SPE 112246 Rapid Model Updating with Right-Time Data - Ensuring Models Remain Evergreen for Improved Reservoir Management Stephen J. Webb, David E. Revus, Angela M. Myhre, Roxar, Nigel H. Goodwin, K. Neil B. Dunlop, John R. Heritage, Energy Scitech Ltd. Copyright 2008, Society of Petroleum Engineers evergreen and providing the most up-to-date basis for the This paper was prepared for presentation at the 2008 SPE Intelligent Energy Conference making of important reservoir management decisions. and Exhibition held in Amsterdam, The Netherlands, 25–27 February 2008. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper Introduction have not been reviewed by the Society of Petroleum Engineers and are subject to Since the early days of reservoir simulation, history correction by the author(s). The material does not necessarily reflect any position of the 1 Society of Petroleum Engineers, its officers, or members. Electronic reproduction, matching has been identified as one of the best methods distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an of validating a reservoir model’s predictive capabilities. abstract of not more than 300 words; illustrations may not be copied. The abstract must Often long periods of time have been spent adjusting the contain conspicuous acknowledgment of SPE copyright. reservoir description so that the reservoir simulator’s calculated results match the observed data from the reservoir. Unfortunately, due to limitations of data Abstract availability, computer hardware performance and History matching reservoir models to production data has mantime, history matching has been a discrete process been a challenge for asset teams since the early days of which is performed only at certain stages in a reservoir’s reservoir simulation. Keeping these models evergreen as life cycle rather than as a continuous process that updates production data continues to arrive, knowing when a re- the reservoir model as new data arrives. Although recent history match is required and being able to re-history advances in assisted matching technology2 have shown match easily and efficiently is also a major challenge there is potential to cut the time required to achieve which is often not addressed in a timely manner. This history matches, often the reservoir model is out of date need is becoming even more pressing as real-time before the history matching process has been completed reservoir performance data is increasingly available. because the production data used in history matching is Decisions can now be made with the support of the good frozen in time when the process is started and is not quality real-time data from the reservoir usually in the updated over the often many months that history matching form of pressure data from downhole gauges and rate data can take. from multiphase meters. With the advent of digital oil field technology3,4,5, data With the recent integration of existing technologies, a is now available from the reservoir in real-time. In rapid model updating workflow is now possible. The particular, downhole pressures6 and multiphase surface history matching workflow that was once a discrete flow rates7 can now be measured directly and process can now be a computer assisted continuous continuously. This real-time data is now routinely used process. Using statistically-based assisted history- for reservoir monitoring, flow assurance calculations, matching technology in conjunction with real-time data production optimization8,9,10 and for reservoir engineering acquisition, data monitoring, and reservoir simulation analysis11. However, despite many good intentions12,13, software, new production data can be quickly assimilated reservoir modeling has been slow to take up the use of into the reservoir model. Real-time data from the field real-time production data to condition its models. While measurement devices is filtered for consumption by the there have been some promising projects using Ensemble reservoir modeling software and compared with the Kalman Filtering to update reservoir models14,15, these forecasts from the reservoir simulator to determine if re- have mostly been research oriented and have not yet made history matching is required. The new data can be added it into mainstream use. to the history file and the model (revised if necessary) can be used in operational performance optimization. The approach taken in this paper is different from those reported previously in that it builds upon assisted This rapid model updating workflow can be run semi- history matching technology that is already in the automatically on a continuous basis as new production commercial domain and is in widespread use. First, a data is gathered, thereby keeping reservoir models process framework is established in which a history match of a reservoir model can be obtained. This process, which 2 SPE 112246 will be summarized in this paper and is described in detail and Bayesian response surface elsewhere2,16, allows the engineer to chose parameters in modeling2,16,17,18,19,20,21,22,23,24,25,26,27,28. the reservoir model that the assisted history matching software can sensitize so that it can explore which The technology used in this work is the advanced parameters need to be changed and how they need to be linear Bayesian tool, EnABLE™. It can be used with a changed to achieve a history match. The process also number of different reservoir simulators and is designed produces an estimate of the uncertainty in the history to assist in the process of identifying multiple acceptable match. While it is obtaining history matches, the process history matches by using a structured workflow process can be used to forecast reservoir performance and define which allows many parameters to be automatically confidence intervals in the forecast performance. These modified simultaneously. Advanced experimental design confidence intervals can then be used as a measure of the methods and linear Bayesian statistical routines are used forecast’s accuracy as new reservoir performance data which permit the use of objective data, subjective opinion arrives. If the new data lies within the confidence and other indirect information in specifying a prior intervals, the model is forecasting field performance to distribution29. A statistical estimator based on a response within the uncertainty of the modeling system. If the new surface model (the Estimator) is created as a proxy for the data lies outside the confidence intervals, either the field full reservoir simulation model and is updated after each is not being operated as forecast (and the simulator’s simulation run. By using the proxy to approximate the operating constraints need updating) or the model itself simulator, extensive exploration of the simulated requires updating. The process framework provides an responses across the solution space may be performed environment where this can be done in an almost without the computational expense of making a complete automatic fashion. simulator run in each evaluation. Starting with a set of scoping runs designed to broadly explore the entire This process will be illustrated by an example based solution space followed by additional “most informative on a large mature North Sea reservoir where the rapid runs” (whereby the regions of the parametric space where model updating process is illustrated by repeatedly the proxy model has the greatest uncertainty are explored) assimilating new production data into the history a reasonably predictive estimator is built. A number of matching process, thereby keeping the model “evergreen” history match solutions are then obtained more rapidly and as up to date as possible for field operating and than would be possible by simply running a large number development decisions. of simulation cases. Fit for purpose history matching and confidence interval estimation using the workflow Rapid Model Updating Process Components described here typically is accomplished with the use of a The rapid model updating process is comprised of the number of reservoir model runs of the order of 100 to 300 runs. Methods that do not employ an estimator are following components: 30 variously reported to require a thousand or more runs . • Statistically-based assisted history-matching The workflow process is shown in Figure 1: technology 1. Identify study objectives. • Robust full field black oil and compositional 2. Setup simulation model as usual. reservoir flow simulator 3. Identify parameters (modifiers) for investigation in the history match (and predictions and optimization31, if • Real-time field monitoring and production anticipated) and their ranges of data management system investigation. 4. Make modifications to the simulator input These technology components are directly interfaced with data to sensitize these parameters. each other to provide a comprehensive and integrated 5. Generate a set of ‘scoping runs’. These total system which is described in the subsequent sections runs, made using an experimental design of this paper. procedure, provide the initial basis for the Estimator model. Typically 25 runs are used Assisted History Matching for this step, although less may be sufficient The availability of powerful and inexpensive computing for small numbers of modifiers.