Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering10867710.54859/kjogi108677Research ArticleAutomatic selection of sites for drilling candidate injection wellsBekenAidana A.a.beken@kmge.kzIbrayevAktan Ye.ak.ibrayev@kmge.kzZhetruovZhassulan T.zh.zhetruov@kmge.kzhttps://orcid.org/0000-0003-3639-4390YelemessovAzamat S.ayelemessov@kmge.kzZholdybayevaAssel T.a.zholdybayeva@kmge.kzhttps://orcid.org/0000-0002-1015-0593KMG Engineering030420246174860210202323022024Copyright © 2024, Beken A.A., Ibrayev A.Y., Zhetruov Z.T., Yelemessov A.S., Zholdybayeva A.T.2024<p><strong>Background: </strong>The increasing difficulty in finding sites for drilling injection wells at the later stages of field development by NC “KazMunayGas” JSC, due to infill drilling of the grid of existing wells and uneven reserve production, is a pressing problem today. Developments in geospatial analysis and artificial intelligence have stimulated the search for new approaches to solve this problem.</p> <p><strong>Aim:</strong> The research is aimed at developing an innovative approach to automatically identifying the most promising sites for drilling injection wells, based on comprehensive analysis of large volumes of data using advanced algorithms.</p> <p><strong>Materials and methods: </strong>The work uses methods for collecting and analyzing production and geological data, uses spatial algorithms for multivariate analysis and data normalization methods, including the adjusted interquartile range to determine outliers.</p> <p><strong>Results:</strong> Results are described showing the ranking of cells by drilling potential based on comprehensive analysis, as well as the assignment of unique codes to each cell to improve decision-making accuracy.</p> <p><strong>Conclusion: </strong>Directions for further research are noted, including analysis of data inaccuracies, consideration of additional parameters, identification of effective interlayers, application of machine learning methods, and expansion of testing of the approach in other fields.</p>injection wellsselection of candidate wellswell spacingcellsfirst radius of wellshydrodynamic studies of wellsайдау ұңғымаларыұңғыма-кандидаттарды іріктеуигеру торыұяшықтарұңғымалардың бірінші радиусыұңғымаларды гидродинамикалық зерттеунагнетательные скважиныподбор скважин-кандидатовсетка разработкиячейкипервый радиус скважингидродинамические исследования скважин1.Wei B. Well Production Prediction and Visualization Using Data Mining and Web GIS [master's thesis]. Calgary: University of Calgary; 2016. doi:10.11575/PRISM/28686.2.Xu X, Shao Y, Fu J, et al. The Application of GIS in The Digital Oilfield Construction. 2nd International Conference on Computer Science and Electronics Engineering; March 2013. Available from: https://www.atlantis-press.com/proceedings/iccsee-13/4443.3.Khan H, Srivastav A, Kumar Mishra A, Anh Tran T. Machine learning methods for estimating permeability of a reservoir. Int J Syst Assur Eng Manag. 2022;13:2118–2131. doi:10.1007/s13198-022- 01655-9.4.Ruizhi Z, Cyrus S, Ray J. Machine learning for drilling applications: A review. Journal of Natural Gas Science and Engineering. 2022;108. doi:10.1016/j.jngse.2022.104807.5.Ramzey H, Badawy M, Elhosseini M, A. Elbaset A. I2OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing. Energies. 2023;16(4). doi:10.3390/en16042023.6.Schiozer DJ, Souza dos Santos AA, Graça Santos SM, Von Hohendorff Filho JC. Model-based decision analysis applied to petroleum field development and management. Oil & Gas Science and Technology – Revue d’IFP Energies Nouvelles. 2019;74. doi:10.2516/ogst/2019019.7.Hubert M., Vandervieren E. An adjusted boxplot for skewed distributions. Computational Statistics & Data Analysis. 2008;52(12):5186–5201. doi:10.1016/j.csda.2007.11.008.