数据驱动方法和人工智能（AI）算法比基于物理的方法更有前景，前者主要来源是数据，这是每个现象的基本要素。这些算法从数据中学习并揭示看不见的模式。这项新技术对每秒产生大量数据的石油行业具有重要意义。由于石油和天然气行业正处于向油田数字化的过渡阶段，在不同的石油工程挑战中，集成数据驱动建模和机器学习（ML）算法的动力越来越大。ML已广泛应用于工业的不同领域。人们已开展大量的研究，探索AI 在该行业各个学科中的适用性。然而，这些研究缺乏两个主要特征，大多数研究要么不够实用，不适用于实际领域的挑战，要么仅限于特定问题，无法推广。必须注意数据本身及其分类和存储方式。尽管有大量来自不同学科的数据，但它们都被存储在部门的数据库中，消费者无法访问。为了从数据中获取尽可能多的信息，需要将数据存储在一个集中的数据库中，不同的应用程序可以从中方便地使用这些数据。

Data-driven approaches and AI algorithms are promising enough to be relied on even more than physics-based methods; their main feed is data which is the fundamental element of each phenomenon. These algorithms learn from data and unveil unseen patterns out of it. The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology. As the oil and gas industry is in the transition phase to oilfield digitization, there has been an increased drive to integrate data-driven modeling and machine learning algorithms in different petroleum engineering challenges. ML has been widely used in different areas of the industry. Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry; however, lack of two main features is noticeable. Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable. Attention must be given to data itself and the way it is classified and stored. Although there are sheer volumes of data coming from different disciplines, they reside in departmental silos and are not accessible by consumers. In order to derive as much insight as possible out of data, the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.