Rig Sensor Data for AI-ML Technology-Based Solutions: Research, Development, and Innovations

The oil and gas industry is currently witnessing a notable shift towards automation and digitalization, driven by cutting-edge technologies like artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and big data analytics. These advanced solutions are being implemented to enhance operational efficiency, improve profitability, and enable data-driven decision-making. Among the various segments of the petroleum industry, drilling operations for oil and gas wells hold immense significance due to the high sharing of the total well cost. Therefore, the adoption of technology-driven solutions is crucial to ensure safe operations and cost reduction. This paper presents the successful applications overview of machine learning in the drilling operations domain and addresses the existing challenges and future opportunities in this field. Surface drilling sensors record real-time drilling parameters such as weight on bit, hook load, drill string rotation, drilling torque, pumping rate and pressure, and rate of penetration. These drilling data parameters provide valuable information about the characteristics of the drilled rock, requiring appropriate preprocessing techniques for data quality improvement. Data collection, preprocessing, analysis, and the development of machine learning models for prediction and classification in drilling operations are significant areas of research. Numerous researchers have utilized drilling data in machine learning applications to predict and optimize drilling rate, drill string vibrations, rock characteristics, and other important variables. These models contribute to optimizing drilling parameters, enhancing operational performance, and reducing costs. The paper technically discusses the achievements in ML research and industrial applications within the drilling domain that cover various ML techniques, different data sources, diverse training data for model features, and the target outputs from the developed models. The research findings highlight the exceptional performance of ML applications, technically and economically, by showcasing successful case studies from the industry. Through the analysis of observations, valuable recommendations and potential future opportunities have been identified. These findings open up promising avenues for improvement and development in various areas. ML applications in drilling data have significantly contributed to the industry and academia by enabling real-time monitoring, advisory systems, automation, digitalization, and accurate prediction and classification through developed ML models.