Down-hole transient temperature, pressure and flow-rate data processing and integrated interpretation for nonlinearity diagnosis using wavelet transform

Abstract

During oil and gas field development, continuous and real-time pressure, temperature and, occasionally, flow-rate data are available. Considering that the transient data acquisition process is relatively independent, integrating the analysis of the transient temperature, pressure and flow-rate data can include a large amount of useful information and can significantly reduce interpretation uncertainties.Typically, permanent down-hole gauge (PDG) data contain noise, outliers and other types of error signals that do not comport with known physical properties due to the uncontrolled collection conditions. For the purpose of correctly interpreting transient pressure and temperature data, data processing is an essential preliminary step. Several effective methods associated with pressure data processing have been applied in practice, but temperature data processing has not been discussed at length. In addition, the nonlinearities, which are caused by gas/multi-phase flow, non-Darcy flow or a change in well-reservoir parameters, may result in the erroneous application of pressure transient analysis (PTA) methods. As a result, an effective method for nonlinearity diagnosis needs to be developed.This paper describes how transient temperature, pressure and flow-rate data were processed and interpreted. First, the theory of wavelet transform was briefly introduced. Second, a workflow for the transient down-hole data processing was developed. Third, the simplest wavelet transform method, the Haar wavelet, was selected as the transient identification algorithm. In the pressure case and the temperature case, it was found that large amplitudes in the wavelet detail coefficients were caused by flow events. Fourth, the relationship between the wavelet transform amplitude and rate change (unit-rate change coefficient) was analysed, and an improved diagnostic method of nonlinearity from both transient pressure and temperature data was developed. According to the derived analytical solutions, the nonlinearities caused by a change in well-reservoir properties can be identified. Finally, the way in which the unit-rate change coefficients behave with the changes of different reservoir-well parameters was also researched by conducting sensitivity studies.As the errors that are caused by measurement technical problems or interpretation constraints in pressure data are not expected to appear in the transient temperature data, the applications of both synthetic datasets and real field datasets demonstrated that the temperature data can provide additional constraints for pressure data. Additionally, the reliability of the developed methods, which reveal complementary reservoir information from transient temperature data, was verified.