Journal of Sedimentary Research · 1984 · 163 citations · 0 references
EngineeringEarth SciencesGeophysicsPetroleum GeologyPetrologyReservoir CharacterizationImage AnalysisPetrographyPore Structure AnalysisPorosity AnalysisPorosity EvaluationReservoir GeologyGeophysical Data AnalysisPetrographic AnalysisPetrophysical CharacterizationPore System CharacterizationReservoir QualityReservoir Quality CharacterizationReservoir Quality EvaluationSurface Area
Petrographic Image Analysis was developed to link reservoir petrology—pore geometry and mineral surface areas—to geophysical and petrophysical data, addressing the need to relate petrology to well‑log and seismic interpretation. PIA uses computer‑based image analysis with pattern‑recognition software that acquires, digitizes, segments, and analyzes images to generate pore‑size and roughness spectra, which are then classified or used to estimate physical parameters. PIA improves reservoir quality assessment and enhances interpretation of well logs and seismic data by estimating pore surface area per unit volume and providing detailed pore‑size and roughness spectra.
ABSTRACT There exists a need to relate the petrology of reservoirs (pore geometry, surface areas of mineral phases and pores) to geophysical and petrophysical data. The end result is improved assessment of reservoir quality as well as better interpretation of well logs and seismic data. Petrographic Image Analysis (PIA) was developed from the beginning to interface with petrophysical/geophysical data. PIA relies upon computer-based image analysis using pattern recognition/classification programs, and so information can be obtained very rapidly--the rate simply tied to sophistication of the computer in use. PIA consists of a critical mix of hardware and software which perform four separate functions: 1) image acquisition; 2) image digitization; 3) image segmentation; and 4) image analysis. A sp cial effort has been made to characterize the geometry of the pore complex. Separate spectra related to pore size and pore roughness are generated from each image. In addition, surface area per unit volume of pore can be estimated. Pore spectra can be decomposed and classified using pattern recognition/ classification algorithms or used directly to estimate physical parameters.