Application of Machine Learning for Target Selection and Acid Treatment Design

Acid treatment is commonly used to enhance the production capacity of wells drilled in carbonate deposits. However, field outcomes of this procedure may vary significantly. Current approaches to acid treatment design rely on advanced software tools that evaluate major acidizing factors. Machine learning is a valuable complement to the existing techniques: it facilitates the selection of target wells and aids in defining initial parameters for design engineering on reliable and effective software platforms. This study examines potential applications of machine learning in target selection based on the history of treatment outcomes influenced by the initial well conditions, operational conditions, treatment frequency, acid volumes, acid system types, pretreatment strategies, acid system diverters, and acid residence time. Acid treatment design requires complex laboratory work to investigate the kinetics of acid-rock interactions determined by the mineral composition of the rock formation and the chemical properties of the acid system, including the concentrations of its components. The problem of predicting the reaction kinetics of acid systems by processing an array of laboratory data using machine learning methods, specifically linear regression and random forest methods, was discussed. It was demonstrated that the incorporation of machine learning enables the development of robust decision-making algorithms that optimize acid treatment by considering its multifactorial effects. These algorithms significantly simplify the tasks of acid treatment design.

1. Akbar M., Vissapragada B., Alghamdi A.H., Allen D., Herron M., Carnegie A., Dutta D., Olesen J.R., Chourasiya R.D., Logan D., Stief D., Netherwood R., Russell S.D., Saxena, K. A snapshot of carbonate reservoir evaluation. Oilfield Rev., 2000, vol. 12, no. 4, pp. 20–41.

2. Burchette T.P. Carbonate rocks and petroleum reservoirs: A geological perspective from the industry. In: Garland J., Neilson J.E., Laubach S.E., Whidden K.J. (Eds.) Advances in Carbonate Exploration and Reservoir Analysis. Ser.: GSL Special Publications. Vol. 370. Geol. Soc. London, 2012. pp. 17–37. https://doi.org/10.1144/SP370.14.

3. Janjuhah H.T., Alansari A. Offshore carbonate facies characterization and reservoir quality of Miocene rocks in the southern margin of South China Sea. Acta Geol. Sin., 2020, vol. 94, no. 5, pp. 1547–1561. https://doi.org/10.1111/1755-6724.13880.

4. Hall-Thompson B., Ernesto A.R., Abdulrahman N., Alsuhaimi A. Acid stimulation-best practices for design, selection and testing of acid recipes in low permeability carbonate reservoirs. Proc. Int. Pet. Technol. Conf. (January 13–15, 2020). Dhahran, 2020, art. IPTC-19690-MS. https://doi.org/10.2523/IPTC-19690-MS.

5. Nasibulin I.M. Justification of acid stimulation technique for carbonate reservoirs using multifunctional compositions with controlled reactivity. Extended Abstract of Cand. Tech. Sci. St. Petersburg, 2013. 20 p. (In Russian)

6. Kopytov A.G., Levkovich S.V., Levitina E.E., Kovalev I.A. Efficiency of acid treatment of the bottom borehole zone of the well for sediments with hard-to-remove reserves. Nauka. Innovatsii. Tekhnol., 2021, no. 4, pp. 27–40. https://doi.org/10.37493/2308-4758.2021.4.2. (In Russian)

7. Sengul M., Remisio L.H.A. Applied carbonate stimulation – an engineering approach. Proc. Abu Dhabi Int. Pet. Exhib. Conf. (October 13–16, 2002). Abu Dhabi, UAE, 2002, art. SPE-78560-MS. https://doi.org/10.2118/78560-MS.

8. Burnashev V.F., Khuzhaerov B.K. Modeling the acid treatment of the dolomitic collector of an oil formation bottom-hole zone with account of rock colmatation. Fluid Dyn., 2015, vol. 50, no. 1, pp. 71–78. https://doi.org/10.1134/S0015462815010081.

9. Economides M.J., Nolte K.G. (Eds.) Reservoir Stimulation. New York, NY, Prentice Hall, 1989. 440 p.

10. Liu X., Ormond A., Bartko K., Li Y., Ortoleva P. A geochemical reaction-transport simulator for matrix acidizing analysis and design. J. Pet. Sci. Eng., 1997, vol. 17, nos. 1–2, pp. 181–196. https://doi.org/10.1016/S0920-4105(96)00064-2.

11. Novikov V.A. The method of predicting efficiency of the matrix acid treatment of carbonates. Perm J. Pet. Min. Eng., 2021, vol. 21, no. 3, рр. 137–143. https://doi.org/10.15593/2712-8008/2021.3.6.

12. Galkin V.I., Khizhnyak G.P., Amirov A.M., Gladkikh E.A. Assessing the efficiency of core sample acidizing using regression analysis. Vestn. PNIPU. Geol. Neftegazov. Gorn. Delo, 2014, no. 13, рр. 38–48. https://doi.org/10.15593/2224-9923/2014.13.4. (In Russian)

13. Fredd C.N. Dynamic model of wormhole formation demonstrates conditions for effective skin reduction during carbonate matrix acidizing. Proc. SPE Permian Basin Oil Gas Recovery Conf. (March 21–23, 2000). Midland, TX, 2000, art. SPE-59537-MS. https://doi.org/10.2118/59537-MS.

14. Safari A., Dowlatabad M.M., Hassani A., Rashidi F. Numerical simulation and X-ray imaging validation of wormhole propagation during acid core-flood experiments in a carbonate gas reservoir. J. Nat. Gas Sci. Eng., 2016, vol. 30, pp. 539–547. https://doi.org/10.1016/j.jngse.2016.02.036.

15. Kurganov D.V. Assessing the effectiveness of bottomhole treatment in oil wells using machine learning techniques. Avtom. Protsessov Upr., 2020, no. 1 (59), pp. 47–57. https://doi.org/10.35752/1991-2927-2020-1-5-47-54. (In Russian)

16. Zangl G., Hannerer J. Data Mining: Applications in the Petroleum Industry. East Nynehead, Round Oak Publ., 2003. 222 p.

17. Hadavimoghaddam F., Mishchenko I.T., Mostajeran M. Using artificial intelligence methods in forecasting the key oil properties. Gazov. Prom‑st., 2019, no. 12 (794), pp. 28–32. (In Russian)

18. Scikit-Learn User Guide. URL: https://scikit-learn.org/stable/_downloads/scikit-learn-docs.pdf.

19. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 2011, vol. 12, no. 85, pp. 2825−2830.