Browsing by Author "Cherana, Amina"

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    Analyse de l'anisotropie acoustique et évaluation géomecanique d'un réservoir tight par l'outil Sonic Scanner : application sur le champ d'Adrar
    (Université M’Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2017) Cherana, Amina; Aliouane, Leila (Promoteur)
    Le but de ce travail est la caractérisation des réservoirs non-conventionnels tight par les diagraphies conventionnelles complétées par les diagraphies de technologies avancées. Il s'agit de la technologie sonique dipôle, à savoir le Sonic Scanner, en analysant le comportement des ondes de compression et celui des ondes de cisaillement rapide et lent pour estimer l’anisotropie acoustique aux abords du puits. Cette anisotropie peut être engendrée par plusieurs effets, tels que les fractures naturelles ou induites, ainsi que les laminations d’argiles. L’interprétation des données Sonic Scanner permet d’identifier ces effets.
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    Fuzzy clustering algorithm for Lithofacies classification of Ordovician unconventional Tight-sand reservoir from well-logs data (Algerian Sahara)
    (2019) Cherana, Amina; Aliouane, Leila; Doghmane, Mohamed Zinelabidine; Ouadfeul, Sid-Ali
    In this paper we present an approach based on a fuzzy clustering algorithm applied for lithofacies classification in an unconventional tight-sand reservoir from well-logs data. In some cases, these kinds of reservoirs are ra-dioactive due to the presence of non clayey radioactive minerals. However, conventional methods can give bad results. For that, artificial intelligence such as Fuzzy logic, can be suitable to solve the problem. Fuzzy clustering is an unsupervised machine learning technique where a given set of data is classified into groups. Hence, fuzzy logic is a more general logic then classical logic because it does not ignore uncertainties and accepts the implicit consideration of the inherited error associated with any physical measurement. This techniquet has been applied to real data of one well in an unconventional tight-sand reservoir in the Algerian Sahara. Predicted results are compared to lithofacies obtained from conventional methods and spectral mineralogical well-logs data Keywords: Well-log, Unconventional Tight reservoir, Lithofacies, Fuzzy logic, Clustering
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    Fuzzy machine learning contribution in reservoir characterization from well-logging data
    (Universite M'Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2024) Cherana, Amina; Aliouane, Leila(Directeur de thèse)
    This thesis presents a comprehensive exploration of the integration of Neuro-Fuzzy Systems (NFS) within the domain of reservoir characterisation, with a specific focus on the analysis of petrophysical data in both conventional and unconventional reservoirs, notably within the Algerian Sahara region. Leveraging recent advancements in machine learning, neural networks, and fuzzy logic, this research elucidates the pivotal role of NFS as hybrid machine learning systems in augmenting reservoir characterisation methodologies. Drawing upon two peer-reviewed publications, this thesis embarks on an elaborate work to contextualize the latest developments in NFS within the broader domain of machine learning applications in reservoir characterisation. In the first foundational chapter, we delineate the fundamental principles underpinning machine learning, fuzzy logic, and the amalgamation thereof in the form of Neuro-Fuzzy Systems. Through a rigorous exposition, the theoretical underpinnings and operational mechanisms of these paradigms are elucidated, laying the groundwork for subsequent chapters. A meticulous examination of contemporary machine learning applications in reservoir characterisation forms the essence of chapter two. By synthesising existing literature, we distinguish prevalent methodologies, challenges, and advancements in employing machine learning techniques for reservoir characterisation tasks, thereby providing a comprehensive overview of the current status. Building upon the theoretical framework established in preceding chapters, Chapter 3 explores the application of unsupervised fuzzy logic methods for lithology classification. Through empirical investigations, the efficacy of fuzzy logic algorithms in delineating lithological boundaries is assessed, contributing to enhanced reservoir characterisation workflows. Chapter four undertakes the task of predicting porosity and permeability in a conventional reservoir situated within the Algerian Sahara region. Leveraging machine learning techniques, predictive models are developed to accurately estimate these critical reservoir properties, thereby facilitating informed decision-making in petroleum exploration and production endeavours. In the concluding chapter, the research findings are synthesized, and key insights gleaned from the empirical investigations are elucidated. Moreover, recommendations for future research endeavours aimed at further enhancing the efficacy and applicability of automated methods in predicting hydrocarbon reservoir properties are delineated, underscoring the imperative for continued interdisciplinary collaboration and innovation in the field of reservoir characterisation
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    Prediction model of reservoir porosity via incorporating Particle Swarm Optimisation into an Adaptive Neuro-Fuzzy Inference System; application to Triassic reservoirs of the Hassi R’mel field (Algeria)
    (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, 2024) Cherana, Amina; Aliouane, Lynda
    Conventional methods for estimating porosity from core data are often limited by their spatial coverage, time-consuming nature, high cost, and inability to capture the entire underground reservoir. To address these challenges, this paper proposes a soft computing method using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate porosity in a conventional gas reservoir. The approach involves integrating well-logging data and the ANFIS model with a Particle Swarm Optimisation (PSO) training algorithm to predict the underground porosity model in the Hassi R’mel region of the Algerian Sahara. The choice of this hybrid method was based on its superior performance compared to other models. Although the Hassi R’mel reservoirs are of Triassic clay sandstones, originated by the fluviatile depositional environment that lay on top of the Hercynian surface, the characterisation of their properties still requires refinement to improve the reservoir performance and address the problems faced using appropriate technologies. With an average porosity of 15% and permeability ranging from 250 to 650 mD, the ANFIS method shows excellent accuracy compared to core data, and a reliability of 85%. Overall, the ANFIS-PSO hybrid model proves to be a dependable and efficient technique for porosity prediction, surpassing traditional methods.
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    Unconventional tight-sand reservoir characterization by geomechanical study from well logs data in illizi basin
    (2021) Cherana, Amina; Aliouane, Leila; Keci, Naima; Ouadfeul, Sid-Ali
    Unconventional Tight sand reservoirs require stimulation methods in order to make them produce; hydraulic fracturing is one of these methods. A geomechanical study is therefore necessary and its main objective is to determine an area favourable to hydraulic fracturing in order to optimize production. For this, the results of the petrophysical evaluation are exploited and the various dynamic and static geomechanical parameters (Young's modulus and the Poisson's ratio) are subsequently calculated. Finally, the pore pressure was also calculated in order to obtain the Effective Minimum Stress which helps determine the area favourable to fracturing. An application has been carried out realized exploiting well-logs of Ordovician Tight-sand reservoir of one well from the Illizi Basin