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。要找到反问题的正确解，策略是考虑尽可能多的数据。对于储层描述问题，可以考虑两类数据：静态数据（如岩心、测井和地震数据）和动态数据（如瞬态压力、饱和度和流量）。目前，大多数可用的技术只允许我们使用有限来源的数据。因此，储层物性的不确定性高于其可承受的范围，无法优化储层管理。该研究计划的目标是开发高效的计算技术来生成储层模型，该模型使用静态和动态生产数据。更有效的油藏描述方法的成功开发将为石油运营商提供改进的油藏管理技术，从而提高油气产量，提高作业效率、成本和环境安全性。

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.To find the correct solution to an inverse problem, the strategy is to consider as much data as possible. For the reservoir description problem two classes of data can then be considered: static data (such as core, log, and seismic data) and the dynamic data (such as transient pressures, saturations and flow rates). At present, the majority of the available techniques only allow us to use data from limited sources. As a result, the uncertainty in reservoir properties is higher than it could be, and reservoir management cannot be optimized. The objective of the proposed research program is to develop computationally efficient techniques for generating reservoir models, which uses both static and dynamic production data. Successful development of more efficient reservoir description methodologies will provide oil operators with improved reservoir management techniques that can lead to increased oil and gas production and more efficient, less costly and environmentally safer operation.