"Reservoir Characterization: Integrating Geological and Core Data"

What is it about?

This study focuses on accurately characterizing the Asmari reservoir, which is complex to access due to structural challenges. Traditional methods like well tests often lack precision in describing fracture properties, especially in dual porosity systems. The paper aims to develop a precise structural model for the Asmari reservoir by interpreting dip data from FMI (Formation MicroImager) logs for permeability analysis, characterizing fractures through image log interpretation, and computing index permeability. The study relies on advanced image logging and interpretation techniques to identify geological features and perform petrophysical analysis, offering advantages over traditional core data for reservoir characterization. The methodology presented here leverages borehole electrical images to predict index permeability, showing promise for heterogeneous carbonate reservoirs. The results are compared and calibrated against permeability derived from MDT (Modular Formation Dynamics Tester) and core samples. It concludes that using this workflow yields permeability values that closely resemble the signatures of formation permeability and core measurements in such heterogeneous carbonate reservoirs. The paper highlights the limitations of core sampling, often failing to represent highly fractured zones and being expensive and unidirectional. It also emphasizes the usefulness of logging tools like high-resolution micro resistivity (OBMI) and acoustic geological imaging (UBI) for studying subsurface fractures, showcasing their reliability through matching with core and other logging data. The integration of FMI data with other logs is shown to be effective in deriving permeability curves, particularly in basement formations, providing valuable insights for reservoir evaluation.

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Photo by Ngar Amini on Unsplash

Photo by Ngar Amini on Unsplash

Why is it important?

This study holds significance for overcoming challenges in characterizing the Asmari reservoir, known for its structural complexities. Traditional methods like well test analysis often fall short in accurately describing fracture properties. Evaluating permeability in dual porosity systems using Formation MicroImager (FMI) data, although valuable, wasn't utilized by the oil company due to the lack of formation testing data. The objectives of this study are to develop a precise structural model for the Asmari reservoir by interpreting dip and fracture characteristics from FMI and image logs, respectively, for permeability analysis. The research leverages recent advancements in image logging and interpretation techniques to reliably identify geological features and perform petrophysical analysis, offering advantages over traditional core data for reservoir characterization. The study demonstrates the distinct advantage of image logging in predicting index permeability using orientation and dip data from borehole electrical images, particularly in heterogeneous carbonate reservoirs. It compares and calibrates FMI results with permeability derived from Modular Formation Dynamics Tester (MDT) and core measurements, revealing closer alignment with formation permeability signatures. It highlights limitations of core sampling, often failing to represent highly fractured zones and being expensive and unidirectional. In contrast, logging instruments like high-resolution micro resistivity (OBMI) and acoustic geological imaging (UBI) are shown to be more useful in studying subsurface fractures. The integration of FMI data with other open-hole logs demonstrates the feasibility of deriving permeability curves, particularly in basement formations. Ultimately, this study proves valuable in showcasing the reliability of logging instruments in characterizing reservoirs, providing a viable alternative or complement to core analysis, and demonstrating the applicability of this method in evaluating basement permeability.

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