective porosity of Kangan gas reservoir rock in South Pars hydrocarbon field. To achieve this goal, well logging data of 4 wells in the area at the depth interval corresponding to Kangan formation were used. Acoustic, density, gamma ray and neutron porosity well log data were assigned as the input of the networks while the effective porosity data were considered as the output of the networks. Back- propagation single neural networks having different structures were trained using regularization method and their results were assessed. Then, the networks with the best results, i.e. contained minimum mean of squares of errors in the test step, were selected for making ensemble combinations. To determine the weighting coefficients of the networks in the linear ensemble combinations, we applied three methods of simple averaging, Hashem’s optimal linear combination and non-analytical optimal linear combination employing genetic algorithm, and their results were compared. The best ensemble combination, in which we had the maximum reduction in mean of squares of errors of the test step compared to the best single neural network, was an optimal linear four-network combination obtained by using genetic algorithm optimization method. This best ensemble combination, compared to the best single neural network, reduced the mean of squares of errors in the training and test steps 3.6% and 11.2%, respectively.
