Dependencies and interdependencies between major risks are becoming a critical issue for large engineering projects. Current project risk analysis and management (PRAM) process does not focus effectively on relationships between risks, even though risks are often not independent in complex projects. As a consequence, we develop a risk dependency analysis (RDA) procedure to support PRAM. It entails three phases: Dependency Identification, Dependency Assessment, and Dependency Quantification. The aim of Dependency Identification is to describe risk dependencies and interdependencies through a design structure matrix (DSM) and gather them in clusters. Dependency Assessment is an unconventional probabilistic approach, which considers risk dependencies as events with their own probabilities, while Dependency Quantification estimates the impact of risk dependencies on project performance. The application of RDA to the Oil & Gas Industry uses a Monte Carlo simulation model, which aims at estimating schedule delays and capital expenditure (CAPEX) overruns.

The exploration and production of hydrocarbons is undeniably a high-risk venture. Uncertainties are in every activity, from the exploration phase up to facilities construction and plants operation. To allow decision makers understanding the uncertainty associated with the decisions they were asked to face, in the 70ies of the past century, when the oil shockwaves upset the entire business world (Bood & Postma, 1997), it was introduced the use of scenario analysis. Since then, stochastic analyses, such as Monte Carlo simulations, started permeating the oil & gas world. Yet, as Lewis et al. (2004) highlighted, despite the benefit greatly outweighs the effort generally required to perform these analyses, managers not seldom refrained themselves from using these tools to support their decision-making processes. And this, as highlighted by Judah (2016), despite risk mitigation is the very core of the oil & gas business as risks at stake are many, namely: drilling risk, subsurface risk, cost & schedule risk, procurement risk, performance risk, health, safety, and environment (HSE) risk, geopolitical risk, and price risk. Today, the energy transition is imposing new challenges to the oil & gas sector. Even amongst those companies that have embraced (quantitative) structured approaches to support their investment decisions, there is another challenge to face: the integration of all risks in one approach capable of capturing the overall complexity of oil & gas projects and highlighting the effects of variations of both endogenous and exogenous risks on the overall profitability of the opportunity at stake (or the portfolio of opportunities). Risks are typically assessed separately by each function with different quantitative approaches that leave grey zones of interpretation where inter-functional risks can grow and create decisional inefficiencies. Aware of this, Eni integrated risk management, started last year a project aimed at enhancing the project risk management process. The innovative approach followed leverages on artificial logic and allows to consider the full life cycle of an investment proposal. In line with the evolution of company's mission and vision, the aim was to assess the project risk in a more comprehensive way by considering endogenous risks (such as permits, drilling, engineering, procurement, construction) jointly with exogenous ones (such as climate change, cyber security, credit, and country). The manuscript explains the methodology used, the results achieved, and the expected benefit for the company in adopting the innovative approach to risk engineering in supporting investments decisions.