Decision analysis related to petroleum field development and management phases with complex models can be time-consuming, especially in highly heterogeneous fields. Probabilistic approaches require a large number of simulation runs to cover all possible solutions, and this can be slow. In this study, we present a methodology to include high-dimensional spatial attributes (geostatistical realizations) in proxy modeling, based on support vector regression (SVR), for building risk curves with decreased run time. The proposed workflow accomplished the following: definition of uncertain inputs, such as porosity and horizontal and vertical permeabilities; selection of outputs from the simulator (cumulative oil, water and gas productions) to be mimicked by the proxies; sample inputs to generate scenarios for training and proxy building; and consistency check to evaluate if the proxy model is reliable to mimic simulator output. We then used proxy models to generate risk curves at the final forecast period (7,305 days) as an application. Using the SVR with high-dimension inputs, we show that the proxy was able to provide reliable results with 300 scenarios, which represent 35% less computational effort compared with using only a reservoir numerical simulator. As a result, we can use this proxy to perform a risk analysis with a high level of accuracy [mean absolute percentage error (MAPE) lower than 0.5%] to predict the production curves. In conclusion, we can use the SVR proxy model technique as an alternative to a reservoir simulator when spatial uncertainty attributes (geostatistical realization) are present.

与石油领域的开发和管理阶段有关的决策分析（具有复杂的模型）可能非常耗时，尤其是在高度异构的领域中。概率方法需要进行大量的模拟运行才能涵盖所有可能的解决方案，但这可能会很慢。在这项研究中，我们基于支持向量回归（SVR），提出了一种在代理模型中包含高维空间属性（地统计实现）的方法，以建立运行时间缩短的风险曲线。拟议的工作流程完成了以下工作：确定不确定性输入，例如孔隙度以及水平和垂直渗透率；从仿真器中选择要由代理模仿的输出（累积的石油，水和天然气生产）；输入样本以生成用于培训和代理构建的方案；和一致性检查，以评估代理模型对于模拟模拟器的输出是否可靠。然后，我们使用代理模型作为最终应用在最终预测期（7,305天）生成风险曲线。通过将SVR与高维输入配合使用，我们证明了该代理能够在300种情况下提供可靠的结果，与仅使用储层数值模拟器相比，其计算量减少了35％。因此，我们可以使用该代理执行具有较高准确性的风险分析[平均绝对百分比误差（MAPE）低于0.5％]，以预测生产曲线。总之，当存在空间不确定性属性（地统计学实现）时，我们可以使用SVR代理模型技术替代储层模拟器。