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Article Type

Review Article

Corresponding Author

Alaa S. Al-Rikaby

Abstract

Reservoir flow zonation is essential in evaluating heterogeneity reservoirs, constructing static/dynamic models, and reserve calculations in any reservoir characteristic. Reservoir flow zonation is accomplished through techniques and methods based on core analyses, well logging or both, and these techniques and methods deal with statistical operations and mathematical equations. The flow zonation definition starts with assigning depositional environment and diagenetic processes of rock types by thin sections of petrographic study taken from cutting and core plugs. In the second step, electrofacies are determined based on well-log classification response using the clustering algorithm method. The well-logging used in this step included Porosity logs (neutron porosity (NPHI), sonic log (DT), and bulk density (RHOB)), deep resistivity (RT), nuclear magnetic resonance (NMR) and gamma-ray logs. The third step is due to determining the flow zone and the analysis of pores size distributions. The flow zone indicator value (FZI) is calculated from routine core data (RCAL) analyses and depends on the hydraulic flow unit concept. The linking process between the above steps with seismic attributes gives certainty prediction to reservoir behavior and a more reliable model. This study reviews many approaches (deterministic and stochastic) for determining flow zonation and for different reservoir types, including conventional and unconventional and also propose the best approach for each by presenting the advantages and disadvantages, especially in the methods that use statistical operations as a non-deterministic method. Permeability estimation for uncored wells is also included in the study's agenda through a simplified presentation of the techniques and methods used in this scope. This study demonstrated that the K-mean approach is the most dependable and consistent method for defining reservoir electrofacies compared to the other techniques investigated of machine learning, despite some drawbacks in running this approach. In addition, Hierarchical Clustering has the problem of not being suited for bigger datasets. Moreover, it only yields the greatest outcomes in certain instances. Automated reservoir zonation has been introduced as a novel approach by previously mentioned researchers using clustering algorithm methods like (Elbow and PELT).

Keywords

Flow Zonation, Hydraulic Flow Units (HFU), Flow Zone Index (FZI), Carbonate Reservoirs, Sand Reservoir, Tight gas reservoir.

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