Collaborative-driven reservoir formation pressure prediction using GAN-ML models and well logging data

Abstract

Formation pressure is the foundation and prerequisite for achieving efficient drilling and risk control in complex geological formations. However, intelligent prediction of formation pressure faces challenges such as insufficient sample sizes, poor prediction performance, and low prediction accuracy, which severely restrict the realization of safe and efficient drilling. To address this, this paper proposes a new method based on Generative Adversarial Networks (GAN) and Machine Learning (ML), aiming to improve adaptation to complex geological conditions and effectively capture the nonlinear relationship between logging data and formation pressure for intelligent prediction of formation pressure. The research results show that the GAN technology successfully amplified the 125 cable-measured formation pressure data by 204.8 times. Compared to machine learning predictions without using GAN technology, the constructed GAN-ML model reduced the average root mean square error by 0.59 MPa and the average absolute error by 0.71 MPa in the training set. The GAN-ML model predicted the formation pressure of adjacent wells with an average root mean square error of 1.59 MPa and an average absolute error of 1.96 MPa compared to the cable-measured formation pressure values. This achievement significantly enhances the robustness and generalization performance of intelligent models, demonstrating the potential of GAN-ML in addressing small sample data problems. It opens a new technical path for data-driven intelligent prediction and provides innovative data analysis and prediction methods for the field of geological exploration.