Abstract
This article presents a comprehensive methodology for algorithmizing the selection and evaluation process of production well placement in mature oil fields, utilizing the combined power of geospatial analysis and machine learning techniques. As many mature fields approach the later stages of their production life cycle, challenges such as reservoir depletion, heterogeneity, and diminishing returns from conventional development methods necessitate the adoption of more data-driven, automated approaches. The integration of big data and advanced machine learning algorithms introduces new opportunities to optimize drilling strategies, minimize geological and operational uncertainties, and enhance the efficiency of hydrocarbon extraction. This study introduces a structured, automated approach divided into three key stages: identifying optimal drilling locations, forecasting production performance, and ranking candidate wells based on an extensive range of geological, petrophysical, and operational parameters. This methodology was tested on a complex multilayer oil field, using historical data from more than 3500 wells, and incorporated diverse datasets such as core analysis, production logs, seismic attributes, and pressure dynamics. Machine learning models demonstrated high predictive accuracy for key production metrics. The predictive accuracy of these models was confirmed by actual drilling results from operations conducted in 2024. The proposed algorithm demonstrated efficiency and accuracy comparable to traditional expert evaluations, but with significantly reduced time, human effort, and cost. The study highlights the transformative potential of integrating geospatial technologies with artificial intelligence in mature field development. It also provides insights into future improvements, including enhanced data fusion methods, real-time analytics, and model transparency. Keywords: oil fields development; drilling; mature oil field; machine learning; well placement forecasting; candidates ranking.