From Real-Time Data to Production Optimization

Abstract

Summary A new way of reservoir management is dawning on the horizon: intelligent-reservoir management using 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 thing—managing a reservoir in real time or close to real time. Real time usually means reacting to an event as it happens or within a short time lag. In the petroleum industry, however, real time is different. This short time lag can be hours, days, or even weeks, which highly depends, of course, on the objective of a project. Integrating real-time data into a reservoir-management workflow, and turning the data into value, is a complicated task. The bottleneck for the data flow, right now, is the transfer of the realtime data, measured with time increments by the second and/or minute and stored on a real-time server, to the engineers' desktops in a clean, timely, and useful fashion. This paper shows ways to provide a continuous (i.e., 24-hours-a-day/7-days-a-week) flow of clean data to the engineers' desktops as a first step in the intelligent-reservoir management. It shows that the implementation of a smart field rises or falls with its ability to provide the data to the information specialist—the petroleum engineer. Because the data are coming into the database very frequently (e.g., at times, every hour or every other day), the engineer is not able to check these data for discrepancies. Therefore, intelligent-reservoir management needs an alarm system that informs the engineers of any underperformance or critical condition of a well or a reservoir. Another important aspect to the intelligent-reservoir management system is the integration of the standard petroleum engineering tools [e.g., decline-curve analysis, material balance, inflow performance relationship (IPR) curves, and reservoir simulation] into this work process. Currently, an IPR curve not only gets data every other month but every other day. This gives the engineer completely new opportunities for closer observation of the workflow (e.g., monitoring the permeability impairment over time). Well tests are usually a snapshot in time, but with continuous surveillance of the reservoir parameters, the development of the skin, for example, can be followed over time and preventative actions can be taken (i.e., predictive maintenance). Neural networks and genetic algorithms are other powerful tools in the real-time environment for handling large amounts of data. A neural network learns from the data gathered and detects underlying relationships—the more data, the better the network can detect these relationships. Once the underlying relationships have been established, the neural networks can be used for predictions (predictive data mining) such as predicting sand production. This approach gives the engineer time to react and prevents the equipment from getting damaged. This work provides a straightforward way of integrating realtime data into a reservoir-management process, and its methodology implies how to gain value from the information provided by a continuous data stream.