马来西亚近海蜿蜒河流环境的先进储层特征,三维建模研究

M. Mohamad, M. Rahman, Benayad Nourreddine, M. H. Yakup, M. F. Sedaralit
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摘要

油田ABC已经进行了全面的储层建模研究,但是在一些特定的砂储层沉积体系(如曲流河储层(点坝和裂缝展))的建模方面仍有挑战需要解决。目前的建模方法,特别是河流储层的建模方法,主要是由井控制的,并且在井控制之间和远离井控制的情况下,造成了基于地质统计(方差等)的横向变化的不确定性。此外,现有的建模方法是使用六到五阶(低阶)层次结构元素,该项目进一步将模型细化到三阶(高阶),从而能够捕获点坝的横向增加。为了提高对河流型储层构型的认识,发展了先进的河流工作流(AFW)。主要分为三个步骤:一是通过岩心分析和文献资料对河流储层沉积学特征进行详细研究;二是基于地震数据集的定性地球物理研究与解释。第三,将第一步和第二步整合成三维(3-D)油藏模型。AFW在ABC油田的应用,可以更好地表征储层非均质性,更准确地评估STOIIP,提高历史匹配质量指数(HMQI),增强对地下风险和不确定性的理解。这有助于优化未来的油田开发计划,如重新激活油井、注水和化学提高采收率(EOR)。AFW是一种强大的建模方法,可用于任何油藏建模平台(PETREL, CMG, RMS, TNAV),具有自动化工作流的多种实现能力。
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Advanced Reservoir Characterisation of Meandering Fluvial Environment, 3D Modelling Study Offshore Malaysia
Thorough reservoir modeling studies have been performed for field ABC, however there are still challenges to be addressed in modelling of some specific sand reservoir depositional systems i.e. meandering fluvial reservoirs (point bars and crevasse splays). The current modelling approaches especially for fluvial reservoirs are mainly controlled by wells and have contributed to uncertainties in lateral variation based on geostatistic (variograms etc) between and away from well control. Moreover, the existing modelling approach is using sixth to fifth order (lower order) hierarchical architecture elements and this project further refines the model up to third order (higher order) which enables capturing lateral accretion of point bars. Advanced fluvial workflow (AFW) have been developed to improve the understanding of the reservoir architecture of fluvial reservoirs. It comprises of three main steps which are, first, details study on fluvial reservoir sedimentology characteristics derived from core analysis and literature. Second, qualitative geophysical study and interpretation derived from seismic dataset. Third, integration between the first and second steps into a three dimensional (3-D) reservoir model. As a result of AFW implementation in field ABC, this has led to better representation of the reservoir heterogeneities, more accurate STOIIP assessment, improved history matching quality index (HMQI) and enhanced subsurface risks and uncertainties understanding. This enable optimization of future field development plan such as infill well reactivation, water flood and chemical enhanced oil recovery (EOR). The AFW is a robust modelling method that can be used in any reservoir modelling platform (PETREL, CMG, RMS, TNAV) with multiple realizations capability using automated workflows.
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