Lithology Identification Of Glutenite Reservoirs Based On The Static Image Logging Data

Deep glutenite reservoirs are an important target for petroleum exploration in China. Due to glutenite reservoir which has complex lithology, lithology facies changed rapidly, and the resistivity of rock matrix has a great impact on the electrical logs , it is very difficult to lithology identification in Deep glutenite reservoirs with conventional well logs.Fullbore Formation MicroImage is promoted by Schlumberger, it has very high resolution is up to 5mm, named as"formation microscope". The static images generated by FMI data can compared with cores, and are more intuitive and continuous.,and have been widely used to identify lithology, fractures, and pores,and have great advantages in the study of sedimentary environment and formation structure.ANN (Artificial Neural Networks) is drawing widespread attention in recent years, combined biology, mathematical models and computer algorithms to simulate the brain so as to deal with the problem. It is used to solve data processing , and has achieved great success. Artificial neural network has a strong fault-tolerant, adaptive and associative memory, so it is feasible for lithology identification and prediction.This paper focuses on identifying glutinite lithology. First, we study the static Image logging data, calibrate borehole image logs to cores, and summarize the features of glutenite rock; Second, we utilize image analysis algorithms to perform quantitative analysis and feature selection to borehole images, and transfer image features to characteristic value; Last, we investigate the network structure and study three models of ANN—BP Network, Hopfield Network and SOM network, especially study BP network and mainly utilize the BP neural network to identify lithology. This paper realizes the BP network to identify well lithology, and achieves satisfactory results using MATLAB code.