Вестник нефтегазовой отрасли КазахстанаВестник нефтегазовой отрасли Казахстана2707-42262957-806XKMG Engineering10887810.54859/kjogi108878Research ArticleMethod of multimodal comparative ranking of project drilling points based on the normalized geological and technological parametersIbrayevAktan Ye.ibrayev.a@su.edu.kzhttps://orcid.org/0009-0005-1731-7092NegimEl-Sayedm.elsaid@satbayev.universityhttps://orcid.org/0000-0002-4370-8995ZhenisDinmuhammed K.dimashzhenis.pe@gmail.comhttps://orcid.org/0009-0003-4934-7347KurmashevAslana_kurmashev@kbtu.kzhttps://orcid.org/0009-0001-8807-4252SagyndykovaAdinaa_sagyndykova@kbtu.kzhttps://orcid.org/0009-0008-3352-3744Satbayev UniversityKMG EngineeringKazakh-British Technical University241220257418240406202526082025Copyright © 2025, Ibrayev A.Y., Negim E., Zhenis D.K., Kurmashev A., Sagyndykova A.2025<p><strong>Background:</strong> Effective reservoir management require integrating multiple geological and technological parameters to optimize decision-making. Traditional approaches, while useful, often struggle with the complexity and volume of reservoir data, highlighting the need for more advanced analytical methods.</p> <p><strong>Aim: </strong>This article examines various methodologies for data-driven comparative analysis and its application for selection of drilling points for production and water flooding operations.</p> <p><strong>Materials and methods: </strong>Advanced computational techniques, including machine learning applications, are explored for their role in improving evaluation accuracy. Additionally, this study compares different comparative analysis approaches used in the industry, highlighting their strengths, limitations, and adaptability to various geological conditions.</p> <p><strong>Results: </strong>The synthesis of recent research demonstrates the potential of multimodal analysis approaches to enhance predictive accuracy and decision-making efficiency. Comparative evaluations reveal that while traditional methods remain valuable in certain contexts, data-driven techniques provide superior adaptability and scalability. Future advancements are identified in integrating real-time data streams and cross-disciplinary modeling.</p> <p><strong>Conclusion: </strong>Data-driven comparative analysis, particularly when supported by machine learning, shows significant promise in improving reservoir management practices. By enabling more accurate drilling point selection and more effective water flooding operations, these approaches can drive both economic and operational efficiency. The study emphasizes the importance of continuous innovation and integration of computational tools to address the evolving complexity of reservoir systems.</p>multimodal analysisgeological parameterstechnological parameterscomparative analysiswell placement optimizationmachine learningreservoir managementмультимодалды талдаугеологиялық параметрлеригеру көрсеткіштерісалыстырмалы талдауұңғымаларды орналастыруды оңтайландырумашиналық оқытукен орнын игерумультимодальный анализгеологические параметрыпоказатели разработкисравнительный анализоптимизация размещения скважинмашинное обучениеразработка месторождений1.Fisher A., O’Keefe F.X., Niedz C., et al. 3D Driven Rock Quality Mapping and Landing Target Selection in the Wolfcamp Formation: A Case Study on How to Combine Geologic, Geophysical, and Engineering Data to Produce Better Well Results, Midland Basin, Texas // Unconventional Resources Technology Conference; July 2019; Denver, Colorado, USA. Available from: chooser.crossref.org/?doi=10.15530%2Furtec-2019-1147.2.Salehi A., Arslan I., Deng L., et al. A Data-Driven Workflow for Identifying Optimum Horizontal Subsurface Targets // SPE Annual Technical Conference and Exhibition; Sept 21–23, 2021; Dubai, UAE. Available from: onepetro.org/SPEATCE/proceedings-abstract/21ATCE/21ATCE/D011S011R004/469195.3.Martins B.V.D., Lesage A., Rondeleux B., et al. Use of Machine Learning Approach on the Results of a 3D Grid Model to Identify Impacting Uncertainties and Derive Low/High Production Profiles, FRF Team // Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC); Oct 31 – Nov 3, 2022; Abu Dhabi, UAE. Available from: onepetro.org/SPEADIP/proceedings-abstract/22ADIP/22ADIP/D031S073R004/513086.4.Kullawan K., Bratvold R.B., Bickel J.E. Value Creation with Multi-Criteria Decision Making in Geosteering Operations // SPE Hydrocarbon Economics and Evaluation Symposium; May 19–20, 2014; Houston, Texas, USA. Available from: onepetro.org/SPEHEES/proceedings-abstract/14HEES/14HEES/D021S009R002/211383.5.Бекен А.А., Ибраев А.Е., Жетруов Ж.Т., и др. Автоматический подбор зон для бурения нагнетательных скважин-кандидатов // Вестник нефтегазовой отрасли Казахстана. 2024. Том 6, №1. С. 74–86. doi: 10.54859/kjogi108677.