Reservoir engineers use large-scale numerical models to predict the production performance in oil and gas fields. However, these models are constructed based on scarce and often inaccurate data, making their predictions highly uncertain. On the other hand, measurements of pressure and flow rates are constantly collected during the operation of the field. The assimilation of these data into the reservoir models (history matching) helps to mitigate uncertainty and improve their predictive capacity. History matching is a nonlinear inverse problem, which is typically handled using optimization and Monte Carlo methods. In practice, however, generating a set of properly history-matched models that preserve the geological realism is very challenging, especially in cases with intricate prior description, such as models with fractures and complex facies distributions. Recently, a new data-space inversion (DSI) approach was introduced in the literature as an alternative to the model-space inversion used in history matching. The essential idea is to update directly the predictions from a prior ensemble of models to account for the observed production history without updating the corresponding models. The present paper introduces a DSI implementation based on the use of an iterative ensemble smoother and demonstrates with examples that the new implementation is computationally faster and more robust than the earlier method based on principal component analysis and gradient-driven optimization. The new DSI is also applied to estimate the production forecast in a real field with long production history and a large number of wells. For this field problem, the new DSI obtained forecasts comparable with a more traditional ensemble-based history matching.

中文翻译：

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整体平滑的数据空间反转

储层工程师使用大型数值模型来预测油气田的生产性能。但是，这些模型是基于稀缺且通常不准确的数据构建的，因此其预测高度不确定。另一方面，在田间作业期间不断收集压力和流速的测量值。将这些数据同化到储层模型中（历史匹配）有助于减轻不确定性并提高其预测能力。历史匹配是一个非线性逆问题，通常使用优化和蒙特卡洛方法进行处理。但是在实践中，要生成一组能够正确保存历史现实性的，与历史相匹配的模型是非常困难的，尤其是在具有复杂的先验描述的情况下，例如具有裂缝和复杂相分布的模型。最近，文献中引入了一种新的数据空间反演（DSI）方法，以替代历史匹配中使用的模型空间反演。基本思想是直接更新先前模型的组合中的预测，以说明观察到的生产历史，而无需更新相应的模型。本文介绍了基于迭代整体平滑器的DSI实现，并通过示例演示了该新实现比基于主成分分析和梯度驱动优化的早期方法在计算上更快，更可靠。新的DSI也可用于估算具有悠久生产历史和大量油井的真实油田的产量预测。