Practical Considerations for Pressure-Rate Deconvolution of Well Test Data
- Michael M. Levitan (BP America Inc) | Gary E. Crawford (Well Test Solutions) | Andrew Hardwick (BP Amoco Co Ltd)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- March 2006
- Document Type
- Journal Paper
- 35 - 47
- 2006. Society of Petroleum Engineers
- 4.3.4 Scale, 5.3.2 Multiphase Flow, 5.3.1 Flow in Porous Media, 4.1.5 Processing Equipment, 4.1.2 Separation and Treating, 5.6.3 Pressure Transient Testing, 5.6.11 Reservoir monitoring with permanent sensors, 3.3 Well & Reservoir Surveillance and Monitoring, 5.1.1 Exploration, Development, Structural Geology, 5.6.4 Drillstem/Well Testing, 5.2 Reservoir Fluid Dynamics, 5.5 Reservoir Simulation
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Pressure-rate deconvolution provides equivalent representation of variable-rate well-test data in the form of characteristic constant rate drawdown system response. Deconvolution allows one to develop additional insights into pressure transient behavior and extract more information from well-test data than is possible by using conventional analysis methods. In some cases, it is possible to interpret the same test data in terms of larger radius of investigation.
There are a number of specific issues of which one has to be aware when using pressure-rate deconvolution. In this paper, we identify and discuss these issues and provide practical considerations and recommendations on how to produce correct deconvolution results. We also demonstrate reliable use of deconvolution on a number of real test examples.
Evaluation and assessment of pressure transient behavior in well-test data normally begins with examination of test data on different analysis plots [e.g., a Bourdet (1983, 1989) derivative plot, a superposition (semilog) plot, or a Cartesian plot]. Each of these plots provides a different view of the pressure transient behavior hidden in the test data by well-rate variation during a test. Integration of these several views into one consistent picture allows one to recognize, understand, and explain the main features of the test transient pressure behavior. Recently, a new method of analyzing test data in the form of constant rate drawdown system response has emerged with development of robust pressure-rate deconvolution algorithm. (von Schroeter et al. 2001, 2004; Levitan 2005).
Deconvolved drawdown system response is another way of presenting well-test data. Pressure--rate deconvolution removes the effects of rate variation from the pressure data measured during a well-test sequence and reveals underlying characteristic system behavior that is controlled by reservoir and well properties and is not masked by the specific rate history during the test. In contrast to a Bourdet derivative plot or to a superposition plot, which display the pressure behavior for a specific flow period of a test sequence, deconvolved drawdown response is a representation of transient pressure behavior for a group of flow periods included in deconvolution. As a result, deconvolved system response is defined on a longer time interval and reveals the features of transient behavior that otherwise would not be observed with conventional analysis approach.
The deconvolution discussed in this paper is based on the algorithm first described by von Schroeter, Hollaender, and Gringarten (2001, 2004). An independent evaluation of the von Schroeter et al. algorithm by Levitan (2005) confirmed that with some enhancements and safeguards it can be used successfully for analysis of real well-test data. There are several enhancements that distinguish our form of the deconvolution algorithm. The original von Schroeter algorithm reconstructs only the logarithm of log-derivative of the pressure response to constant rate production. Initial reservoir pressure is supposed to be determined in the deconvolution process along with the deconvolved drawdown system response. However, inclusion of the initial pressure in the list of deconvolution parameters often causes the algorithm to fail. For this reason, the authors do not recommend determination of initial pressure in the deconvolution process (von Schroeter et al. 2004). It becomes an input parameter and has to be evaluated through other means. Our form of deconvolution algorithm reconstructs the pressure response to constant rate production along with its log-derivative. Depending on the test sequence, in some cases we can recover the initial reservoir pressure.
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