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    Study of Pore Level Influences on Reservoir Quality Based on Rock Typing: Case Study of Quartzite El Hamra, Algeria
    (Springer Nature, 2024) Nettari, Ferhat; Doghmane, Mohamed Zinelabidine; Aliouane, Leila; Ouadfeul, Sid-Ali
    One of the biggest challenges facing Geoscientists and reservoir modelers is how to improve the descriptive understanding of the hydrocarbons reservoir, and therefore, define the best representative reservoir properties (e.g., fluid flow capacity) in the simulation model, whereas poorly described reservoir characteristics can lead to a significant impact on reservoir performance predictions and its future production behaviors. In order to master the Quartzite El Hamra reservoir in Southern part of Hassi Messaoud field in Algeria, this study was dedicated to characterize the petrophysical properties by using rock typing and flow unit techniques (Winland R35 and FZI). The main objective was to evaluate the pore level’s influences on reservoir quality and log response and to study the relationships between the composition of pore geometry and reservoir quality. This allowed us to understand the factors that control the quality of the reservoir and the fluids’ flow characteristics. Moreover, this study was based on detailed description and laboratory tests on cores and thin sections and the integration of this information with geological, Petrophysical, and engineering data. Furthermore, appropriate set of reservoir properties (i.e., porosity—permeability ratio, R35, storage percentage, and percent flow) are well defined for six identified Hydraulic Flow Units (HFUs). The obtained results can improve reservoir simulation studies for performance prediction, history matching, and future development decisions in the field.
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    Classification of ordovician tight reservoir facies in Algeria by using Neuro-Fuzzy algorithm
    (Springer, 2022) Doghmane, Mohamed Zinelabidine; Ouadfeul, Sid-Ali; Benaissa, Z.; Eladj, S.
    The Tight reservoirs in Algeria are generally characterized by their complex nature and their degree of heterogeneity. Wherein, the quantitative evaluation of such type of reservoirs necessitate the determination of facies in order to estimate the in-situ hydrocarbons and their nature. However, the classical methods of determining facies are essentially based on core data and carrots, which are not always technically available. Artificial neural network (ANN) is one of the recent developed methods being used to provide facies classification with a minimum available core data and by using well logs. Even though, the ANN results are acceptable, it determines only the dominant facies at each depth point off logs, no information can be provided for the secondary facies. For that reason, the main objective of this study is to develop a Neuro-fuzzy algorithm that allows the determination of secondary facies in addition to dominant facies. Indeed, the algorithm has been trained by using core data at wells’ scale in the Ordovician reservoir located in an Algerian southern Petroleum field. Moreover, the Neuro-fuzzy classifier has been tested in near wells, for which, the obtained results has demonstrated the effectiveness of the proposed approach to improve tight reservoir characterization in the studied field. Hence, the designed algorithm is highly recommended for other petroleum systems in Middle East and North Africa region