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Rapid Model Updating with Right-Time Data - Ensuring Models Remain Evergreen for Improved Reservoir Management

History matching reservoir models to production data has been a challenge for asset teams since the early days of reservoir simulation. Keeping these models evergreen as production data continues to arrive, knowing when a rehistory match is required and being able to re-history match easily and efficiently is also a major challenge which is often not addressed in a timely manner. This need is becoming even more pressing as real-time reservoir performance data is increasingly available. Decisions can now be made with the support of the good quality real-time data from the reservoir usually in the form of pressure data from downhole gauges and rate data from multiphase meters. With the recent integration of existing technologies, a rapid model updating workflow is now possible. The history matching workflow that was once a discrete process can now be a computer assisted continuous process. Using statistically-based assisted historymatching technology in conjunction with real-time data acquisition, data monitoring, and reservoir simulation software, new production data can be quickly assimilated into the reservoir model. Real-time data from the field measurement devices is filtered for consumption by the reservoir modeling software and compared with the forecasts from the reservoir simulator to determine if rehistory matching is required. The new data can be added to the history file and the model (revised if necessary) can be used in operational performance optimization. This rapid model updating workflow can be run semiautomatically on a continuous basis as new production data is gathered, thereby keeping reservoir models evergreen and providing the most up-to-date basis for the making of important reservoir management decisions. Introduction Since the early days of reservoir simulation, history matching has been identified as one of the best methods of validating a reservoir model’s predictive capabilities. Often long periods of time have been spent adjusting the reservoir description so that the reservoir simulator’s calculated results match the observed data from the reservoir. Unfortunately, due to limitations of data availability, computer hardware performance and mantime, history matching has been a discrete process which is performed only at certain stages in a reservoir’s life cycle rather than as a continuous process that updates the reservoir model as new data arrives. Although recent advances in assisted matching technology have shown there is potential to cut the time required to achieve history matches, often the reservoir model is out of date before the history matching process has been completed because the production data used in history matching is frozen in time when the process is started and is not updated over the often many months that history matching can take. With the advent of digital oil field technology, data is now available from the reservoir in real-time. In particular, downhole pressures and multiphase surface flow rates can now be measured directly and continuously. This real-time data is now routinely used for reservoir monitoring, flow assurance calculations, production optimization and for reservoir engineering analysis. However, despite many good intentions, reservoir modeling has been slow to take up the use of real-time production data to condition its models. While there have been some promising projects using Ensemble Kalman Filtering to update reservoir models, these have mostly been research oriented and have not yet made it into mainstream use. The approach taken in this paper is different from those reported previously in that it builds upon assisted history matching technology that is already in the commercial domain and is in widespread use. First, a process framework is established in which a history match of a reservoir model can be obtained. This process, which