A new petrophysical-mathematical approach to estimate RQI and FZI parameters in carbonate reservoirs

• Published in: Journal of Petroleum Exploration and Production Technology
• Main Authors: Farshad Sadeghpour, Kamran Jahangiri, Javad Honarmand
• Format: Article
• Language: English
• Published: SpringerOpen 2025-02-01
• Subjects: Reservoir quality index; Flow zone indicator; Petrophysical well log; Rock characterization; Genetic algorithm
• Online Access: https://doi.org/10.1007/s13202-025-01952-6
• ISSN: 2190-0558; 2190-0566

Abstract One of the major challenges for engineers is to enhance the description techniques of oil and gas reservoirs. Among the most significant parameters for assessing reservoir quality, the reservoir quality index (RQI) and flow zone indicator (FZI) are particularly noteworthy. Their precise determination is crucial for evaluating the reservoir formation quality, its production ability, rock typing, etc. This study used core data (including porosity and permeability) and petrophysical well logs (including RHOB (Density), DT (Sonic or Acoustic), NPHI (Neutron-Porosity), and GR (Gamma Ray)) from 64 wells in one of the Iranian oil fields with carbonate reservoir. This study has used intelligent techniques such as curve fitting, regression, genetic algorithms, and well-log data to develop a new model and estimate RQI and FZI. Then the results were compared with those obtained from two other methods (that use core and well-log data in common empirical relationships) and showed a very good correlation. Based on the results, three grades were determined for the reservoir quality of the studied formation: low quality (RQI less than 0.01 μm and FZI less than 0.03 μm), medium quality (RQI between 0.01 and 0.03 μm and FZI between 0.03 and 0.07 μm), and high quality (RQI greater than 0.03 μm and FZI greater than 0.07 μm). In most areas of this reservoir, RQI and FZI were found to be less than 0.03 μm and 0.07 μm, respectively. These findings indicate medium to low reservoir quality. This study aims to present a new method for estimating RQI using petrophysical well-log data and intelligent techniques. This method applies to all formations and oil fields and eliminates the need for core samples and traditional empirical relationships. The findings of this study can help reduce costs and time related to accessing data, particularly data from core samples. Additionally, it can minimize errors when compared to traditional methods for evaluating reservoir quality and characterization, ultimately leading to better reservoir management.