A new way of reservoir management is dawning at the horizon – intelligent reservoir management utilizing continuous data from intelligent wells and/or smart fields. Even though there are many different buzz words for this new technology, they all lead to the same – managing a reservoir in REAL TIME or close to REAL TIME. Real time usually means to react to an event as it happens or within a short time lag. In the petroleum industry real time is for sure different. This "short time lag" can be hours, days or even weeks, which of course depends highly on the objective itself.
Integrating real-time data into a reservoir management work flow and turn the data into value is a complex task. The bottle neck for the data flow right now is the transfer of the real time data - measured with a secondly and minutely time increment and stored on real time server – to the engineers’ desktops in a clean and timely useful fashion.
This paper will show ways how to provide a continuous (24/7) flow of clean data to the engineers’ desktop as a first step for the intelligent reservoir management. It will be shown that the implementation of a smart field rises or falls with the ability to provide the data to the knowledge worker – the petroleum engineer. Since the data is coming into the database, let’s say every hour or every other day, the engineer is not able to check this data for discrepancies. Therefore, intelligent reservoir management needs an alarm system to inform the engineers about any under performing or critical condition of a well or the reservoir itself.
Another important aspect is the integration of the standard petroleum engineering tools, like Decline Curve Analysis, Material Balance, IPR curves, Reservoir Simulation, etc., into this work process. Now an Inflow Performance Relationship Curve does not only get data every other month, but every other day. This gives the engineer completely new opportunities, e.g. monitoring the permeability impairment over time. Well tests are usually a snapshot in time, but with a continuous surveillance of the reservoir parameters, the development of, e.g., the skin can be followed over time and actions can be taken in time – predictive maintenance.
Neural Networks and Genetic Algorithms are other powerful tools in the real time environment, handling such a large amount of data. A Neural Network learns on the gathered data and detects their underlying relationships – the more data, the better. Afterwards, the Neural Networks can be used for predictions (predictive data mining) – for instance predicting sand production. This approach gives the engineer time to react, and prevents the equipment from harm.
This work and the methodology it implies, provides a straight forward way of integrating real time data into a reservoir management process and how to gain value from the information provided by a continuous data stream.