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Abstract The aim of the study is to condition stochastic generated realizations on well test data in order to improve simulation of facies and petrophysics in fluvial reservoirs. First we have used the pressure data to estimate the shortest distance from the well to a possible channel boundary and thereby simulate the channel structures. The well test also provides the permeability average in the part of the channel intersected by the well. Together with core/log data and general knowledge of the reservoir this have been used to simulate permeability. These permeability realizations is input to a numerical flow simulator and compared with experimental results of the well test. Introduction Lack of relevant data is often a hindrance to proper reservoir management, particularly for offshore reservoirs at an early stage. Therefore, it is important to use all the available data to their full extent. There is still a considerable uncertainty that should be quantified. By using a stochastic approach it is possible to include various types of data and to quantify the uncertainty. It is important to have an efficient algorithm for generating different stochastic realizations. The algorithm should be compatible with other software programs, which are used in reservoir evaluation. In this paper it is demonstrated how information from transient pressure well test may be used in an existing commercial software package, and how this improves the reservoir description and history matching and thereby reduces uncertainty in the results. Stochastic modelling principles have become increasingly popular and many Companies base their reservoir management on results from stochastic models, Several techniques are currently available. The focus is still on heterogeneity modelling, i.e. generating one or a few realizations which satisfies a geological interpretation and a set of specified data. There is, however, a growing use of stochastic models also in history matching and quantification of uncertainty both of volumes and production, Quantification of uncertainty requires a quantification of the geological and geophysical interpretation, specification of the distributions of the most important parameters in the stochastic model, and many realizations of the stochastic model. Typically, the following data are used: well observations, spatial distributions of facies and petrophysics, and seismic horizons. There has been an increased use of seismic data for both facies and petrophysical modelling. Most stochastic models may easily include seismic data. The crucial point is the correlation between the seismic and petrophysical variables. The correlation is probably significant in many reservoirs, but is difficult to estimate. In addition there are some technical challenges related to the difference in scale between the seismic and petrophysical data. This paper reports our experience in including the use of well test data in a stochastic reservoir model. Our aim is to use all available data in the reservoir modelling. Within the Norwegian petroleum research community there are similar projects focusing on seismic data, production data, well logs etc. The same software tools are used in the different projects such that it is possible to use all the information in the same project. P. 653 |
