The Value of Assessing Uncertainty

Abstract

Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is routinely below expectations. For example, the average reported return for the industry was around 7% in the 1990s, even though a typical project hurdle rate was at least 15%. The underperformance is generally attributed to poor project evaluation and selection due to chronic bias. While a number of authors have investigated cognitive biases in oil and gas project evaluation, there have been few quantitative studies of the impact of biases on economic performance.Incomplete investigation and possible underestimation of the impact of biases in project evaluation and selection are at least partially responsible for persistence of these biases.The objectives of this work were to determine quantitatively the value of assessing uncertainty or, alternatively, the cost of underestimating uncertainty. This work presents a new framework for assessing the monetary impact of overconfidence bias and directional bias (i.e., optimism or pessimism) on portfolio performance. For moderate amounts of overconfidence and optimism, expected disappointment (having realized NPV less than estimated NPV) was 30-35% of estimated NPV for typical industry portfolios and optimization cases. Greater degrees of overconfidence and optimism resulted in expected disappointments approaching 100% of estimated NPV.Comparison of simulation results with expected industry performance in the 1990s, indicates that these greater degrees of overconfidence and optimism have been experienced in the industry.iii The value of reliably quantifying uncertainty is in reducing or eliminating expected disappointment and expected decision error (selecting the wrong projects), which is achieved by focusing primarily on elimination of overconfidence; other biases are taken care of in the process. Elimination of expected disappointment will improve industry performance overall to the extent that superior projects are available and better quantification of uncertainty allows identification of these superior projects.iv ACKNOWLEDGEMENTS

Assessment of the Reliability of Reserves Estimates of Public Companies in the US and Canada

“…Unfortunately, humans are poor at assessing uncertainty, i.e., we are biased. Several authors have reported on the tendency for overconfidence and optimism in the petroleum industry (Capen 1976;Welsh et al 2005;McVay and Dossary 2014).…”

Probabilistic Production Forecasting and Reserves Estimation in Waterflooded Oil Reservoirs

“…According to McVay and Dossary (2014), even moderate overconfidence and optimism can lead to portfolio disappointment of more than 30%. It is also important to evaluate reserves and resources early for optimal development.…”

Incertidumbre de datos geoespaciales en la exploración petrolera

“…Otro factor de mayor relevancia ha sido: la falta de pruebas claras de que el incremento de la cuantificación de la incertidumbre y del análisis de decisiones, haya conducido a una mejora significativa en la toma de decisiones y en el rendimiento en la industria. Cabe resaltar, que la mayoría de los beneficios que se citan en los procesos de cuantificación realmente son de naturaleza cualitativa, motivo por el cual muchas empresas son reacias a adoptar estos procesos [6].…”

“…Basado en la fórmula de la media aritmética ponderada, tenemos: W = (I1 + I2 + I3) / (I1 x P + I2 x P + I3 x P) (6) dónde: I1 = Índice de Confiablidad de la Precisión de la Coordenadas Proyectadas. I2 = Índice de Confiablidad de la Georreferenciación.…”