Quantitative Risk Assessments Are Useful but Have Limitations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 220333, “Quantitative Risk Assessments: Why They Fall Short and How To Use Them Better,” by Peter A.R. Hudson, SPE, and Timothy G.L. Hudson, SPE, Hudson Global Consulting. The paper has not been peer reviewed. _ This paper discusses the limitations of quantitative risk assessment (QRA) with respect to the usefulness of the concept in managing day‑to‑day and emerging risks and the effect of change. The current use of QRA creates a mismatch between the management of risks on paper and real‑world situations. This paper proposes updating QRA by transforming it into an active management tool linked to proactive management of risks through bowtie and similar models. Challenges of QRA The purpose of QRA is to establish a number that represents the likelihood of incidents and to undertake measures that mitigate risk to within an acceptable level. In a range of industries, it can be observed that what are presumed to be very unlikely events, based on QRA reports, nevertheless appear to occur with regularity. Major events, such as refinery explosions and rail fires, in theory, should occur so rarely that a single instance should not be expected. In oil and gas, the expected rate of fires and explosions is approximately one incident per 10 years per refinery. This is orders of magnitude away from the intended level. Hyperbolically, it should be closer to once in the lifetime of the universe. One real‑world example is the Texas City refinery explosion on 23 March 2005 that resulted in the deaths of 15 people and 170 injuries. Shortcomings in that incident included overreliance within QRA on historical data that did not reflect the unique conditions and actual operations at the refinery. The QRA underestimated the likelihood of organizational and human‑error deficiencies, which were significant contributors to the incident. The QRA used simplified assumptions that failed to capture the complexity of the refinery’s operations and the scale of catastrophic failure modes. An example from the nuclear industry is the Fukushima Daiichi nuclear disaster on 11 March 2011. Following a massive earthquake and tsunami, the cooling systems at the nuclear plant failed, leading to a core meltdown in three reactors, extensive radioactive contamination, and significant evacuation zones. The QRA underestimated the risks because of several factors, including the natural hazard, the effectiveness of redundancy systems, and the effect of organizational and regulatory failures. The QRA assumed that redundancy systems would function correctly without considering how a single event might cause simultaneous failures. These examples demonstrate that discrepancies between predicted failure rates or rates of negative outcomes from QRA and actual failure rates can be attributed to several factors. These factors include limitations in data, modeling assumptions, human factors, unforeseen events, and the complexity of real‑world operations. An appendix is included in the complete paper to ensure that unfamiliar readers understand the basic concepts of QRA and understand forms of data and sources for such data.