Series-2 (Jan – Feb 2020)Jan – Feb 2020 Issue Statistics
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Abstract: The Miocene rocks are carefully studied and sampled from Wadi El Deir section (Lat. 28˚ 51΄ 16˝ N and Long. 32˚ 35΄ 20˝ E) which exposed of the Southern Galala Plateau in the Eastern Desert. Lithostratigraphically, the Miocene sequence could be differentiated into two main rock units arranged from the oldest to youngest; Hommath, and Sarbut El Gamal formations. In terms of benthonic foraminifera zonation, the study area is yielded I benthonic foraminiferal zone; Amphistegina vulgaris – Textularia gramen – Amphimorphina haueriana Assemblage zone (Lower Miocene), Stilostomella spp. – Bulimina spp. – Mesolenticulina spp. Assemblage Zone and Textularia nussdorfensis – Textularia mariae Assemblage Zone (Middle Miocene).
Keywords: Miocene, benthonic foraminifera, biostratigraphy, Eastern Desert-Egypt
[1]. Abd El-Naby, A., Ghanem, H., Boukhary, M., Abd El-Aal, M., Lüning, S., & Kuss, J. (2010): Sequence-stratigraphic interpretation of structurally controlled deposition: Middle Miocene Kareem Formation, southwestern Gulf of Suez, Egypt. - GeoArabia, 15: 129-150.
[2]. Abd El-Razek, A.A. (1991): Nannoplanktonic and planktonic foraminiferal zonation of the lower Miocene sequence in Gabal Hadahid, South west Sinai, Egypt. - Egyptian Journal of Geology, 35 (1-2): 275-284.
[3]. Abdallah, M.A., Abd El-Hady, F.M., (1966): Geology of Sadat area, Gulf of Suez. Journal of Geology. United Arab Repub, 10 (1):1–24.
[4]. Andreae, A. (1884): Ein Beitrag zur Kenntniss des Elsässer Tertiärs. Abhandlungen zur geologischen Specialkarte von Elsass-Lothringen, Strassburg, 2(3):1-331.
[5]. Barbat, W. F., and von Estorff, F. E. (1933): Lower Miocene Foraminifera from the southern San Joaquin Valley, California: Jour. Paleontology, v. 7, no. 2, p. 164-174, pi. 23.
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Abstract: In mitigating the challenge in oil and gas industries caused by inaccurate estimation of reservoir porosity from well log measurement due to heterogeneity nature of reservoir rocks, Artificial Neural Network (ANN) was employed for the accurate prediction of reservoir porosity. The understanding that the volume of oil, gas, and water in a reservoir depends directly on its porosity underscore the importance of having accurate porosityprediction. In this study, well logs (sonic, resistivity and density) which are known to affect the porosity within the reservoir of interest were selected as input variables of the supervised network while core porosity data of Well 1 was set as its target and trained using Levernberg-Marquadt algorithm of ANN at epoch 10. The input data were randomly divided.....
KEY WORDS: Heterogeneity, Algorithm, Predicted porosity, Cost effectiveness, Accuracy
[1]. Oyeneyin, B. (2015). Introduction to the hydrocarbon composite production system. Elsevier, Amsterdam. pp25.
[2]. Martin, F. David and Robert M. Colplts P. G. (1966). Reservoir Engineering. Standard Handbook of Petroleum and Natural Gas Engineering. Gulf Publishing Company Houston, Texas. Volume 2, pp 35.
[3]. Arabani, M. S. and Bidhendi, M. (2002). Porosity Prediction from Wireline Logs using Artificial Neural Networks: A case study in North-East of Iran. Iranian International Journal Sci., pp. 3, 221-233.
[4]. Lim, J. and Kim, J. (2004). Reservoir Porosity and Permeability Estimation from Well Logs using Fuzzy Logic and Neural Network. Society of Petroleum Engineers, Spe-88476-pp. 9-12
[5]. Hyne, N. (2014). Dictionary of Petroleum Exploration, Drilling and Production (Tulsa: Penwell Corporation) pp 394-395.
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Abstract: Bahariya oasis is a great syncline in the Egyptian Western Desert. Because of the overpopulation in Bahariya oasis in the past 40 years, it is important to extract as much groundwater as possible to expand the agricultural lands and fulfill people's needs of water and crops. Being far away from the River Nile, it is very essential to investigate the groundwater existence and its origin on it. The present study utilizes vertical electrical soundings (VES'es) and time domain electromagnetic soundings (TDEM) to study the investigated area. The main objective of this study is to give a detailed description of the main subsurface conditions of groundwater existence, hydrochemistry, potentiality, and its depth and quality. Geophysical analysis and interpretation of the obtained findings expose that the subsurface is composed of five geoelectrical layers with a clear general slope from southwest towards the northeast. The fourth and the fifth layers are considered the two water bearing formations; whereas the......
Key Word: Vertical electrical Sounding (VES), Time Domain Electromagnetic Sounding (TDEM), hydrochemistry, Bahariya Oasis area, Egypt..
[1]. Plyusnina EE, Sallam ES, Ruban DA (2016) Geological heritage of the Bahariya and Farafra oases, the central Western Desert of Egypt, Journal of African Earth Sciences, January 2016, doi: /j.jafrearsci, vol. 4, p. 151-159.
[2]. Abd El-Gawad EA, Mousa DA, Lotfy MA, El-Shorbagy AI (2017) Origin of Bahariya oil in Salam oil field, Western Desert- Egypt, Egyptian Journal of Petroleum, https://doi.org/10.1016/j.ejpe.2017.09.003.
[3]. El-Akkad S and Issawi B (1963) Geology and iron ore deposits of the Bahariya Oasis, Geological Survey and Mineral Research Department, Egypt, Paper 18p. 301.
[4]. Mohamed I Abdel-Fattah, John D Pigott, Zakaria M Abd-Allah (2017) Integrative 1D-2D Basin Modeling of the Cretaceous BeniSuef basin, Western Desert, Egypt., Research article, Journal of Petroleum Science and Engineering, Volume 153, May 2017,P. 297-313.
[5]. Mohamed Yousif, Hassan S Sabet, Saad Y Ghoubachi, Ameer Aziz (2018) Utilizing the geological data and remote sensing applications for investigation of groundwater occurrences, West El Minia, Western Desert of Egypt, NRIAG Journal of Astronomy and Geophysics, Volume 7, Issue 2, December 2018, P. 318-333.
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Abstract: The memories of the past earthquakeincidences in Nigeria has recently warranted the warnings from research agencies and the recently predicted2028 large earthquake from researchers in Nigeria. Thus the major forecast from researchers has appeared to be open ended. To this end, this research aimed at the prevention of probabilistic earthquake incidence in Mokwa, Nigeria and the mitigation of its hazards using geophysical method. The Findings of this research established that Mokwa is at the risk of experiencing earthquake. Thisis due to the delineated low resistive subsurface features, overcrowded surface structures and ground punctures in relation to their proximities to the existing rail line.Immediate relocation in addition to Nigerian Government and inclined agenciesto pay rapt attention to predicted earthquakes in Mokwa and Nigeria at large.
Keywords: earthquake; geophysical; probabilistic; ground-punctures; low resistive; subsurface features
[1]. Anthony E, Akpan A, Abidemi OI, & Nse UEGeophysical investigation of Obot Ekpo Landslide site, Cross River State, Nigeria. Journal of African Earth Sciences. 2015; 109:154 – 167.
[2]. Awoyera P, Ogundeji P and Aderonmu P Simulated Combined Earthquake and DeadLoad Lateral Resistance Building Systems using Nigeria Seismic Data Journal of Material Environmental Science. 2016;7:781-789.
[3]. Gaudio CD, Ricci P, Verderame GM, and Manfredi G Urban-scale seismic fragility assessment of RC buildings subjected to L'Aquila earthquake Journal of Soil Dynamics and Earthquake Engineering. 2017;:49-63.
[4]. Kadiri UA, Yakubu TA, Akpan OU, Duncan D and Usifor ES Towards an integrated seismic hazard monitoring in Nigeria using geophysical and geodetic techniques International Journal of the Physical Sciences. 2011; 6(28):6385-6393.
[5]. Available online at http://www.academicjournals.org/IJPS. DOI: 10.5897/IJPS10.375.
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Abstract: Lithology and fluid discrimination are the two foremost objectives in any seismic reservoir characterization project. In an attempt to enhance the understanding of seismic responses and thus the capacity to predict of fluids and lithologies, a modified form of the Zoeppritz equation was derived using relations between elastic constants and velocities. This modified equation was used to generate Extended Elastic Impedance (EEI) logs and volumes. Sensitivity analyses of the absolute Acoustic Impedance and the derived Extended Elastic Impedance (EEI) at zero angle were performed and the results from both of log correlation and crossplot analyses show that at zero incidence angle these attributes exhibit similar response in characterization of the reservoir but....
[1]. Galvin.R.J., Calculation of correct compressional wave amplitudes for simple three-dimensional earth models in seismic exploration using computer algebra: MSc Thesis, Curtin University of Technology. 2001
[2]. Zoeppritz, K., Erdbebenwellen VIIIB, on the reflection and propagation of seismic waves: Göttinger Nachrichten, 1919; I, 66-84.
[3]. Whitcombe, D., Connolly, P., Reagan, R and Redshaw, T. Extended Elastic Impedance for Fluid and Lithology Prediction, Geophysics, 2002;67(1): 63-67.
[4]. Connolly, P , Calibration and inversion of non-zero offset seismic: 68th SEG Annual Meeting, New Orleans, USA, Expanded Abstracts, 1998; 182-184.
[5]. Connolly, P., , Elastic Impedance: The Leading Edge, 1999;438-452.
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Abstract: In the past two decades, the application of seismic velocities to pore pressure estimation has received a great deal of attention within the oil and gas industry. This increasing dependence on seismic velocities forpore pressure prediction is well understood given the shift in hydrocarbon exploration focus to frontier areas, especially the deepwater where well data is rarely available for measured pressure information for well planning. In this study, we present pitfalls in pore pressure prediction results in onshore Niger Delta due to the application of seismic interval velocity to the velocity-pressure transform using the Bowers' model. Results obtained show that stacking velocities and its corresponding seismic interval velocity derived through Dix conversion produce low resolution velocity.
Keywords: Pore Pressure, stacking velocities, seismic interval velocities, overburden model, Niger Delta
[1]. Bowers, G. L. (2001). Determining an appropriate pore pressure estimation strategy, Offshore Technology Conference, Paper OTC 13042.
[2]. Bowers, G. L. (2002). Detecting high Overpressure. The Leading Edge, 174-177.[3]. Craganu, C. (2007). Using artificial neural networks to predict the presence of overpressured zones in the Anadarko basin, Okhlahoma. Pure and Applied Geophysics, 64, 2067-2081.
[4]. Dix, C. H. (1955). Seismic velocities from surface measurements. Geophysics, 20, 68-66.
[5]. Doust, H. and Omatsola, E. (1990). Niger Delta. In Edwards, J. D. and Santogross, P. A., Divergent / Passive margin Basins. AAPG Memoir. American Association of Petroleum Geologists, Tulsa, OK, 48, 239-248.