Ethereum smart contracts operate with hundreds of billions of dollars, promising algorithmic fairness and guaranteed execution. But in reality, the guaranteed execution promise is often unfulfilled. For instance, a smart contract might reject the withdrawal of previously invested funds due to resource over-consumption - the condition known as running out-of-gas (OOG). In this work, we challenge the common binary perception of the OOG condition as a vulnerability, showing instead that OOG is a risk spectrum. Furthermore, we develop an API called OGRISK (Out of Gas Risk Estimator) that quantifies the OOG risk for a given smart contract. OGRISK uses a novel machine learning approach that builds a feature vector from the histogram of inter-node relationships in an augmented abstract syntax tree (AAST). Using manual expert labeling, we train a supervised model based on three discrete risk levels. Next, we develop a risk score heuristic that represents the OOG risk prediction as a scalar risk score (R -score). We apply the developed heuristic to Ethereum Mainnet contracts in the wild and discover that: 1) approximately 97.8% of smart contracts with an R-score of at least 0.9 are indeed in danger of running out of gas; 2) approximately 1.31% of all open-source smart contracts deployed on the Ethereum Mainnet have an R-score of 0.9 or higher.