Hydrocarbon volume prediction performance in the Dutch subsurface and the impact of selection bias (2020)

MSc thesis by former intern Vincent van der Kraan.

The E&P industry is frequently characterized by disappointing project outcomes. Specifically, the industry fails to deliver what is promised in term of hydrocarbon volumes due to overly optimistic predictions (NPD Resource Report, 2018). Although this prediction bias problem is well-known amongst specialists involved, literature is scarce. Two suggested causes of the prediction bias are evaluation tool bias (e.g. imprecise seismic interpretation) and cognitive bias (e.g. individual motivational bias). In addition, Hoetz (2016) proposed the idea of a Selection Bias (SB) in the E&P industry. SB is based on the idea that more attractive prospects are assumed to be more matured; when these overly optimistic projects are drilled, they therefore result in disappointing volume delivery. In the Netherlands, the state-owned company EBN participates in virtually all E&P projects and has been reporting disappointing volumes for years (EBN Focus, 2019). This study aims to address this problem by identifying and quantifying key parameters that contribute to the volume prediction bias.

The main outcome of this study is that the look-back analysis of the EBN portfolio shows a volume bias of 42%. This can partly be explained by SB, but other factors contribute. The findings of this study can help prioritize which parameters need more careful attention in reviewing project proposals. Furthermore, including the effect of EB in resource prediction tools might help to improve prognosis quality.