Lithology Classification and Porosity Estimation of Tight Gas Reservoirs With Well Logs Based on an Equivalent Multi-Component Model

Abstract

Tight gas makes up a significant portion of the natural gas resources. There are tight gas reservoirs with great reserve and economic potential in the west Sichuan Basin, China. Due to the complex mineral component and heterogeneity of the thick tight sand formations, the reservoir parameters are challenging to evaluate from well logs using conventional methods, even the fundamental porosity. The mineral components must be considered. In this study, based on the analysis of different logging responses of varying lithologies, we introduced the complex reservoir analysis (CRA) method. CRA is always used in the carbonate reservoirs to calculate the different rock component volume fractions and can be used to classify the lithology and calculate the porosity simultaneously. By analyzing the component, a new equivalent component method (CRAE) is proposed based on the CRA method in this paper. In this method, the AC-CNL equation-calculated porosity is calibrated according to the core porosity data to set the rock components’ physical parameters. After calibration, the rock component fractions and porosity can be calculated accurately. Also, according to the relationship between the grain size and natural gamma-ray, a granularity median model was established. Six lithology types, including coarse-grained quartz sandstone and coarse-grained lithic sandstone, are distinguished, and the porosity is estimated in the study area. The identification results are compared with the mud logging data and other methods. It shows that this method is very well adequate in the tight sandstone gas reservoirs in the study area.

Residual Convolutional Neural Network for Lithology Classification: A Case Study of an Iranian Gas Field

“…They introduce LSTM-FCN, a hybrid model that outperforms conventional methods, and enhance it with particle swarm optimization. Also, these conventional well reports are used in other fields such as predicting porosity [15], permeability [16,17], and water saturation [18]. Among the methods discussed, utilizing well-logs proves to be a more prac-tical and cost-effective approach in the oil and gas industry compared to relying on seismic data and core images…”