Fluvial sandstones deposited by high-sinuosity fluvial systems are one of the most complex reservoirs to predict and model with confidence, a reflection of both the geometries and complex distribution of the component geobodies. By integrating both analogue outcrop data and associated subsurface data, as well as new technical advances in the reconstruction of the outcrop in 3D (Digital Outcrop Models, DOM), the geostatistical parameters, which condition the modelling of these reservoirs, can be better determined. In addition, DOMs also allow us to easily extract the necessary georeferenced input data (digitized outcrop interpretations, geometrical parameters, as well as key surfaces) and so create geocellular outcrop models (GOM); a useful tool with which to contrast the results obtained from geostatistical simulations, as well as to quantify the uncertainty associated with the results.

In this study, classical field data, digital data derived from outcrop models and subsurface data were combined in order to carry out a geostatistical modelling of a Channel – Crevasse-splay complex outcrop analogue, located in the Triassic Red Beds of Iberian Meseta (TIBEM). Geostatistical modelling results were obtained by combining Object-based (OBM) and MultiPoint Statistics-based (MPS) modelling techniques.

A critical element in this study was the design of appropriate modelling workflows with Petrel® which would best reproduce the distribution of heterogeneities at macroscale. The designed modelling workflow was used to construct a 3D Training Image (TI) of a fluvial reservoir comprising both a meandering channel system and its associated overbank sandstone deposits. The resulting TI represents all geobodies described in the studied outcrop example and is exportable to similar fluvial reservoirs. This TI was then used in MPS simulations, in order to establish how it could assist in the prediction of the reservoir geobodies, as well as confirming to what extent this prediction matched the outcrop.

高柔度河流系统沉积的河流砂岩是最有把握地进行预测和建模的储层之一，反映了组分地质体的几何形状和复杂分布。通过集成模拟露头数据和相关的地下数据以及3D露头重建的新技术进步（数字露头模型，DOM），可以更好地确定调节这些油藏建模的地统计参数。此外，DOM还使我们能够轻松提取必要的地理参考输入数据（数字化露头解释，几何参数以及关键面），从而创建地细胞露头模型（GOM）；一个有用的工具，可用来对比从地统计模拟中获得的结果，

在这项研究中，结合了经典的野外数据，从露头模型获得的数字数据和地下数据，以便对位于伊比利亚梅西塔三叠纪红层（TIBEM）的海沟-裂隙-露头复杂露头类似物进行地统计学建模。 。地统计建模结果是通过结合基于对象的（OBM）和基于多点统计的（MPS）建模技术获得的。

这项研究的关键要素是使用Petrel®设计适当的建模工作流程，这将最好地再现宏观上的异质性分布。设计的建模工作流程用于构造河流水库的3D训练图像（TI），该水库包括曲折河道系统及其相关的滩涂砂岩矿床。生成的TI代表了所研究露头示例中描述的所有地体，并且可以输出到类似的河流储层。然后将此TI用于MPS模拟中，以便确定它如何协助储层地质体的预测，并确认该预测在多大程度上与露头相匹配。