Hydrocarbon production from unconventional reservoirs has become more common in the past decade, and there are increasing demands to understand and analyse the geochemical, petrophysical, and geomechanical properties of these resources. The recent developments in intelligent techniques, such as Machine Leaning techniques, Fuzzy Logic, and optimization algorithms, provided the scientists from different disciplines with a robust tool to analyse their data. These methods, similarly, can be used in the upstream industry to enhance the efficiency of field development. In this study, the Nene Marine field, as a high potential candidate for future developments in West Africa, will be analysed to find its best productive zones (sweet spots). Tight sandstones of Djeno formation, which are interbedded with shale layers, are the main productive zones in this field. Initially, the routine methods of reservoir evaluation will be applied to classify the reservoir and detect the sweet spot zones. For this purpose, various reservoir properties, including petrophysical, geochemical, and geomechanical parameters, will be calculated from the available data. To validate the results, the identified sweet spots will be compared with the available mobility data from Djeno formation. Afterward, a 3D model of the reservoir productive zones will be constructed. Development of tight sand reservoirs, in addition to a comprehensive study on reservoir's properties, demands for a precise stimulation plan, in order to make the project economical. Therefore, a comprehensive Hydraulic Fracturing analysis for the Nene field will be proposed. To complement this, advanced artificial intelligent techniques, including supervised and unsupervised ML techniques (Fuzzy C-Means (FCM), Hierarchical clustering, K-Means, Logistic regression, K-Nearest Neighbors (KNN), Random Forest, and Boosting), along with Deep Learning (DL) methods will be employed to identify the sweet spots. In addition, a novel method (Quick Analyser) of reservoir classification, which works based on the cut-off values or optimum values, will be presented. Application of this method will be considered for the Nene field and will be demonstrated for another field from Oman. After examination of the accuracy of the proposed methods for sweet spot identification, all of the methods will be compared and the best method will be determined. Another application of intelligent techniques in this study is the estimation of shear velocity wave (an important parameter for geomechanical analysis), which is performed by using an integration of fuzzy inference systems and optimization algorithms. Finally, the application of ML techniques (unsupervised learning) in reservoir classification, by using seismic SEGY data, for a North American oilfield will be illustrated. The results of this study show the huge potential of advanced AI techniques in petroleum industry.