Workplace safety is a main objective of any company working in the oil and gas business. The processes have been developed and established over the past decades based on individual experiences and causal pathways. The exhaustion of technical and administrative barriers has led to the introduction of behavioral safety. Recent advances in data technology and machine learning have disrupted many businesses and processes and can lead to a new paradigm in workplace safety as well.
This case study demonstrates the application of data science and predictive analytics to aid the health, safety, and environment (HSE) function and prevent accidents. Operational and accident data from the past 10 years at a leading oil and gas company has been analyzed to quantify the effectiveness of their safety programs.
The authors have determined how many accidents each program prevents and is able to prevent in an optimal setting. They determined the optimal level of engagement for each program and what level results in diminishing returns.
Further, a predictive model was developed to forecast the occurrence of accidents one month ahead of time. In this way, the HSE function is able to focus on 15% of locations to control 69% of the accidents. The forecast also was able to predict accidents at locations where one would traditionally not expect accidents to happen, such as locations with low activity.
This paper shows the potential for improvement that is possible with the emerging big data, artificial intelligence, and machine learning tools specifically in the field of workplace safety.
Find paper SPE 195737 on the HSE Technical Discipline Page free for a limited time.