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The detailed micropaleontological analysis carried out on four offshore Oligocene (Tineh Formation) Nile Delta successions revealed the identification of 44 foraminiferal species and subspecies. Nine benthic and two planktonic zones are identified. The established benthic zonation has been chronostratigraphically calibrated with those based on the planktonic foraminiferal ones. This sequence is assigned to the Oligocene (Chattian), and possibly extends to the upper part ofRupelian.Aremarkable feature of thisOligocene succession is the almost complete absence of planktonic foraminifera in its major lower part, a situation that caused a difficulty of subdividing it into smaller units. The environmental interpretation for theOligocene succession implies deposition under a background of persistent subsidence with remarkable paucity or complete absence of any calcareous fauna, probably related to deposition in carbon dioxide-rich marine conditions hostile to the precipitation of any calcareous material and caused by volcanic activity, lowered temperature or situation under the lysocline or carbonate-compensation depth. Source

Armacanqui J.S.,CoO WOGI Incorporated | Hassan A.M.,Belayim Petroleum Company
Society of Petroleum Engineers - SPE North Africa Technical Conference and Exhibition 2015, NATC 2015

The variety of available EOR techniques requires an in-depth screening to select a viable method that matches well to the reservoir rock and fluid parameters and still remains economically attractive. In the present paper a Comprehensive Integrated EOR Workflow is proposed that starts with an Advanced EOR Screening Method. This is comprised of both a Neural Network Part and an Operational Module. While the First part uses proven data mining techniques the Operational Module considers the specific features of the screened EOR Method influencing the field implementation. The Neural Network Part is based on an exhaustive review and selection of successfully deployed literature case. It uses the rock, fluid and other reservoir parameters to screen various EOR methods considering their technical-economical applicability. This Artificial Intelligence approach utilizes data mining techniques in the form of a hybrid system that makes use of a neural network as a screening tool and the genetic algorithm as an optimization tool to land into the optimum recommendation. The Operational Part enables to evaluate the implementation capability on the given field based on the specific requirements of the preselected EOR Method. The system works its way through the literature data of successful EOR projects trying to detect patterns and learning from the data the relationship between these characteristics and the feasibility of applying each EOR technique mimicking the ability of the human mind to learn from previous experience. The system is a multi-layers neural network whereby the input layer is composed of seven key reservoir parameters (depth, temperature, porosity, permeability, initial oil saturation, oil gravity and in-situ oil viscosity) while the output layer is composed of the probability of success of the evaluated EOR methods (steam, CO2 miscible, hydro-carbon miscible, in-situ combustion, polymer flooding). The number of hidden layers and neurons are optimized using genetic algorithm for best matching of the training data set and accurate prediction of the testing set. Comparing the system output with the actual applied EOR techniques in the field shows a reliable result with only a 5% miss-prediction of the total test fields. The Operational Module determines the deployment capabilities in the given reservoir considering the specific parameters of the pre-selected EOR Method, production-pressure history, Formation fluid flow properties and the actual field and well set up, thus providing an advanced EOR Screening. Copyright © 2015 Society of Petroleum Engineers. Source

Helal A.E.-N.,Ain Shams University | Lala A.M.S.,Ain Shams University | Salah Salah Ahmed A.,Belayim Petroleum Company | Talaat Mohamed Mohamed A.,Belayim Petroleum Company
Arabian Journal of Geosciences

The geometry and architecture of the gas chimneys in the Baltim area have been imaged by 3D seismic techniques, time section, and a variety of attribute extractions, providing us with a high resolution definition of these features. The main indicator for gas chimneys in the seismic is an almost cylindrical-shaped chaotic behavior of seismic signal, due to scattering of seismic energy by diffused gas through the cap rocks above the leaked reservoirs. The origin of gas chimneys has to be related to hydraulic fracturing by gas leaking through faults from deep accumulation where overpressure conditions have been generated by fast burial during the Plio-Pleistocene mega sequence deposition. The petroleum system in Post-Messinian (Plio-Pleistocene) succession generated biogenic gas only, whereas the Pre-Messinian system proved to generate thermogenic gas and oil. The role of gas chimney and associated structures as hydrocarbon migration pathways from the pre-Messinian kitchen section to the Post-Messinian reservoirs is testified by many DHI’s within the Pliocene–Pleistocene reservoirs that are in contact with the boundaries of the gas chimneys. © 2015, Saudi Society for Geosciences. Source

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