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

你有没有想过倒过来解决问题？也就是说，从答案中，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。其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，表征或描述地下“油藏”（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。从特定储层中可采出的石油和天然气的预期量与储层的大小和内部多孔介质的性质直接相关。因此，我们可以想象，了解储层的形状和大小，以及它的内部性质，对于整个储层管理过程的重要性。问题是没有直接进入储层岩石的通道。这意味着水库“图像”的构建不是一个微不足道的问题。事实上，它是一个反问题，如上所述。从数学上讲，我们将其描述为“病态问题”，因为有几个解决方案同样可行，但只有一个是正确的。

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.