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Abstract Data-driven optimization for drilling operations is becoming increasingly important in the oil and gas industry. Digitalization and utilization of historical offset well data can provide critical insights into improving drilling performance and aid in making informed decisions for the best selection of drilling parameters. This paper aims to present an integrated approach between a research center and the field that leverages offset well data and drilling engineering domain knowledge with data science and machine-learning models to improve drilling performance through more stable drilling dynamics. This novel drilling optimization method utilizes a digitalized process that combines drilling dynamics data from offset wells and drilling engineering domain knowledge with advanced data science algorithms (statistical analysis, optimal searching, and machine learning) to generate optimum drilling parameters automatically. This hybrid optimization approach, integrating science and domain knowledge, aims to systematically minimize the energy dissipated in drilling shocks and vibration (S&V), which would then lead to maximizing drilling efficiency in a continuous improvement cycle. The effectiveness of this approach is proven through case studies from various oil and gas fields in which we discuss the associated key challenges for current and future developments. The authors showcase through these examples the significant gains in drilling engineering efficiency when automated data analytics’ workflows are used to make the utmost value of offset well data utilization in improving drilling parameters. The results include a significant reduction of S&V, which leads to better directional drilling accuracy and reduced downhole tool failure risks. The approach has also increased drilling performance, resulting in faster and more efficient drilling operations. Furthermore, the use of this approach has led to a reduction in overall drilling time, resulting in substantial cost savings for drilling major oil and gas fields.
Elghoneimy et al. (Mon,) studied this question.