The construction of subsurface reservoir models is typically aided by the use of outcrops and modern analogue systems. We show how process-based models of depositional systems help to develop and substantiate reservoir architectural concepts. Process-based models can simulate assumptions relating to the physical processes influencing sedimentary deposition, accumulation and erosion on the resultant 3D sediment distribution. In this manner, a complete suite of analogue geometries can be produced by implementing different sets of boundary conditions based on hypotheses of depositional controls. Simulations are therefore not driven by a desired/defined outcome in the depositional patterns, but their application to date in reservoir modelling workflows has been limited because they cannot be conditioned to data such as well logs or seismic information.

In this study a reservoir modelling methodology is presented that addresses this problem using a two-step approach: process-based models producing 3D sediment distributions that are subsequently used to generate training images for multi-point geostatistics.

The approach has been tested on a dataset derived from a well-exposed outcrop from central Utah. The Ferron Sandstone Member includes a shallow-marine deltaic interval that has been digitally mapped using a high-resolution unmanned aerial vehicle (UAV) survey in 3D to produce a virtual outcrop (VO). The VO was used as the basis to build a semi-deterministic outcrop reference model (ORM) against which to compare the results of the combined process/multiple-point statistics (MPS) geostatistical realizations. Models were compared statically and dynamically through flow simulation.

When used with a dense well dataset, the MPS realizations struggle to account for the high levels of non-stationarity inherent in the depositional system that are captured in the process-based training image. When trends are extracted from the outcrop analogue and used to condition the simulation, the geologically realistic geometries and spatial relationships from the process-based models are directly imparted onto the modelling domain, whilst simultaneously allowing the facies models to be conditioned to subsurface data.

When sense-checked against preserved analogues, this approach reproduces more realistic architectures than traditional, more stochastic techniques.

地下油藏模型的构建通常借助露头和现代模拟系统来辅助。我们将展示基于过程的沉积系统模型如何帮助开发和证实储层构造概念。基于过程的模型可以模拟与影响沉积3D沉积物分布的沉积，堆积和侵蚀的物理过程有关的假设。以这种方式，通过基于沉积控制的假设实施不同组的边界条件，可以产生一套完整的模拟几何体。因此，模拟不受沉积模式中期望/确定结果的驱动，但是迄今为止它们在储层建模工作流程中的应用受到了限制，因为它们无法适应诸如测井或地震信息之类的数据。

在这项研究中，提出了一种采用两步法解决该问题的油藏建模方法：基于过程的模型产生3D沉积物分布，随后将其用于生成多点地统计学的训练图像。

该方法已经在来自犹他州中部一个暴晒的露头的数据集上进行了测试。Ferron砂岩成员包括一个浅海三角洲间隔，该间隔已使用高分辨率无人飞行器（UAV）进行了3D三维测量，并进行了数字映射，以生成虚拟露头（VO）。VO被用作构建半确定性露头参考模型（ORM）的基础，通过该模型可以比较组合的过程/多点统计（MPS）地统计实现的结果。通过流仿真对模型进行了静态和动态比较。

当与稠密的井数据集一起使用时，MPS实现难以解决沉积过程中固有的非平稳性的高水平问题，该非平稳性是在基于过程的训练图像中捕获的。当从露头类比中提取趋势并用于模拟条件时，将基于过程的模型的地质现实几何形状和空间关系直接赋予建模领域，同时允许将相模型适应于地下数据。

当对保留的类似物进行合理检查时，这种方法比传统的，随机性更高的方法重现了更为现实的体系结构。