Understanding well production performance in hydrocarbon reservoirs in a timely manner is essential for closed loop reservoir management, improving operational efficiency, and maximizing value. It is desirable to have a robust and scalable method for estimating well health, which can be applied in a practical and automated manner. However, traditional surveillance methods are often interpretive and do not scale for manual surveillance of either large fields or those with large data volumes.
In this talk, we will cover a new generation of reservoir modeling tools that use physics-constrained, data driven methods that can be built using routinely collected field measurements for real-world reservoir surveillance and production optimization. The field case studies will illustrate the business value through several examples such as estimating well productivity, real-time production rates, pressure depletion, short-term forecasts and flood optimization.