Application of the new multi-component suspension model for skin-factor evaluating on the wells equipped with gravel packs

UDK: 622.276.5.05
DOI: 10.24887/0028-2448-2018-12-63-67
Key words: filtration, deep bed filtration, gravel pack, sand production
Authors: M.M. Khasanov (Gazprom Neft PJSC, RF, Saint-Petersburg; Gazpromneft NTC LLC, RF, Saint-Petersburg), K.E. Lezhnev (Gazpromneft NTC LLC, RF, Saint-Petersburg; Peter the Great St. Petersburg Polytechnic University, RF, Saint-Petersburg), V.D. Pashkin (Saint-Petersburg State University, RF, Saint-Petersburg), A.P. Roshchektaev (Gazpromneft NTC LLC, RF, Saint-Petersburg)

and production can become a major problem during the development of weakly consolidated reservoirs. Sand control methods include various downhole filters such as slotted liners, wire wrapped screens, gravel packs, etc. However, only a few methods are capable to evaluate and predict the effectiveness of the sand control method depending on the geological and mechanical parameters of the reservoir.

The article presents a new model of multi component suspension that can be used to estimate the additional pressure drop due to the presence of gravel filter. The constructed model describes fluid flow with solid particles of different sizes in a porous medium. The model is based on the conservation of mass equations for individual phases in multiphase flow. The phases considered in the model include carrier fluid, mobile and trapped solid particles. Empirical relationships of suspension viscosity on the concentration of solid particles, the dependence for the particles trapping probability, and the formula connecting the permeability and the porosity of a gravel filter were used as constitutive relationships. In contrast to the previously presented models, in this article, particles of different sizes are considered as separate phases, so that particle size distribution is taken into account. Adaptation can be performed by comparing the model calculations with the results of numerical experiments based on the discrete element method or with field data. The model, set up by the first method, can be used to estimate the changes in the parameters of a gravel filter in time, based solely on the particle size distribution of the formation rock. The data obtained from the operation allows improving this evaluation.

In general, the presented model can be used to calculate the dynamic changes in the skin factor of a well equipped with a gravel filter, and also potentially to optimize the sizing criteria for gravel packs.

References

1. Saucier R., Considerations in gravel pack design, Journal of Petroleum Technology, 1974, V. 26, no. 2, pp. 205–212.

2. Unneland T., An improved model for predicting high-rate cased-hole gravel-pack well performance, SPE 54759-MS, 1999.

3. Furui K., Zhu D., Hill A., A new skin factor model for gravel-packed completions, SPE 90433-MS, 2004.

4. McDowell-Boyer L., Hunt J., Sitar N., Particle transport through porous media, Water Resourses Research, 1986, V. 22, no. 13, pp. 1901–1921.

5. Boronin S.A., Osiptsov A.A., Tolmacheva K.I., Multi-fluid model of suspension filtration in a porous medium, Fluid Dynamics, 2015, V. 50, no. 6, pp. 759–768.

6. Sacramento R., Yang Y., You Z. et al., Deep bed and cake filtration of two-size particle suspension in porous media, Journal of Petrolium Science and Engineering, 2015, V. 126, pp. 201–210.

7. Lezhnev K., Application of discrete element method for modelling sand control systems (In Russ.), SPE 191525-18RPTC-MS, 2018.

8. Coelho D., Thovert J.-F., Adler P., Geometrical and transport properties of random packings of spheres and aspherical particles, Physical Review E, 1997, V. 55, no. 2, pp. 1959–1978.

9. Rong L., Dong K., Yu A., Lattice-Boltzmann simulation of fluid flow through packed beds of uniform spheres: Effect of porosity, Chemical Engineering Science, 2013, V. 99, pp. 44–58.

10. Osiptsov A., Hydraulic fracture conductivity: effects of rod-shaped proppant from lattice-Boltzmann simulations and lab tests, Advances in Water Resources, 2017, V. 104, pp. 293–303.

11. Van den Hoek P., Geilikman M., Prediction of sand production rate in oil and gas reservoirs, SPE 84496-MS, 2003.

12. Wang J., Walters D., Wan R., Settari A., Prediction of volumetric sand production and wellbore stability analysis of a well at different completion schemes, The 40th U.S. Symposium on Rock Mechanics (USRMS), Anchorage, Alaska, USA, 05-842 ARMA Conference Paper, 2005.

13. Sharma M., Wang H., A fully 3-D, multi-phase, poro-elasto-plastic model for sand production, SPE 181566-MS, 2016.

14. Wu C., Sharma M., Fuller M., Mathis S., Estimating sand production through gravel packs, SPE 189481-MS, 2018.



Attention!
To buy the complete text of article (Russian version a format - PDF) or to read the material which is in open access only the authorized visitors of the website can. .