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.

碳氢化合物（再开发）项目需要在不确定的情况下进行评估。预测油气产量需要捕获众多不确定参数的范围及其对预测的影响，以使公司的项目价值最大化。但是，一些作者表明，石油和天然气行业在充分评估碳氢化合物产量预测的分布方面面临挑战。

预测碳氢化合物产量的方法是通过数字化发展的，从使用解析解决方案到具有越来越多的网格块（“数字双胞胎”）的数值模型，到涵盖各种参数不确定性的整体模型。分析解决方案和单个数值模型允许计算单个案例的增量产量。但是，既无法确定预测的不确定性，也无法确定单个模型所位于的各种结果的分布的问题。基于集合的预测能够解决这些问题，但是它们需要能够涵盖大量不确定的参数以及相应生成的数据量。

理论指导的数据科学（TGDS）方法最近已被用来克服这些挑战。这样的方法利用在数值模型中捕获的科学知识来生成足够大的数据集以应用数据科学方法。这些方法可以与经济学相结合来确定公司对项目的期望（期望的效用）。可以进行定量决策分析，包括信息价值（VoI）计算，以解决不确定性范围，也可以解决决策者（DM）要求的风险障碍。下一步是开发学习代理系统（代理：自治，

公司需要应对数据民主化的挑战，以集成和使用可用数据，组织敏捷性以及数据科学技能的发展，但要确保保留TGDS方法所需的技术技能。