Robust data-driven process for improved description and optimal management of oil and gas reservoirs

你有没有想过倒着解决问题？也就是说，从答案上看，试图发现问题？在一般意义上，这是这里描述的研究计划的主要目标。它的最终目标是开发计算效率高的技术，利用地面上所有可用的信息来表征或描述地下“储油层”(即自然储存石油和天然气流体的多孔地下岩石)的结构和性质。从一个特定的储集层中可以回收的石油和天然气的预期数量，直接与其大小和内部多孔介质的性质有关。因此，我们可以想象，了解油藏的形状和大小以及其内部特性对于整个油藏管理过程是多么重要。问题是，没有直接进入储集岩的通道。这意味着，建设水库的这一“形象”不是一个小问题。事实上，如上所述，这是一个逆问题。从数学上讲，我们将其描述为“不适定问题”，因为有几种解决方案同样可以实现，但只有一种是正确的。

Have you ever thought of solving a problem backwards? That is, from the answer, tried to discover the question? In a general sense this is the main objective of the research program described here. Its final goal is to develop computationally efficient techniques that use all available information at the ground surface, to characterize or describe the structure and properties of an underground 'oil reservoir' (that is, porous subsurface rock in which oil and gas fluids are naturally stored). The expected amount of oil and gas that can be recovered from a particular reservoir, is directly related to its size and internal porous media properties. Thus, we can imagine the tremendous importance of knowing the reservoir shape and size, as well as its internal properties, for the overall reservoir management process. The problem is that there is no direct access to the reservoir rock. This means that the construction of this "image" of the reservoir is not a trivial problem. It is, in fact, an inverse problem, as mentioned above. Mathematically speaking, we describe this as an 'ill posed problem', since several solutions are equally achievable, but only one is correct.