Abstract
The objective of this work is to develop a system of models and an adaptive algorithm to analyze combined well test data with resulting inflow performance relationship (IPR) curve and pressure buildup (PBU) curves, which allows defining the reservoir parameters, the required number of test stages, and the test completion time while testing.
The adaptive system for data analysis of combined well test is based on an integrated system of interacting IPR and PBU curve models with time variable parameters and considering additional a priori information. The process of adaptive identification and interpretation of well test data implies consecutive interpretation of IPR and PBU curves at different test stages. The solution on test completion is taken on the basis of quality indicator by means of comparing current estimates with approximations obtained at the previous test stages.
The proposed system allows defining filtration parameters and the initial reservoir pressure as well as the required number of test stages and the test completion time, since input data are registered in real time. The case study of well test data analysis for gas wells equipped with Permanent Downhole Gauges (PDG) shows that the adaptive interpretation method provides robust estimates of reservoir parameters, which starting from the second test stage are almost as adequate as those obtained at the following stages.
This novel integrated system of IPR and PBU models takes into account additional a priori information with time variable parameters presented as unknown unambiguous functions of time. Adaptive algorithms of identification and interpretation allows obtaining robust estimates of reservoir parameters, required number of test stages and completion time of the test within data input process in real time.
The proposed adaptive system is a new resource-efficient technology to automate data analysis for intelligent well tests conducted with PDG in oil and gas fields.