Reservoir development forecasts depend on accurate descriptions of the spatial distribution of rock properties that impact subsurface fluid-flow pathways and volume connectivity. Reservoir models constructed using geostatistical methods combine analogous facies dimension data with sparse subsurface data to predict spatial variations in rock properties. This study uses a physics-based depositional process model to define realistic facies variations within a river-dominated delta deposit formed during multiple shoreline regressions and transgressions. Geostatistical models are conditioned to varying amounts of information extracted from the depositional model to examine how well they reproduce the facies patterns. Reservoir simulation is used to examine the impact of analogous dimension data and varying conditioning constraints on reservoir performance predictions of water displacing oil. The dimensions of surface depositional features underestimate the continuity of preserved facies patterns, proportional grids following major flooding surfaces allow significantly better predictions than uniform rectangular grids, and trend constraints are more important when defined facies correlation length is significantly less than well spacing. When geostatistical model parameters are poorly chosen, reservoir simulation of the resulting weakly-structured facies patterns overpredict recovery and water breakthrough time. It is demonstrated that process-based depositional models can be used to optimize geostatistical model construction methods and input parameters to reduce uncertainty of reservoir development assessments.

中文翻译：

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为地质统计模拟定义以河流为主导的三角洲矿床内的岩性模式。

储层开发预测取决于对影响地下流体流动路径和体积连通性的岩石特性的空间分布的准确描述。使用地质统计学方法构建的储层模型将类似的相维数据与稀疏的地下数据相结合，以预测岩石特性的空间变化。本研究使用基于物理学的沉积过程模型来定义在多次海岸线海退和海侵期间形成的以河流为主的三角洲沉积物内的真实相变化。地质统计模型以从沉积模型中提取的不同数量的信息为条件，以检查它们再现相模式的程度。油藏模拟用于检查类似维度数据和不同条件约束对水驱油的油藏性能预测的影响。地表沉积特征的尺寸低估了保存相模式的连续性，主要洪水面之后的比例网格比统一的矩形网格允许明显更好的预测，并且当定义的相相关长度显着小于井距时，趋势约束更为重要。当地质统计模型参数选择不当时，对由此产生的弱结构相模式的储层模拟会高估采收率和见水时间。