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This study presents the results of a deep seismic survey across the north Algerian margin, based on the combination of 2-D multichannel and wide-angle seismic data simultaneously recorded by 41 ocean bottom seismometers deployed along a north-south line extending 180 km off Jijel into the Algerian offshore basin, and 25 land stations deployed along a 100-km-long line, cutting through the Lesser Kabylia and the Tellian thrust-belt. The final model obtained using forward modelling of the wide-angle data and pre-stack depth migration of the seismic reflection data provides an unprecedented view of the sedimentary and crustal structure of the margin. The sedimentary layers in the Algerian basin are 3.75 km thick to the north and up to 4.5-5 km thick at the foot of the margin. They are characterized by seismic velocities from 1.9 to 3.8 km s -1 . Messinian salt formations are about 1 km thick in the study area, and are modelled and imaged using a velocity between 3.7 and 3.8 km s -1 . The crust in the deep sea basin is about 4.5 km thick and of oceanic origin, presenting two distinct layers with a high gradient upper crust (4.7-6.1 km s -1 ) and a low gradient lower crust (6.2-7.1 km s -1 ). The upper-mantle velocity is constrained to 7.9 km s -1 . The ocean-continent transition zone is very narrow between 15 and 20 km wide. The continental crust reaches 25 km thickness as imaged from the most landward station and thins to 5 km over a less than 70 km distance. The continental crust presents steep and asymmetric upper-and lower-crustal geometry, possibly due to either asymmetric rifting of the margin, an underplated body, or flow of lower crustal material towards the ocean basin. Present-time deformation, as imaged from three additional seismic profiles, is characterized by an interplay of gravity-driven mobile-salt creep and active thrusting at the foot of the tectonically inverted Algerian margin.

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A new multidisciplinary workflow is suggested to re-characterize the Hamra Quartzite (QH) formation using artificial neural networks. This approach involves core description, routine core analysis, special core analysis and raw logs of fourteen wells. An efficient electrofacies clustering neural network technology based on a self-organizing map is performed. The inputs in the model computation are: neutron porosity, gamma ray and bulk density logs. According to the self-organizing map results, the reservoir is composed of five electrofacies (EF1 to EF5): EF1, EF2 and EF3 with good reservoir quality, EF4 with moderate quality, and EF5 with bad quality. Hydraulic flow units are determined from well logs and core data using the flow zone indicator (FZI) approach and the multilayer perception (MLP) method. Obtained results indicate eight optimal hydraulic flow units. Hydraulic flow units for un-cored well are determined using the MLP, the used inputs to train the neural system are: neutron porosity, gamma ray, bulk density and predefined electrofacies. A dynamic rock typing is achieved using the FZI approach and combining special core data analysis to better characterize the hydraulic reservoir behavior. A best-fit relationship between water saturation and J-function is established and a good saturation match is obtained between capillary pressure and interpreted log results.

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