Real-Time Autoregressive Deep Learning Framework for In-Line Automatic Surface Logging

摘要

Abstract 4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas. From AI inspired well logging data interpretation to real-time reservoir monitoring, technologies have led to cost savings, increase in efficiencies and infrastructure centralization. In this work we provide an overview of how autoregressive deep learning methodologies can lead to major advances in the field of formation evaluation and reservoir characterization, providing a comprehensive overview of the technologies developed and utilized in this domain. Furthermore, we provide a future outlook for smart technologies in formation evaluation, and how these sensor-derived data can be integrated. This also describes the challenges ahead. Future developments will experience a growing penetration of 4IR technology for enhancing formation evaluation in subsurface reservoirs.