A fracture identification method using fractal dimension and singular spectrum entropy in array acoustic logging: Insights from the Asmari Formation in the M oilfield, Iraq

Abstract

As the exploration and development of fracture-type hydrocarbon reservoirs progress, accurately identifying reservoir fracture development is crucial for reservoir evaluation. This method is founded on array acoustic logging data, integrating fractal theory and singular spectrum analysis. It computes and evaluates the full-wave signals and dipole shear wave signals to assess their self-similarity and chaotic characteristics in various stratigraphic conditions. A comprehensive fracture identification method suitable for complex formations has been established, with its application to carbonate fracture-type reservoir characterization demonstrated through a case study of the Asmari Formation in the M oilfield, southeastern Iraq, as a case study. This method demonstrates an accuracy of approximately 90 % in identifying both low-to-moderate and high-angle fractures. Compared to traditional mode wave attenuation methods, this approach significantly reduces errors in complex wellbore environments, providing a new technical pathway for exploring and developing fracture-type hydrocarbon reservoirs.