DEVELOPMENT OF FAILURE PREDICTION MODEL FOR THE OIL AND GAS INDUSTRY

Abstract

The oil and gas is a crucial sector in the global economy, and the Republic of Kazakhstan has established itself as a significant player in this field. As the industry continues to evolve, companies are actively seeking ways to leverage advanced digital technologies to optimize their operations, enhance safety, and increase profitability. This capstone project represents a cutting-edge effort to develop a framework for failure prediction in the oil and gas sector. Through close collaboration with Caspi Neft, which is a leading digitalization pioneer in the Kazakhstani market, this study has produced a range of innovative deliverables. These include the failure prediction model for the oil and gas equipment, an example of a well. In addition, the project has developed a comprehensive framework for the implementation of the model, which enables it to be readily integrated into the enterprise resource planning (ERP) system of the company. To achieve these outcomes, the project team employed a range of advanced machine learning algorithms, leveraging a rich dataset provided by Caspi Neft. This dataset included detailed information on operational conditions, downtime history, and other critical variables, allowing the team to generate a highly accurate failure prediction model with a validation accuracy of over 90%. The model was further validated through expert review, demonstrating its robustness and applicability to real-world conditions. The oil and gas industry is characterized by a dynamic and evolving dataset, which presents challenges for long-term prediction and modeling. In addition, the industry is subject to strict confidentiality requirements, which can limit the availability of data for research purposes. Nonetheless, the present project represents a significant step forward in the establishment of advanced digital technologies for the oil and gas industry, with the potential to drive cost reductions, enhance safety, and increase efficiency in this vital sector.