Quantitative pore‐type characterization from well logs based on the seismic petrophysics in a carbonate reservoir. (18th June 2020)

• Record Type: Journal Article
• Authors: Mirkamali, Maryam Sadat; Javaherian, Abdolrahim; Hassani, Hossein; Saberi, Mohammad Reza; Hosseini, Seyed Abolfazl
• Is Part Of: Geophysical prospecting. Volume 68:Number 7(2020)
• Page Start: 2195
• Page End: 2216
• Publication Date: 2020-06-18
• Subjects: Carbonate -- Pore type -- Seismic petrophysics -- Well‐to‐seismic tie -- Xu and Payne rock physics modelling; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1111/1365-2478.12989
• Languages: English
• ISSNs: 0016-8025

Abstract

ABSTRACT: Quality, availability and consistency of the measured and interpreted well log data are essential in the seismic reservoir characterization methods, and seismic petrophysics is the recommended workflow to achieve data consistency between logs and seismic domains. This paper uses seismic petrophysics workflow to improve well logs and pore geometry interpretations for an oil carbonate reservoir in the Fahliyan Formation in the southwest of Iran. The petrophysical interpreted well logs, rock physics and well‐to‐seismic tie analysis are integrated into the proposed workflow. Our implementation incorporates revising petrophysical well log interpretations and updating pore geometry characteristics to obtain a better well‐tie quality. We first propose an improved pore‐type characterization approach based on both P‐ and S‐wave velocities for quantifying pore geometry. Then, seismic logs are estimated accordingly, and the results are used in the well‐to‐seismic analysis. The quality of the well‐tie is improved, furthermore, by iterating on the petrophysical interpreted well logs as well as the calculated pore geometries. For the intervals with high‐quality data, our workflow improves the consistency between the results of measured and modelled seismic logs. For the intervals with problematic well logs, the application of our proposed workflow results in the successful replacement of the poor data and subsequently leads to an improved wavelet estimation and well‐tie results. In both cases, a higher quantification of pore geometries is achieved, which in turn is confirmed by the core images and formation micro‐imager analysis.