Application of deconvolution in interpretation of well test data of gas-saturated low-permeability formations of the Achimov deposits

Introduction and aim. An important task in assessing well interventions (hydraulic fracturing, repeated hydraulic fracturing), assessing the state of hydraulic fractures, the energy state of the reservoir, and assessing the volumes of drained reserves is to provide high-quality input information about the “reservoir-wellbore” system. To solve such problems, well testing is used, the duration of which can be limited by both geological features of the field structure and economic factors.

Materials and methods. In order to increase the information content of the studies, alternative tools for processing well testing data are used. Such tools require the use of permanent bottomhole pressure and temperature monitoring systems, as well as permanent well flowmeters for correct determination of the well stream fractional composition (these can be stationary multiphase flow meters or mobile metering complexes). One of such tools is deconvolution. Deconvolution converts previously recorded bottomhole pressure at a changing rate into a constant-rate pressure profile, i.e. into a pressure level-offcurve with a duration equal to the entire well operation history. The resulting pressure curve is then processed using traditional well testing methods, including changes in gas properties.

Results. The paper describes examples of applying deconvolution on real well data from one of the ROSPAN INTERNATIONAL license blocks.

Conclusion. Recommended for use under applicable conditions.

1. Tulenkov S.V., Tulenkova S.V., Mamonov D.M., Romashkin S.V., Zakharzhevsky Yu.A. Testing deconvolution in interpreting well testing data from wells with hydraulic fracturing in gas-saturated low-permeable Achimov reservoirs. Rossiyskoye gazovoye obshchestvo [Russian Gas community]. 2024, no. 3(45), pp. 103–109. (In Russ.)

2. Afanaskin I.V., Volpin S.G., Babaevsky A.V. et al. Methodology for using numerical flow simulation in interpreting pressure buildup curves in complex cases. Inzhenernaya praktika [Engineering Practice]. 2013, no. 10, pp. 58–61. (In Russ.)

3. Von Schroeter T., Hollaender F., Gringarten A.C. Deconvolution of Well-Test Data as a Nonlinear Total Least-Squares Problem. SPE Journal, 2004, vol. 9, no. 4, pp. 375–390.

4. Gringarten A.C. From Straight lines to Deconvolution: the Evolution of the State of the art in Well Test Analysis, SPE paper 102079 presented at the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, U.S.A., 24–27 September 2006. SPEREE. Feb. 2008, no. 11–1, pp. 41–62.

5. Gulyaev D.N., Batmanova O.V. Pulse-code interference testing and multiwell deconvolution algorithms — new technologies for measuring reservoir properties in the interwell space. Bulletin of the Russian New University. Series: Complex Systems: Models, Analysis, Management. 2017, no. 4, pp. 26–32. (In Russ.)

6. Levitan M.M., Wilson M.R. Deconvolution of Pressure and Rate Data From Gas Reservoirs with Significant Pressure Depletion. SPE paper 134261. Presented at the 2010 Annual Conference and Exhibition held in Florence, Italy, 19–22 September, 2010.

7. Levitan M.M. Practical Application of Pressure-Rate Deconvolution to Analysis of Real Well Tests. SPE 84290. 2003.

8. Halzagarov M.P. Mul’tiskvazhinnaya dekonvolyutsiya i modelirovaniye razrabotki dlya uvelicheniya koeffitsiyenta okhvata [Multiwell deconvolution and development modeling to increase the sweep efficiency] / Final Master’s Qualifying Paper. — St. Petersburg: Peter the Great St. Petersburg Polytechnic University, 2019. (In Russ.)