Hydrocarbon (re‐)development projects need to be evaluated under uncertainty. Forecasting oil and gas production needs to capture the ranges of the multitude of uncertain parameters and their impact on the forecast to maximize the value of the project for the company. Several authors showed, however, that the oil and gas industry has challenges in adequately assessing the distributions of hydrocarbon production forecasts.

The methods for forecasting hydrocarbon production developed with digitalization from using analytical solutions to numerical models with an increasing number of gridblocks (“digital twins”) toward ensembles of models covering the uncertainty of the various parameters. Analytical solutions and single numerical models allow calculation of incremental production for a single case. However, neither the uncertainty of the forecasts nor the question in which the distribution of various outcomes the single model is located can be determined. Ensemble‐based forecasts are able to address these questions, but they need to be able to cover a large number of uncertain parameters and the amount of data that is generated accordingly.

Theory‐guided data science (TGDS) approaches have recently been used to overcome these challenges. Such approaches make use of the scientific knowledge captured in numerical models to generate a sufficiently large data set to apply data science approaches. These approaches can be combined with economics to determine the desirability of a project for a company (expected utility). Quantitative decision analysis, including a value of information (VoI) calculation, can be done addressing the uncertainty range but also the risk hurdles as required by the decision‐maker (DM). The next step is the development of learning agent systems (agent: autonomous, goal‐directed entity that observes and acts upon an environment) that are able to cope with the large amount of data generated by sensors and to use them for conditioning models to data and use the data in decision analysis.

Companies need to address the challenges of data democratization to integrate and use the available data, organizational agility, and the development of data science skills but making sure that the technical skills, which are required for the TGDS approach, are kept.

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