Publications

Many sensors and devices in the field are available for constant monitoring at the reservoir manager's desk, but the industry finds itself faced with the challenge of transforming all this raw streaming data into useful information. At short periods, data are used to identify anomalies and provide alarms in the classical Field Automation sense. In the medium to long term, data should also be used to identify well and reservoir signatures for formation evaluation, reservoir management and planning through the use of flow simulation and automated history matching. However, data are rarely managed effectively in short timeframes, and hardly ever at the medium and long term, largely because the analysis involved simply cannot be automated by computer. Skilled engineers to deliver this analysis are becoming ever busier and in short supply, making it vital to streamline these activities and deliver this analysis as a turnkey service to the Operator. On a pre-arranged schedule, data should be inspected and any potential transients of interest analyzed. Similarly, updates should be made to a ",living", reservoir simulation, material balance and streamtube model using automated historymatching. Updates to the completion, reservoir drainage, reserves and production forecast picture are made via web reports to the Operator. This process is automatic, consistent, and reliable, requiring no effort or wasted time from the Field Reservoir or Production Engineer. Rather, it allows them to review results carefully and to focus on specific important items. The delivery of this key reservoir knowledge, given the current staff structure of the industry, is impractical without a third-party service approach. Novel processes described in this paper include: (a) Data transmission from the field of relevant analysis-grade data, (b) Interfaces to software and specialized reporting displays for information screening, (c) Systematic analysis workflows, including specialized petroleum software packages, (d) Web reporting mechanisms for Asset Management.