Fluid classification through well logging is conducted using the extreme gradient boosting model based on the adaptive piecewise flatness-based fast transform feature extraction algorithm

Abstract

In recent years, fluid prediction through well logging has assumed a pivotal role in the realm of oil and gas exploration. Seeking to enhance prediction accuracy, this paper introduces an adaptive piecewise flatness-based fast transform (APFFT) algorithm in conjunction with the XGBoost (extreme gradient boosting) method for logging fluid prediction. Initially, the APFFT technology is employed to extract frequency-domain features from the logging data. This algorithm dynamically determines the optimal frequency interval, transforming raw logging curves into frequency domain data. This adaptive process enhances the preservation of frequency domain information reflective of fluid characteristics, simultaneously minimizing the impact of noise and non-fluid compositions. Subsequently, the acquired frequency domain features are utilized as inputs to construct an XGBoost model for fluid prediction. To validate the efficacy of this proposed approach, real logging data were collected, and an extensive experimental evaluation was conducted. The experimental findings underscore the substantial advantages of the APFFT-XGBoost method over traditional machine learning models such as XGBoost, random forest, K-nearest neighbor algorithm, support vector machine, and backpropagation neural network in logging fluid prediction. The proposed method demonstrates the ability to accurately capture fluid features, leading to improved prediction accuracy and stability.