The complete characteristics knowledge of clay minerals is necessary in the evaluation studies of hydrocarbon reservoirs. Ten samples taken from two wells in a heterogeneous clastic gas reservoir formation in NE Iran were selected to conduct the transmission Fourier transform infrared spectroscopy (FTIR) tests for the clay mineralogy studies. The FTIR analysis showed that there were clear signs of clay minerals in all samples. The wavenumber region of the clay minerals in FTIR tests was detected to be 3621, 3432, 1034, and 515 cm−1 for illite, 3567, 3432, 1613, 1088, 990, 687, 651, and 515 cm−1 for magnesium-rich chlorite, 3700, 3621, 3432, 1034, 687, and 463 cm−1 for kaolinite, and 3567, 1088, 990, and 463 cm−1 for glauconite. After screening of samples by the FTIR method, the samples were then analyzed by powder X-ray diffraction (PXRD), wavelength dispersive X-ray fluorescence (WDXRF), and scanning electron microscopy (SEM). The PXRD and SEM result showed illite was by far the most common clay present. Kaolinite, magnesium-rich chlorite, and traces of smectite and the mixed-layer clays of both the illite–smectite and chlorite-smectite types were also recognized. The combination of PXRD and WDXRF results could quantify the clay abundances in the each well too. It was concluded that the FTIR analysis successfully could show the absorption bonds of all constituent clays. However, the infrared absorption spectra of mixed-layer clays overlapped those of the respective constituents of each mixed-layer minerals. This can be considered as the evidence of the usefulness of FTIR technique in the screening of the samples for the clay mineralogy studies.

The Shurijeh Reservoir Formation of Neocomian age is represented by a sandstone sequence, occasionally interbedded with shale, in the Gonbadli gas field, Kopet-Dagh Basin, Northeastern Iran. In this study X-ray diffraction (XRD) and X-ray fluorescence (XRF) techniques were used together to characterize the Shuirjeh clay minerals in 76 core samples collected from two deep Gonbadli wells. The results of XRF analysis showed high percentages of silicon and moderate to low percentages of aluminum, sulfur, calcium, potassium, sodium, magnesium, and iron in both wells. The XRD analysis indicated that the above elements were concentrated in the form of quartz, anhydrite, dolomite, calcite, plagioclase, K-feldspar, hematite, and clay minerals. Further XRD examination of the clay fraction revealed that illite, chlorite, and kaolinite were the major types of clay minerals. Unlike, glauconite, smectite, and a mixed layer clays of both the illite–smectite and chlorite–smectite types were observed only in very few samples. The percentages of individual clay minerals were determined using external standard calibration curves and successfully validated by a system of simultaneous linear equations acquired from detailed elemental information based on the XRF analysis. The error reached ±5% for the main mineral constituent and ±15% for minor minerals. A local regression relationship was also derived, based on the XRF elemental information, which can be used to estimate the clay contents of other Shurijeh drilled wells with data of pulsed-neutron spectroscopy tools. According to the proposed quantitative approach, the amount of illite varied considerably, reaching 18.3%. In contrast, the amounts of kaolinite and chlorite were generally small, i.e., less than 8.4%. The amount of total clay minerals changed greatly from a minimum of 5% to a maximum of 32.5%. An increase in illite with increasing burial depth and temperature was an obvious indication of deep burial diagenesis in this formation.

Volume of clay is an important component in the assessment of shaly sand reservoirs, due to its significant impact on the production characteristics. The Shurijeh sandstone Formation of Lower Cretaceous age, with subordinate shales is one of the most challenging gas reservoirs to be properly characterized in the eastern Kopet-Dagh sedimentary Basin, Northeastern Iran. This paper describes the improvement achieved in estimating the volume of clay in the Shurijeh reservoir Formation, with an application to a gas producing well and another nonproducing well in a joint field between Iran and Turkmenistan. A clear comparison between estimates from several conventional petrophysical methods and actual laboratory measured data of 76 core samples showed very large estimation errors; therefore an attempt has been made to improve the estimation with developing a multilayer feedforward backpropagation neural network. Six types of well logs were selected, through a sensitivity analysis, as the most relevant input data to the volume of clay (network output). Data were then standardized and randomly divided into three sets of 70% for training, 15% for validation and 15% for testing. Three different training algorithms of genetics, particle swarm optimization, and Levenberg-Marquardt were tested on a network with certain topology, and the latter was within data set from the Shurijeh Formation.

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The characterization of carbonate rocks is not straightforward, as they often experience complex diagenetic processes causing them to expose wide variations in pore types. This research aims to characterize the properties of a carbonate reservoir with a complicated porous structure through rock physics principles and tools. Two representative wells from an oil field located in SW of Iran were selected, and two-dimensional (2D) and three-dimensional (3D) rock physics templates (RPTs) were constructed by employing the appropriate rock physics models. The porosity, water saturation, and pore type are considered reservoir parameters affecting carbonate rock's elastic properties and indicating the reservoir quality. The 2D RPTs described variations in two reservoir parameters in terms of elastic properties. However, they were not able to simultaneously characterize all three reservoir parameters. The proposed 3D RPTs revealed the underlying relationship of elastic properties with pore aspect ratio, water saturation, and porosity. To validate the constructed RPTs, well logging data, scanning electron microscope images, and thin section images were utilized. The RPTs were also employed to predict the reservoir properties quantitatively, and these predictions were compared with the petrophysical data. The average errors of the predicted porosity and water saturation by 3D RPT were, respectively, 1.22% and 6.66% for well A, and 2.65% and 8.18% for well B. The 2D RPTs provided three sets of predictions for porosity and water saturation (considering three specific pore aspect ratios of 0.03, 0.1, and 0.5), all with higher average errors compared to the predictions by 3D RPT for both wells. The obtained results proved that 3D RPT could predict reservoir properties more accurately. Finally, based on the estimated values of pore aspect ratio, water saturation, and porosity using 3D RPTs, the reservoir under study was divided into distinct depth intervals, and a quality level was assigned to each interval. The introduced rock physics-based procedure for a carbonate reservoir characterization could increase the reliability in predicting the reservoir properties, enhance the ability to detect the reservoir fluid, and thereby reduce the interpretation risk.