TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractPublished analyses of well tests in gas-condensate reservoirs when pressure drops below the dew point are usually based on a two-zone radial composite model, representing regions of condensate drop-out around the wellbore and of initial gas composition away from the well. Laboratory experiments, on the other hand, suggest that three different mobility zones could exist: (1) an outer zone away from the well, with the initial liquid condensate saturation; (2) a zone nearer to the well, with increased condensate saturation and lower gas mobility; and (2) a zone in the immediate vicinity of the well with high capillary number which increases the gas relative permeability, resulting in a recovery of much of the gas mobility lost from condensate blockage. This paper investigates the existence of this latter zone in well test data. An example of well test analysis is discussed, which illustrates the difficulty of identifying such a zone as, in many cases, build-up and/or drawdown data are dominated by wellbore phase redistribution effects. Where the three zones can be identified, data are analyzed using a three-zone radial composite model to yield a complete characterization of the near-wellbore effects, and in particular the knowledge of the various components of the total skin effect: mechanical skin; rate-dependent two-phase skin; and skin due to gas condensate blockage. The existence of the three zones and the results of the analysis are verified with a compositional simulator where relative permeability depends on capillary number.

Well testing in exploration and appraisal wells has become increasingly unpopular. Reasons include costs, safety and environmental impact. Well testing has also become rare in production wells because of the potential revenue loss during build-ups. Whether suitable alternatives can be found for sampling and reservoir parameter estimation is the subject of regular debate. Alternatives are wireline formation tests in exploration and appraisal, and continuous recording with permanent pressure gauge in production wells. The quality of pressure and rate transients measured during wireline formation tests has improved greatly in recent years. The transients obey the same laws of physics as those measured during a well test and can theoretically be interpreted in the same way. The scale of the measurements, however, is very different. The challenge is to understand what the wireline formation test interpretation results mean and how they can be upscaled to the information provided by a well test. The paper discusses these key issues with examples that illustrate the quality of the data and the analysis process. The interpretation methods are essentially the same as those used in well test analysis with the addition of the formation rate analysis plot, which is particularly useful in high permeability formations where other methods are limited by pressure gauge resolution. Introduction Well testing is often used in the exploration and development of hydrocarbon reservoirs to:Obtain representative formation fluid samples;Measure initial reservoir pressure;Demonstrate and/or establish well productivity;Determine permeability thickness product, kh, and skin, S;Identify the drainage area of the well and any boundary effects that may exist within;Identify and quantify depletion. These objectives can be compared with those of a wireline formation test:Determine formation pressures at zones of interest, and establish pressure gradients for fluid type identification;Identify zones in hydraulic communication or isolation;Collect representative formation fluid samples;Estimate formation fluid mobility. Clearly, an overlap exists between the two techniques and whether one can replace the other depends on the specific well objectives. For example, some exploration wells are drilled solely for the purposes of confirming the existence of a hydrocarbon column, in which case a wireline formation test is probably sufficient. Other wells may be drilled to prove a minimum volume of hydrocarbon-fluids-in-place for which a reservoir limit well test is then the only option. Between these two extremes, there are a number of cases where it may be unclear whether a well test is required. The strongest reason not to perform the well test is, of course, financial. Environmental cost is also increasingly important. The decision to test has to be made taking into account the cost of acquiring the information. This implies an understanding of what that information is and whether it can be acquired by other means.1 The collection of representative fluid samples is often an important objective of both wireline formation tests and well tests. This is clearly an area of overlap where wireline formation tests have proved to be a valid alternative to well tests.2 However, there are cases where the collection of fluid samples at surface may also be considered necessary.3 In this paper, the focus is on the information that can be obtained from the pressure transients recorded during a wireline formation test and how the information compares with the data recorded during a well test.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWell testing in exploration and appraisal wells has become increasingly unpopular. Reasons include costs, safety and environmental impact. Well testing has also become rare in production wells because of the potential revenue loss during build-ups. Whether suitable alternatives can be found for sampling and reservoir parameter estimation is the subject of regular debate. Alternatives are wireline formation tests in exploration and appraisal, and continuous recording with permanent pressure gauge in production wells.The quality of pressure and rate transients measured during wireline formation tests has improved greatly in recent years. The transients obey the same laws of physics as those measured during a well test and can theoretically be interpreted in the same way. The scale of the measurements, however, is very different. The challenge is to understand what the wireline formation test interpretation results mean and how they can be upscaled to the information provided by a well test.The paper discusses these key issues with examples that illustrate the quality of the data and the analysis process. The interpretation methods are essentially the same as those used in well test analysis with the addition of the formation rate analysis plot, which is particularly useful in high permeability formations where other methods are limited by pressure gauge resolution.

Uncertainty in well test analysis results from errors in pressure and rate measurements, from uncertainties in basic well and reservoir parameters; from the quality of the match with the interpretation model; and from the non-uniqueness of the interpretation model. These various uncertainties, except the non-uniqueness of the interpretation model, were examined in SPE 113888. It was concluded that the permeability-product kh is generally known within 15%; the permeability k, within 20% (because of the uncertainty on the thickness h); and the skin effect S, within 8% for high S values and within ±0.5 for low S values. Distances (half-fracture lengths, horizontal well lengths, and distances to reservoir boundaries) are usually known within 25%. The issue of non-uniqueness of the interpretation model is more complex: not only may there be a multitude of possible models for any one derivative response (the usual inverse problem), but there may be also a multitude of derivative responses, due to the uncertainty inherent in the observed data. This paper presents a methodology for assessing the derivative response uncertainty using deconvolution. It is shown that the uncertainty depends mainly on the error bounds for initial pressure and flow rates, which yield a range of possible shapes for the deconvolved pressure derivative and therefore different possible interpretation models. In some cases, the non-uniqueness of deconvolution can be reduced using knowledge of the expected model response, for instance from geology or seismic. In the absence of differentiating information, however, alternative interpretation models have to be considered, which may lead to completely different development options. The methodology is illustrated with three field examples.

Radius of investigation and tested volume are important results of well test analysis which can make or break future field development. Currently, their evaluation is either approximate or relies on a complete analysis of the transient pressure response using an appropriate model. A new method is proposed that uses the deconvolved derivative response to determine a minimum tested volume. It is accurate and does not rely on further transient analysis. The method can be applied to any oil or gas well test. It is simple and only requires input of data that is known at the time of testing. Furthermore, if the uncertainty in the deconvolution is quantified, then the uncertainty in tested volume is also defined. Field examples of both oil and gas well tests are presented which demonstrate how tested volume is easily calculated. Radius of investigation is calculated from the tested volume by making assumptions about the reservoir geometry. The method relies on a good deconvolution algorithm which can also compute the error bars in the derivative response. Recent advances in deconvolution algorithms have enabled the use of this simple but powerful new method to accurately calculate tested volume without the need for complex transient analysis. Introduction In the last thirty years, there have been many advances in well test analysis (Earlougher 1, 1977, Gringarten 2, 2007) of which deconvolution is probably the most recent. In the context of the analysis of pressure transients recorded during the testing or production of a well, deconvolution is the extraction of an equivalent single rate pressure response from the actual well response which is usually a continuous variation in rate and pressure. Within its known limitations (Levitan 3, 2004), it is a powerful technique because it allows the engineer to see the transient response of the well and reservoir without the complexity of the effects of superposition. Furthermore, the deconvolved response applies to the entire duration of the test rather than the duration of any particular constant rate period within the test (usually a build-up). A well test analysis work flow starts with the diagnosis of the transient response using a log-log plot of the pressure change and derivative response. Flow regimes are identified and an appropriate model selected. Often the models are analytical and they describe the pressure response of the well producing at constant rate for a given set of model parameters. The principle of superposition is then used to convert the constant rate response into the response due to the actual rates at which the well flowed. The resulting pressure history simulation is then compared with the observed data on a variety of plots. The parameters of the model are adjusted manually or automatically using regression techniques (Levenberg-Marquardt 4,5) until the best fit between the model simulation and the observed pressure response is obtained. If a numerical model is selected, a similar process is used but without the constraints of linearity that are imposed by the use of analytical models and superposition (Houze 6,7 et al, 2002, 2007).