TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractLong-term data from permanent gauges have the potential to provide more information about a reservoir than data from traditional pressure transient tests that last for a relatively small duration. Besides reducing ambiguity and uncertainties in the interpretation, long-term data also provide an insight on how reservoir properties may change as the reservoir is produced. This type of long-term surveillance provides the opportunity to look at the reservoir information in four dimensions rather than obtaining a glimpse or snapshot in time. However, the installation of permanent downhole gauges is only a recent phenomenon, and a methodology for the interpretation of the data has yet to be developed. The use of long-term data requires special handling and interpretation techniques due to the instability of in-situ permanent data acquisition systems, extremely large volume of data, incomplete flow rate history caused by unmeasured and uncertain rate changes, and dynamic changes in reservoir conditions and properties throughout the life of the reservoir.This study developed a multistep procedure for the processing and interpretation of long-term pressure data. The procedure was tested with several sets of simulated and actual field data. It was found to be an effective approach for the analysis of long-term pressure data from permanent gauges.

Summary Long-term data from permanent gauges have the potential to provide more information about a reservoir than data from traditional pressure-transient tests that last for a relatively small duration. Besides reducing ambiguity and uncertainties in the interpretation, long-term data also provide an insight on how reservoir properties may change as the reservoir is produced. This study developed a multistep procedure for the processing and interpretation of long-term pressure data. The procedure was tested with several sets of simulated and actual field data. It was found to be an effective approach for the analysis of long-term pressure data from permanent gauges.

We propose a robust way of achieving a well test interpretation by combining the sequential predictive probability method with an artificial neural network approach. The sequential predictive probability method considers all possible reservoir models and determines which candidate model or models best predict the well response. This method is dependent on obtaining good initial estimates for the parameters governing the candidate reservoir models, which is achieved by applying the artificial neural network approach. We use the neural network to identify the characteristic components of the pressure derivative curve corresponding to the flow regimes known to be in each candidate model. Reservoir parameters are then computed using the data in the identified range of the corresponding behavior. As a final step, the candidate models and their initial estimates are evaluated using the sequential probability method. The method discriminates between the candidate models and simultaneously performs nonlinear regression to compute the best estimates of reservoir parameters. The trained neural network was able to identify the characteristic components of the derivative curve in most cases. The algorithm written to interpret the neural network signals into flow regimes required special procedures to take care of the misclassification from the neural network. The initial estimates of reservoir parameters from the neural network were found to be reasonably close to the eventual estimates from the sequential predictive probability method. Introduction Traditional methods of well test interpretation are usually based on a combination of manual and automated techniques, although both techniques are usually computer based. Manual interpretation uses the pressure derivative plot introduced by Bourdet et al. The characteristics of different reservoir flow regimes can be observed from the plot. Hence, we are able to analyze the type of the associated reservoir and determine their parameters from the appropriate flow regimes. Automated interpretation by nonlinear regression is then used to determine the best estimates of reservoir parameters, and confidence intervals are used to authenticate the selected reservoir model. With new developments in pressure measurement, including permanently installed gauges, we may have an enormous amount of pressure data coming in each day. This study looked at a procedure to mechanize the interpretation of such well test data. There are three key steps in the procedure. First, all the characteristic components of the derivative plot have to be recognized. This task is accomplished by a specially trained neural network. Second, the signals from the neural network are translated into reservoir flow regimes so that initial estimates of reservoir parameters can be evaluated. Third, the sequential predictive probability procedure discriminates between candidate reservoir models, simultaneously performing nonlinear regression based on the initial estimates provided by the neural network. Previous Work Allain and Horne used syntactic pattern recognition and a rule-based system to identify the reservoir model and to estimate its parameters. The pressure derivative data were first preprocessed in order to distinguish the true response from the noise. P. 249

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Due to increasing costs in hydrocarbon exploration, formation evaluation needs to be efficient in order to avoid excessive expenditures. Proper reservoir characterization in thinly laminated reservoirs is a key to successful field development. These thinly laminated reservoirs are complex due to their vertical heterogeneity. As a result, there is low resistivity contrast between water and hydrocarbon bearing zones when standard resistivity logs are used. Thus, it is crucial to deploy a high resolution formation evaluation in order to capture reservoir pay and detect hydrocarbon zones. This paper aims to demonstrate a methodology of using borehole electrical image log to determine effective permeability. The paper is the first attempt to develop a numerical technique to build a correlation between synthetic resistivity derived from borehole electrical image tool with other dynamic permeability measurements such as dual packer formation tester. A single well predictive model was used in the process to generate a high resolution numerical radial model from high resolution log data. Since the 1980's, micro electrical imaging tools have mainly been used for sedimentological and structural interpretation, qualitative interpretation for sand quality, and also for the sand count. However, from the systematic integration of borehole electrical image, NMR, pressure transient from an Interval Pressure Transient Test (IPTT) in the thinly bedded reservoirs, it is possible to use electrical image log variations for permeability estimations. This study therefore aims to develop the methodology for actual field data to predict well productivity. Introduction Laminated formations pose two major evaluation challenges for reservoir engineers:- 1st is the classic low resistivity pay problem as seen in vertical wells. Layers of clay, silt and fine-grained sand distributed within a hydrocarbon bearing sand will significantly reduce the apparent resistivity measurement. This low resistivity results in wrong fluid identification and therefore this zone might be overlooked. 2nd is the high angle well evaluation problem. The same laminated formation, when measured by an induction tool at moderate - high relative dip, will exhibit an increase in apparent resistivity beyond that measured in the vertical well. Again, the inaccurate calculation of water saturation and hydrocarbon volume may cause the error in reservoir estimation.