We consider the challenging task of evaluating the commercial viability of hydrocarbon prospects based on limited information, and in limited time. We investigate purely data-driven approaches to predicting key reservoir parameters and obtain a negative result: the information that is typically available for prospect evaluation and is suitable for data-based methods, cannot be used for the required predictions. We can show however that the same information is sufficient to produce a limited list of potentially similar well-explored reservoirs (known as analogues) that can support the prospect evaluation work of human geoscientists. We base the proposal of analogues on similarity measures on the data available about prospects. Technically, the challenge is to define suitable similarity measures on categorical data like depositional environment or rock types. Existing data-based similarity measures for categorical data do not perform well, since they do not take geological domain knowledge into account. We propose two novel similarity measures that use domain knowledge in the form of hierarchies on categorical values. Comparative evaluation shows that the semantic-based similarity measures outperform the existing data-driven approaches and are effective in comparison to the human analogue selection.

我们认为，在有限的时间和有限的信息基础上，评估碳氢化合物前景的商业可行性具有挑战性。我们调查纯数据驱动的方法来预测关键储层参数并获得否定结果：通常可用于前景评估且适用于基于数据的方法的信息不能用于所需的预测。但是，我们可以证明，相同的信息足以产生有限列表的潜在相似，经过良好勘探的储层（称为类似物）），可以支持人类地球科学家的前景评估工作。我们根据有关前景的可用数据基于相似性度量提出类似物的建议。从技术上讲，挑战在于在分类数据（如沉积环境或岩石类型）上定义合适的相似性度量。现有的用于分类数据的基于数据的相似性度量不能很好地执行，因为它们没有考虑到地质领域的知识。我们提出了两种新颖的相似性度量，它们以分类值的层次结构形式使用领域知识。比较评估表明，基于语义的相似性度量优于现有的数据驱动方法，并且与人类类似物选择相比是有效的。