A Dynamic Data-Driven Inversion Based Method for Multi-Layer Flow and Formation Properties Estimation

摘要

Real-time monitoring of downhole temperature and pressure in injection and production wells equipped with permanent downhole gauges as well as distributed temperature and acoustic sensors plays an important role in production optimization, improved hydrocarbon recovery and daily well operations management decisions. To fully take advantage of the real-time data, there is currently a pervasive need in intelligent oilfield application areas for near real-time, high-fidelity, dynamic data-driven inversion methodologies combined with fast forward flow models comprising partial differential equations. Such methods facilitate dynamic data-driven interpretation of temperature and pressure measurements that can be used to provide continuous downhole production performance characteristics and forecasting, and are key to optimizing and controlling intelligent well production systems under closed-loop conditions. However, a central challenge is the construction of robust computational algorithms for near real-time solutions of the underlying data-driven inverse problem which is often more challenging to obtain than the corresponding forward simulation model. This paper presents the development and testing of a robust computational algorithm for the estimation of multi-layer reservoir flow in addition to static and dynamic formation properties.