Advanced History Matching Techniques Reviewed
- Richard Wilfred Rwechungura (Norwegian University of Science and Technology) | Mohsen Dadashpour (Norwegian University of Science and Technology) | Jon Kleppe (Norwegian University of Science and Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 25-28 September, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 3.3 Well & Reservoir Surveillance and Monitoring, 5.1.9 Four-Dimensional and Four-Component Seismic, 5.1.6 Near-Well and Vertical Seismic Profiles, 5.6.4 Drillstem/Well Testing, 5.1.5 Geologic Modeling, 1.2.3 Rock properties, 5.6.5 Tracers, 4.1.2 Separation and Treating, 5.5.8 History Matching, 5.6.1 Open hole/cased hole log analysis, 4.3.4 Scale, 5.1.8 Seismic Modelling, 7.6.2 Data Integration, 5.2 Reservoir Fluid Dynamics, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.1 Reservoir Characterisation, 4.1.5 Processing Equipment, 5.1.7 Seismic Processing and Interpretation
- 16 in the last 30 days
- 1,801 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
The process of conditioning the geological or the static model to production data is typically known as history matching (HM). The economic viability of a petroleum recovery project is greatly influenced by the reservoir production performance under the current and future operating conditions. Therefore evaluation of the past and present reservoir performance and forecast of its future are essential in reservoir management process. At this point history matching plays a very important role in model updating and hence optimum forecasting, researchers are looking for new techniques, methods and algorithms to improve it.
This paper therefore reviews HM and its advancements to date including time-lapse seismic data integration. The paper covers manual and automatic HM, minimization algorithms including gradient and non gradient methods. It reviews the advantages and disadvantages of using one method over the other. Gradient methods covered include conjugate gradient, steepest descent, Gauss-Newton and Quasi-Newton. Non-gradient methods covered includes evolutionary strategies, genetic algorithm and Kalman filter (ensemble Kalman filter).It also addresses re-parameterization techniques including principal component analysis (PCA) and discrete cosine transforms (DCT). The methods are evaluated using a data set based on data from the Norne Field in the Norwegian Sea provided by Statoil and its partners to the Center of Integrated in Petroleum Industry (IO Center) at the Norwegian University of Science and Technology (NTNU).
History matching is defined as the act of adjusting a model of a reservoir until it closely reproduces the past behavior of a reservoir; this is a common practice in oil and gas industry in order to have reasonable future predictions. It is traditionally performed by trial and error. Reservoir engineers analyse the difference between simulated and observed value and manually change one or a few parameters at a time in the hope of improving the match. In such an approach, reservoir parameters are updated manually and often in two steps: the pressure match and the saturation match (Mattax and Dalton 1991, Saleri and Toronyi 1988). The quality of this type of history matching largely depends on the engineer's experience and the amount of the budget. Since reservoirs are usually very heterogeneous, there are hundreds of thousands of grid blocks in a typical reservoir simulation model to estimate reservoir parameters in high resolution. Therefore, manual history matching is often not reliable for long periods and is always associated with many uncertainties, and computers are employed to automatically vary the parameters. This procedure is called automatic history matching (semi-automatic HM) and it is an inversion problem. Semi automatic history matching is defined as construction of an initial model with an initial approximation of the reservoir parameters which then goes through a systematic process reduction of an objective function that represents the mismatch between observation and calculated response by perturbing the relevant parameters (Guohua et al. 2004).
|File Size||5 MB||Number of Pages||19|