The urgency of the discussed issueis caused by the need to develop models and adaptive algorithms to analyze combined well test data of intelligent wells equipped with permanent downhole gauges which allow determining the parameters of reservoirs and wells when testing in real time. The main aim of the study is to increase the efficiency and quality of integrated systems of models (rates, bottomhole pressures, additional a priori data and expert estimates of gas reservoir parameters) with variable time-dependent parameters and algorithms for adaptive identification and interpretation of combined well test data at steady and unsteady state of well test by indicator diagram and pressure building up curve. The methods used in the studyare the theoretical and practical developments in well testing, system analysis, system identification, optimization of functions and linear algebra. For analyzing the accuracy and stability of models and algorithms of identification and interpretation the authors used the field data of well test by indicator diagram and pressure building up curve, expert estimates of reservoir pressure and filtration parameters. Results. The analysis of the efficiency and quality of models and algorithms of identification and interpretation on the examples of processing oil and gas well test data by indicator diagram and pressure building up curve has shown that integrated systems of models and algorithms of adaptive identification and interpretation allow: determining filtration parameters and the energy state of reservoirs and wells, the number of test stages and the completion time of the test within data input process in real time; providing stability and improving accuracy of estimates of permeability, reservoir conductivity, reservoir pressure, well skin factor due to the use and correction of additional a priori data and expert reservoir pressure and reservoir filtration parameters; analyzing short poorly reconstructed bottomhole pressure building up curves after well shut in.