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Results of the comprehensive interpretation of well logs in carbonate and siliciclastic rocks – similarities and differences in the case studies of selected formations

Waszkiewicz Sebastian, Alvarez Ronal Barcala, Jarzyna Jadwiga

Geology, Geophysics and Environment

2019

Vol. 45, no. 3

163--182

This paper was made using geological and well logging data from the Cuban oilfield area and the Polish Carpathian Foredeep gas deposit to compare the interpretation process and underline similarities and differences between data analysis from two reservoir rocks of different lithology. Data from conventional hydrocarbon deposits, i.e. the Mesozoic Cuban carbonate formation and Miocene shaly-sandy sediments were processed and interpreted using Techlog (Schlumberger Co.) software. Selected approaches were used to determine the step by step volume of shale, total and effective porosity, water/hydrocarbon saturation (Quanti) and for the comprehensive interpretation of well logs (Quanti Elan). Brief characteristics of the carbonate and siliciclastic formations were presented to indicate that the interpretation methodology oriented to the determination of petrophysical properties depends strongly on the type of reservoir. Cross-plots were presented for primary mineral composition recognition, determination of m exponent and resistivity of formation water in the Archie equation. Effective intervals for the carbonate reservoir were calculated according to the Cumulative Hydrocarbon Column methodology. Finally, the results of the interpretation of well logs were presented as continuous curves of mineral composition, including shaliness, porosity and hydrocarbon saturation. The conclusions included recommendations for the effective comprehensive interpretation of well logs in the carbonate and siliciclastic reservoirs.

Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland

Waszkiewicz Sebastian, Krakowska-Madejska Paulina, Puskarczyk Edyta

Acta Geophysica

2019

Vol. 67, no. 6

1885--1894

Permeability is a property of rocks which refers to the ability of fluids to flow through each substance. It depends on several factors as pore shape and diameter. Also the presence and type of clay has a large influence on the permeability value. Permeability can be measured on rock sample in the laboratory by injecting fluid through the rock under known condition, but this provides only point information. Due to the dependence of the parameter on many factors, the deterministic estimation of permeability based on laboratory measurement and well logs is problematic. Many empirical methods for determining permeability are available in the literature and interpretation systems. An interesting approach to the problem is the use of artificial neural networks based on laboratory measurement and modern, high-resolution logging tools. The authors decided to use MLP artificial neural networks, which allow permeability estimation and can be used both in the test well and applied to neighbouring wells. The network was checked in several variants. Obtained results show the legitimacy of using artificial neural networks in the issue of estimating permeability. However, they also show limitations resulting from the lack of accurate data or influence of geological setting and processes.