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

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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.