1. The document analyzes sandstone samples from the Shajara Formation in Saudi Arabia to characterize the reservoirs.
2. Arithmetic relaxation time of induced polarization was calculated from permeability distributions and used to calculate fractal dimensions.
3. Based on the results, the Shajara reservoirs were divided into three units from base to top: Lower, Middle, and Upper Shajara Arithmetic Relaxation Time Fractal Dimension Units. Fractal dimension increased with increasing permeability and relaxation time.
Evaluation of Morphometric Parameters Derived from CartoDEM and Aster GDEM wi...Dr Ramesh Dikpal
From three different sources viz. survey of India topographic map (1:50,000), CartoDEM (10 mts) and Aster GDEM (30 mts) morphometric parameters are derived and evaluated. Manually digitized the drainage network from toposheets and extracted drainage network system from CartoDEM and Aster GDEM using ArcGIS 10.2 software. Basic, derived and shape parameters are considered for basin analysis. The mean bifurcation ratio of the given basin for CartoDEM & Aster GDEM are having nearby values and also indicates some sort of geological control, high stream frequency (Fs) is indicative of high relief and low infiltration capacity of the bedrock pointing towards the increase in stream population with respect to increase in drainage density, low drainage density (Dd) leads to coarse drainage texture, value of Lg for topographic, CartoDEM and Aster GDEM data indicating very fine texture & fine texture respectively. From the shape parameters the Kumudvathi watershed indicates it is sub-circular and elongated. The results from the high resolution data will have the nearby values and less of % variation, whereas in low resolution data % of variation is more and will not meet criteria.
Radial Derivative and Radial Inversion for Interpreting 4D Gravity Anomaly Du...TELKOMNIKA JOURNAL
The 4D gravity or time lapse gravity has been used many reseracher to identify fluid injection in oil reservoir. The objective of this study is to find the better way in interpreting 4D gravity anomaly due to fluid injection around the reservoir. Radial Derivatives are derivative values of gravity anomalies against horizontal distances in the radial direction. Radial inversion is a two-dimensional inversion of lines with radial directions resulting in a 3-dimension model. Time lapse microgravity research have been performed in “X Oil Field” with amount of 604 data point covering area of 4000 m x 5000 m. This Radial derivative and Radial inversion have been aplied at an injection well of the X oil field. The yield show that 4D gravity anomaly value due to injection is 0.02 mGal to 0.36 mGal. Radial derivative value in the area is 0 micro Gal/cm to 0,012 mGal/meter. Radial inversion shows radius of fluid front movement is 304 meters to 1120 meters. Radial derivative and Radial inversion have been proven fairly good to identify injected fluid movement in the reservoir.
Evaluation of Morphometric Parameters Derived from CartoDEM and Aster GDEM wi...Dr Ramesh Dikpal
From three different sources viz. survey of India topographic map (1:50,000), CartoDEM (10 mts) and Aster GDEM (30 mts) morphometric parameters are derived and evaluated. Manually digitized the drainage network from toposheets and extracted drainage network system from CartoDEM and Aster GDEM using ArcGIS 10.2 software. Basic, derived and shape parameters are considered for basin analysis. The mean bifurcation ratio of the given basin for CartoDEM & Aster GDEM are having nearby values and also indicates some sort of geological control, high stream frequency (Fs) is indicative of high relief and low infiltration capacity of the bedrock pointing towards the increase in stream population with respect to increase in drainage density, low drainage density (Dd) leads to coarse drainage texture, value of Lg for topographic, CartoDEM and Aster GDEM data indicating very fine texture & fine texture respectively. From the shape parameters the Kumudvathi watershed indicates it is sub-circular and elongated. The results from the high resolution data will have the nearby values and less of % variation, whereas in low resolution data % of variation is more and will not meet criteria.
Radial Derivative and Radial Inversion for Interpreting 4D Gravity Anomaly Du...TELKOMNIKA JOURNAL
The 4D gravity or time lapse gravity has been used many reseracher to identify fluid injection in oil reservoir. The objective of this study is to find the better way in interpreting 4D gravity anomaly due to fluid injection around the reservoir. Radial Derivatives are derivative values of gravity anomalies against horizontal distances in the radial direction. Radial inversion is a two-dimensional inversion of lines with radial directions resulting in a 3-dimension model. Time lapse microgravity research have been performed in “X Oil Field” with amount of 604 data point covering area of 4000 m x 5000 m. This Radial derivative and Radial inversion have been aplied at an injection well of the X oil field. The yield show that 4D gravity anomaly value due to injection is 0.02 mGal to 0.36 mGal. Radial derivative value in the area is 0 micro Gal/cm to 0,012 mGal/meter. Radial inversion shows radius of fluid front movement is 304 meters to 1120 meters. Radial derivative and Radial inversion have been proven fairly good to identify injected fluid movement in the reservoir.
On the equality of electric power fractal dimension and capillary pressure fr...Khalid Al-Khidir
On the equality of electric power fractal dimension and capillary pressure fractal dimension for characterizing shajara reservoirs of the permo carboniferous shajara formation
Effects of shale volume distribution on the elastic properties of reserviors ...DR. RICHMOND IDEOZU
Shale volume (Vsh) estimation has been carried out on three selected reservoirs (Nan.1, Nan.2, and Nan.4) distributed across four wells (01, 03, 06, and 12) in Nantin Field, using petrophysical analysis and reservoir modeling techniques with a view to understanding the reservoir elastic properties. Materials utilized for this research work include: Well Log data (Gamma Ray Log, Resistivity Log, Sonic Log, Density Log, Neutron porosity log), and a 3-D Seismic volume were used for the study. Sand and shale were the prevalent lithologies in Nantin Field. Nan. 1 reservoir was thickest in Nantin well 12 (29.7ft), Nantin 2 reservoir was thickest in Nantin Well 12 (30.9ft) while Nantin 4 reservoir was thickest in Well 3 (72ft). Correlation well panel across the Field showed that Nantin 4 reservoir, was thicker than Nan 1 and Nan 2 Reservoir respectively. Normal and synthetic Faults were also mapped, the trapping system in the field includes anticlines in association with fault closures. The thicknesses and lateral extents of these reservoirs were delineated into three zones (1, 2, and 3) which were modeled appropriately. Petrophysical and some elasticity parameters such as Poisson ratio (PR), Acoustic Impedance (AI), and Reflectivity Coefficient (RC) were evaluated for the wells. The results from elasticity evaluation showed a high Poisson Ratio of 0.40 in Nantin 2 reservoir of Well 12 based on high shale volume distribution of 0.70 indicating high stress level and possible boundary to hydraulic fracture. The lowest Poisson Ratio was evaluated in Nantin reservoir of Well 1 with lowest shale volume of 0.18 which indicates weak zones and may not constrain a fracturing job. Results from Acoustic impedance showed a high AI value of 7994.3 in Nan 2 Reservoir compared to Nan.1 which has the least AI value of 7447.3 because of low shale volume. A higher Reflectivity Coefficient of 0.01 was recorded in Nan.2 reservoir indicating bright spot while a lower RC of -0.00023 was recorded in Nan.4 Reservoir indicating dim spot. Hydrocarbon volume estimate of the three reservoirs showed 163mmstb in Nan.1 reservoir, 169mmstb, in Nantin 2 reservoir and 115mmstb in Nan. 4 Reservoir. The reservoirs encountered were faulted and laterally extensive. Nantin 2 reservoir was more prolific with a STOIIP of 169 mmstb compared to Nan. 1 with a STOIP of 163 mmstb and Nantin.4 with a STOIP of 115 mmstb, because of its good petrophysical values, facies quality and low shale volume distributions.
Transverse relaxation time fractal dimension of nuclear magnetic resonance fo...Khalid Al-Khidir
Transverse relaxation time fractal dimension of nuclear magnetic resonance for characterizing shajara reservoirs of the permo carboniferous shajara formation, saudi arabia
Characterization of heterogeneity of the shajara reservoirs of the shajara f...Khalid Al-Khidir
Representative sandstone samples were collected from the surface-type section of the Shajara Formation of thePermo-Carboniferous Unayzah Group for reservoir characterization
through fractal dimension investigation. Two models based on mercury intrusion technique were employed to represent the pores characteristics of these sandstones. The results show that realistic dimensions and outstanding fractal features of porous structures in Shajara sandstones, when these materials are correlated by thermodynamic model and 3-D fractal model of mercury intrusion. On the basis of sub-Unayzah unconformity, sub-Middle Shajara local unconformity, mudstone of the Middle Shajara and sub-Khuff unconformity, the three porous and permeable sandstone units of Shajara Formation were treated
separately and classified here into three fractal dimension
units. The units from base to top are: Lower Shajara Fractal
Dimension Unit, Middle Shajara Fractal Dimension Unit,
and Upper Shajara Fractal Dimension Unit. The thermodynamic model and 3-D fractal model were effectively used to characterize the porous structures of Shajara sandstones in logical and quantitative way.
Keywords Heterogeneity . Shajara formation . Shajara
fractal dimension units . Permeability
On Similarity of Differential Capacity and Capillary Pressure FractalKhalid Al-Khidir
On Similarity of Differential Capacity and Capillary Pressure Fractal Dimensions for Characterizing Shajara Reservoirs of the Permo-Carboniferous Shajara Formation, Saudi Arabia
Electric potential gradient fractal dimensions for characterizing shajara res...Khalid Al-Khidir
Electric potential gradient fractal dimensions for characterizing shajara reservoirs of the shajara formation of the permo carboniferous unayzah group, saudi arabia
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
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using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
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• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
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• Remote control system for accessing CCR and allied system over serial or TCP.
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• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
2. 2 | Prof. Khalid Elyas Mohamed Elameen AlKhidir. Nanoscience and Nanotechnology
Results and Discussion
Based on field observation the Shajara Reservoirs of the Shajara Formation of the Permo-Carboniferous Unayzah
Group were divided here into three units as described in Figure1.These units from base to top are: Lower Shajara
Reservoir, Middle Shajara reservoir, and Upper Shajara Reservoir. Their acquired results of the arithmetic relaxation
time fractal dimension of induced polarization are displayed in Table 1. Based on the attained results it was found the
maximum value of the fractal dimension was found to be 2.7872 assigned to sample SJ13 from the Upper Shajara
Reservoir as verified in Table 1.Whereas the minimum value of the fractal dimension was reported from sample SJ3
from the Lower Shajara reservoir as displayed in Table1.The arithmetic relaxation time induced polarization fractal
dimensions were observed to increase with increasing permeability as shown in Table1 owing to the possibility of
having interconnected channels. The Lower Shajara reservoir was denoted by four sandstone samples out of six, label
as SJ1, SJ2, SJ3 and SJ4 as confirmed in (Figure1). Their geometric fractal dimension values are shown in (Table 1) and
their slopes (3-fractal dimension) were displayed in Figure 2, Figure 3, Figure 4, and Figure 5 respectively. As we
proceed from sample SJ2 to SJ3 a pronounced re-duction in permeability due to compaction was reported from 1955
md to 56 md which reflects decrease in arithmetic relaxation time fractal dimension from 2.7748 to 2.4379 as specified
in (Table 1) and an increase in slope from 0.2252 to 0.5621 as shown in Figure 3 and Figure 4. Again decrease in slope
and increase of permeability was recorded as we proceed from sample SJ3 to sample SJ4 whose arithmetic fractal
dimension value was found to be 2.6843 as described in Table 1.
Figure 2; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ1
Figure 3; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ2
3. Nanoscience and Nanotechnology Volume 2 Issue 1 | 2018 | 3
Figure 4; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ3
Figure 5; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ4
4. 4 | Prof. Khalid Elyas Mohamed Elameen AlKhidir. Nanoscience and Nanotechnology
Figure 1; Stratigraphic column of the type section of the Permo-carboniferous Shajara Formation of the Unayzah Group, Wadi
Shajara, Qusayba area, al Qassim district, Saudi Arabia, N 26_52 17.4, E 43_36 18
In contrast, the Middle Shajara reservoir was separated from the Lower Shajara reservoir by an unconformity
surface as shown in (Figure 1). It was designated by three sam-ples out of four, namely SJ7, SJ8, and SJ9 as illustrated
in (Figure1). Their arithmetic relaxation time induced polarization fractal dimensions values were delineated in (Table 1)
and their slopes (3-fractal dimension) were illustrated in Figure 6, Figure 7, and figure 8 respectively .Their fractal
dimensions values are higher than those of samples SJ3 and SJ4 from the Lower Shajara Reservoir due to an increase in
their permeabilities as explained in (Table 1).
5. Nanoscience and Nanotechnology Volume 2 Issue 1 | 2018 | 5
Figure 6; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ7
Figure 7; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ8
Figure 8; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ9
On the other hand, the Upper Shajara reservoir was separated from the underlying Middle Shajara reservoir by
yellow- green mudstone as revealed in (Figure1). It is defined by three sandstone sam-ples so called SJ11, SJ12, SJ13 as
explained in (Table1) and Figure 9, Figure 10, and Figure 11.
6. 6 | Prof. Khalid Elyas Mohamed Elameen AlKhidir. Nanoscience and Nanotechnology
Figure 9; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ11
Figure 10; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ12
Figure 11; Arithmetic relaxation time of induced polarization versus wetting phase saturation of sample SJ13
Moreover, their arithmetic relaxation time fractal dimension values are also higher than those of sample SJ3 and
SJ4 from the Lower Shajara Reservoir due to an increase in their permeabilities as clarified in (Table 1).
7. Nanoscience and Nanotechnology Volume 2 Issue 1 | 2018 | 7
Reservoirs Sample Porosity % Permeability mD Arithmetic relaxation
time fractal dimension
Upper Shajara
Reservoir
SJ13 25 973 2.7872
SJ12 28 1440 2.7859
SJ11 36 1197 2.7586
Middle Shajara
Reservoir
SJ9 31 1394 2.7786
SJ8 32 1344 2.7752
SJ7 35 1472 2.7683
Lower Shajara
Reservoir
SJ4 30 176 2.6843
SJ3 34 56 2.4379
SJ2 35 1955 2.7748
SJ1 25 1680 2.7889
Table1: Petrophysical properties of the three Shajara reservoirs of the Shajara Formation with their Arithmetic relaxation time
fractal dimension values.
Overall a plot of fractal dimension versus permeability (Figure 12) reveals three permeable zones which confirm
the occurrence of the three Shajara reservoirs of the constructed stratigraphic column. The higher permeable zone with
permeability 973 mD and above and fractal dimension 2.7586 and above (Figure 12) belongs to Upper Shajara
reservoir, Middle Shajara Reservoir, and samples SJ1 and SJ2 from the Lower Shajara Reservoir. The Moderate
permeable zone with fractal dimension 2.6843 and permeability 176 mD (Figure 12) corresponds to sample SJ4 from
the Lower Shajara reservoir. The lower permeable zone with fractal dimension 2.4379 and permeability 56 mD (Figure
12) assigns to sample SJ3 from the Lower Shajara reservoir. Such variation in fractal dimension and permeability will
reflect heterogeneity of the Shajara reservoirs.
Figure 12; Arithmetic relaxation time of induced polarization fractal dimension versus permeability showing the three
permeable zones of the Shajara reservoirs of the Shajara formation
4. Conclusion
4.1 The sandstones of the Shajara Reservoirs of the Shajara formation of the permo-Carboniferous Unayzah group
were divided here into three units. Lower Shajara Reservoir, Middle Shajara Reservoir, and Upper Shajara Reservoir.
4.2 The three Shajara reservoirs were confirmed by fractal dimension. These units from base to top are: Lower
Shajara arithmetic Relaxation time Fractal dimension Unit, Middle Shajara Arithmetic Relaxation Time Fractal
Dimension Unit, and Upper Shajara Arithmetic Relaxation time Fractal Dimension Unit.
4.3 The fractal dimension was found to increase with increasing grain size and permeability.
8. 8 | Prof. Khalid Elyas Mohamed Elameen AlKhidir. Nanoscience and Nanotechnology
Acknowledgements
The author would like to thank King Saud University for their supports.
References
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shalyness, SPWLA 7th Annual Logging Symposium 1966; 5:9-11.
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relaxation time spectra. J. Petrol. Sci. Eng 2004; 45: (1-2) 1-10.
3. Liu X, Kong Li, Zhang P, et al. Permeability estimation using relaxation time spectra derived from differential
evolution inversion. J. Geophys. Eng 2014; 11: 1-8.
4. Al-Khidir KE, Al-Laboun A, Al-Qurishi A, et al. Bi¬modal pore size behavior of the Shajara Formation Reservoirs
of the Permo-Carboniferous Unayzah Group, Saudi Arabia. J Petrol Explor Prod Technol 2011; 1(1): 1-9.
https://link.springer.com/article/10.1007/s13202-011-0007-5
5. Al-Khidir KE, Benzagouta M, Al-Qurishi A, et al. Charac¬terization of heterogeneity of the Shajara Reservoirs of
the Shajara Formation of the Permo-Carboniferous Unayzah Group. Arab J Geosci.Arabian 2013; 6(10):
3989-3995. https://link.springer.com/article/10.1007%2Fs12517-012-0656-9
6. Al-Khidir. Nuclear magnetic resonance as diagnostic parameter for reservoir characterization. Archives of
petroleum and Environment biotechnology 2017.
https://gavinpublishers.com/journals/articleprocedureview/Nuclear-magnetic-resonance-relaxation-time-as-a-diagn
ostic-parameter-for-reservoir-characterization