The use of deconvolution in interpreting field data on pressures and flow rates obtained from gas wells requires the use of nonlinear influence functions, as well as taking into account the dependences of viscosity and supercompressibility of gas on pressure. The deconvolution algorithm is based on the well-known von Schroeter approach, supplemented by Levitan modifications that remove restrictions on the solution at small times. In addition, the implemented algorithm enables to use current PVT correlations of gas for the object of investigation. The diagnostic graph after applying deconvolution enables to transform long-term historical production data containing periods of production and shutdown into a pressure stabilization curve during production with a constant flow rate, which increases the time range of data on the diagnostic graph compared to pressure test analysis of the longest shutdown period. Deconvolution has become widespread among foreign researchers, but in our country it is practically not used due to the insufficient development of the methodological base. Articles with the results of testing the application based on field data of low-permeability gas formations are rare. The article discusses the results of approbation an innovative approach to monitoring gas wells implemented in the corporate software package RN-VEGA. The results of deconvolution were tested on the basis of actual data on gas wells of ROSPAN INTERNATIONAL JSC. Comparison of diagnostic graphs after applying deconvolution and rate transient analysis adapted to field data also shows their good convergence.
References
1. Von Schroeter T., Hollaender F., Gringarten A.C., Deconvolution of well-test data as a nonlinear total least-squares problem, SPE-71574-MS, 2004,
DOI: http://doi.org/10.2118/71574-MS
2. Levitan M.M., Practical application of pressure/rate deconvolution to analysis of real well tests, SPE-84290-RA, 2005, DOI: http://doi.org/10.2118/84290-PA
3. Levitan M.M., Wilson M.R., Deconvolution of pressure and rate data from gas reservoirs with significant pressure depletion, SPE-134261-RA, 2012,
DOI: http://doi.org/10.2118/134261-PA
4. Kovalev A.L., Interpretation of gas-dynamic studies of wells of the Myldzhinskoye gas condensate field in non-stationary filtration modes using the influence function (In Russ.), Vesti gazovoy nauki, 2013, no. 1(12), pp. 192–198.
5. Geravand R., Foroozesh J., Nakhaee A., Abbasi M., Well-test deconvolution analysis of gas condensate layered reservoirs, Offshore Technology Conference, 2020, DOI: http://doi.org/10.4043/30291-MS
6. Kim J., Jang Y., Ertekin T., Sung W.M., Production analysis of a shale gas reservoir using modified deconvolution method in the presence of sorption phenomena, SPE-177320-MS, 2015, DOI: http://doi.org/10.2118/177320-MS
7. Buzinov S.N., Umrikhin I.D., Issledovanie neftyanykh i gazovykh skvazhin i plastov (The study of oil and gas wells and reservoirs), Moscow: Nedra Publ., 1984, 269 p.
8. Davletbaev A.Ya., Asalkhuzina G.F., Urazov R.R., Sarapulova V.V., Gidrodinamicheskie issledovaniya skvazhin v nizkopronitsaemykh kollektorakh (Hydrodynamic studies of wells in low-permeability reservoirs), Novosibirsk: DOM MIRA Publ., 2023, 176 p.
9. Ishkin D.Z., Nuriev R.I., Davletbaev A.YA. et al., Decline-analysis/“short” build-up welltest analysis of low permeability gas reservoir (In Russ.), SPE 181974-RU, 2016, DOI: http://doi.org/10.2118/181974-RU
