Mohd Azizi Ibrahim
Sr. Petrophysicist Consultant
Saudi Aramco
Participates in
TECHNICAL PROGRAMME | Primary Energy Supply
Advances in Geoscience
Forum 05 | Digital Poster Plaza 1
30
April
10:00
12:00
UTC+3
Mineralogy-based petrophysical evaluation in near real-time has been traditionally utilized for optimal well placement in deep gas clastic reservoirs. The minimum required logs were Spectral Gamma-Ray, Density, Neutron, Resistivity and Sonic. Due to the presence of silt, which reduces permeability, intervals of similar porosity from Multimin (MM) vary significantly in reservoir performance. This paper will demonstrate a new workflow that requires less logging tools to provide a robust petrophysical evaluation.
A Shaly-Sand Analysis (SSA) model for the area is developed using Gamma-Ray, Density, Neutron and Resistivity only. The petrophysical results such as porosity and water saturation were calibrated to the MM petrophysical analysis. The SSA petrophysical results are also correlated to the Formation Tester (FT) pressure test analysis to verify the permeability zones. The lithology from SSA is comparable to Mudlogs lithology interpretation. The established model will be used across the silty clastic reservoirs for a specific area.
The correlation between porosity, lithology, and FT results shows that the most significant factor that reduces permeability is the volume of silt in the tested layers. The SSA allows the quantification of silt for each layer which results in improving the FT operation by identifying the best zone to test and reducing the time of screening. The SSA total porosity and water saturation values matched the reference values computed using Multimin (MM) approach. Total porosity and water saturation from MM were used as reference since the models were core calibrated for consistency of the basic petrophysical parameters across the studied field. In addition, the calibrated SSA enables the identification of the sweet spot while drilling the horizontal wells by avoiding highly silty clean zones, which can indicate good total porosity.
The proposed workflow identifies sweet spots in horizontal sandstone wells by utilizing minimum logs that LWD companies can provide as inputs to the SSA. The silt volume quantification takes into consideration further factors to characterize permeability than relying only on the porosity since it can be misleading. This approach reduces overall logging costs and potentially improves well productivity. In addition, it leverages the power of big data into creating performance calibrated models that support well placement.
Co-author/s:
Mohammed F. Alzayer, Lead Petroleum Engineer, Saudi Aramco.
Layal N. Alhussain, Petroleum Engineer, Saudi Aramco.
Ali E. Alqunais, Lead Petroleum Engineer, Saudi Aramco.
A Shaly-Sand Analysis (SSA) model for the area is developed using Gamma-Ray, Density, Neutron and Resistivity only. The petrophysical results such as porosity and water saturation were calibrated to the MM petrophysical analysis. The SSA petrophysical results are also correlated to the Formation Tester (FT) pressure test analysis to verify the permeability zones. The lithology from SSA is comparable to Mudlogs lithology interpretation. The established model will be used across the silty clastic reservoirs for a specific area.
The correlation between porosity, lithology, and FT results shows that the most significant factor that reduces permeability is the volume of silt in the tested layers. The SSA allows the quantification of silt for each layer which results in improving the FT operation by identifying the best zone to test and reducing the time of screening. The SSA total porosity and water saturation values matched the reference values computed using Multimin (MM) approach. Total porosity and water saturation from MM were used as reference since the models were core calibrated for consistency of the basic petrophysical parameters across the studied field. In addition, the calibrated SSA enables the identification of the sweet spot while drilling the horizontal wells by avoiding highly silty clean zones, which can indicate good total porosity.
The proposed workflow identifies sweet spots in horizontal sandstone wells by utilizing minimum logs that LWD companies can provide as inputs to the SSA. The silt volume quantification takes into consideration further factors to characterize permeability than relying only on the porosity since it can be misleading. This approach reduces overall logging costs and potentially improves well productivity. In addition, it leverages the power of big data into creating performance calibrated models that support well placement.
Co-author/s:
Mohammed F. Alzayer, Lead Petroleum Engineer, Saudi Aramco.
Layal N. Alhussain, Petroleum Engineer, Saudi Aramco.
Ali E. Alqunais, Lead Petroleum Engineer, Saudi Aramco.
