| S t a n
f o r d U n i v e r s i t y P e t r o l e u m R e s e a r c h I
n s t i t u t e SUPRI-D Overview |
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| R e s e a r c h C o n s o r t i u m o n I n n o v a t i o n i n W e l l T e s t i n g | ||
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Well Testing for Heterogeneous Formations |
Background The research has looked at many different aspects of automated well test interpretation (see later for a complete list). In addition, as a result of the greater insights awarded by the computerization of the interpretation, several other useful results were discovered (for example, a method of measuring in-situ relative permeability from a well test has been developed). Thirty technical papers and several reports have made these findings available to the industry, and many of the techniques and ideas have been incorporated into oil company and service company methodology and software. Innovative well test interpretation techniques that can make use of the new measurements and new computer capability now available have already been shown to provide more results, more reliable results and less expensive tests. We aim to explore new ways to improve further on these successes, and to investigate novel approaches. Research Objectives TOP Installing the algorithms on a wellsite or remotely connected computer makes it possible (with surface recording electronic gauges) to perform the analysis in real time while the test is actually in progress. In this way tools need be left in the hole only as long as is necessary to meet the design objectives for the test. On the other hand, if unexpected results appear the test could be extended to achieve a resolution of the problem and a subsequent retesting would be unnecessary. Based on the algorithms we and others have developed, this goal has already been achieved. However, the techniques do require the presence of a reservoir engineer to monitor the test, either at the wellsite or at the remote computer. This joint manual/automated approach is likely to be standard procedure for some years, however we have directed our research towards a future goal in which the computer can perform the analysis by itself. We have explored different approaches to this goal involving artificial intelligence. In addition, we would like to formulate standard methods by which reservoir models in algorithmic form could be made as widely accessible as the standard log-log type curve. The two inch square cycle log-log type curve has become a convenient form by which authors have made reservoir models available to be used by others. However, as graphical methods gradually give way to computerized analysis, the industry needs to formulate a standard format of subroutine or table look-up so that reservoir models can easily be transferred from one automated computer program to another. This could be a great advantage to the industry and would save the need for recoding or the reliance on commercial software packages. A third area of interest is the development of methodology (other than automated analysis) to use newly available measurement technology. In this area, we developed techniques for the estimation of in- situ gas-oil relative permeabilities and water-oil relative permeabilities. Recent Activities TOP The following sections outline four typical research areas that are of current interest. These four typical areas are described as an indication of our current state of thinking within the overall research area. Computer Aided Interpretation TOP One of the primary difficulties in including the flow rate data arises from the problem of identifying the reservoir model, since traditional methods of model recognition are based on characteristics of the constant rate derivative type curves. In order to extract a recognizable constant rate response, it is necessary to use deconvolution. We have conducted several investigations into effective methods of deconvolution, including both Laplace space and real space techniques. A new way of presenting the diagnostic plot was developed in SUPRI-D by Bourgeois and Horne (1991), who proposed the use of the Laplace pressure, , and its derivative as a means of displaying conventional plots in Laplace space. The advantage of the Laplace space presentation is that deconvolution is straightforward and numerically stable. An example of a Laplace pressure type curve is shown in Fig. 1. Bourgeois also showed that there is significant advantage in using the Laplace pressure during nonlinear regression. Another major focus has been the quantificaiton of uncertainty in the estimates of reservoir parameters. Starting with Rosa and Horne (1983), SUPRI-D has made frequent use of confidence intervals as a way of discriminating between valid and invalid interpretations. As an example of the way in which confidence intervals illustrate this uncertainty, consider the two interpretations shown in Figs. 2 and 3, which result in very different estimates of permeability and skin, even though the match to the data is good in both cases. The reason for the uncertainty is that this particular example has a strong correlation between k and s, as shown in Fig. 4 which illustrates the surface of the residual function (sum of sqaures of differences between the measured and calculated pressures). There is a wide range of combinations of k and s that result in similarly small values of the residual, everywhere at the bottom of the "valley" in the surface. In 1992 we started a new project that looks more closely at confidence intervals and the way that they may be used most effectively. Well Testing for Heterogeneous Formations TOP Sato (1992) considered another aspect of the reservoir characterization problem, by developing the methodology to generate highly accurate solutions for pressure transients in a heterogeneous medium. The technique used the boundary integral method (BEM) as a way of avoiding the numerical dispersion inherent in finite difference solutions. Fig. 5 illlustrates an example of a heterogeneous permeability distribution and Figs. 6 and 7 show the pressure transients and flow lines calculated for it by Sato (1992). Optimization of Wellbore Production SystemsTOP Measurement of Flow RateTOP
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