正演地层模拟技术在复杂油田储层模型中的应用

R. Adam, S. H. Ayub, H. N. Nguyen, R. Masoudi, T. S. Murugesu, Muhammad Hanif Haziq Mohammad, Fauzi Kadir, J. Margotta, Zuhar Zahir Tuan Harith, Alexander Kolupaev, Y. Zainudain, Dj Thomas
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引用次数: 0

摘要

本文的目的是通过将正演地层模拟(FSM)作为地质输入,证明另一种数值模拟方法的成功。该新方法在马来盆地的一个油田进行了应用,该油田的早期生产井显示出原油原产、相分布和储层连通性的高度不确定性。因此,在该地区开发了一种新的静态模型方法,为地下储层提供了新的见解,降低了未来油田资产的风险,并减轻了地下的不确定性。FSM提供的基于过程的模拟提供了岩性分布和垂直障碍的真实场景,从而实现了先进的地下表征。FSM过程建立了一种定量的方法来模拟a油田D和E砂从区域到油藏结构的沉积物分布。模拟运行的主要参数包括对沉积物来源的区域认识、原位有机沉积物生成、米兰科维奇旋回增强的全球海平面曲线和控制该地区沉降的主要长期过程。FSM预测结合了区域地震、岩心和测井数据,为静态模型提供了一个可靠的储层特征场景。研究结果详细介绍了高分辨率层序地层学,沉积体系和砂体随时间的显著变化。FSM的结果与A油田井数据集进行了一致性质量检查。在进行敏感性分析后,选择最匹配的模型进行后续的静态模型构建。在生成静态沉积和岩石类型模型时,将FSM的结果与地质统计随机反演(GSI)的结果进行比较,以获得远离井控的性质分布。FSM导向模型建立结果表明,A油田D储层砂质相对较好,横向连通性较好。然而,油田E砂是一个更复杂的储层,面积和垂直连通性有限。总体而言,D油藏的总STOIIP显著提高,而E油藏的STOIIP与现有模型相当。考虑到主要的不确定性和风险,以及对多个油田开发方案的评估,根据油田和井的动态(生产历史、MDT、DST等)进行了校准。通过对A油田及其周围环境进行深入的综合分析,FSM和GSI导出的综合静态模型与常规工作流程相比,更准确地反映了该地区的相分布。该方法被用作A油田开发优化的辅助工具,并从最近钻探的开发井的发现中证明,它增加了找到良好储层相的可能性。
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Enhancement of a Complex Field's Reservoir Model Through Novel Application of Forward Stratigraphic Modeling
The objective of this paper is to demonstrate the success of an alternative numerical modeling approach to build a static model by incorporating Forward Stratigraphic Modelling (FSM) as geological input. This new methodology was performed on a field in the Malay Basin where early production wells indicated the high uncertainty in oil-originally-in-place, facies distribution and reservoir connectivity. For this reason, a new approach was developed for a static model in the area that provides new insights of subsurface reservoirs, de-risking future field assets and mitigates the subsurface uncertainty. Process-based simulations as presented with FSM present realistic scenarios of lithology distribution and vertical barriers that enable advanced subsurface characterization. FSM process built a quantitative method that simulate sediment distribution from regional to reservoir architecture for A field D and E sands. The main parameters for simulation run include regional understanding of sediment sources, in-situ organic sediment production, global sea-level curve enhanced by Milankovitch cycles and main long-term processes that control the subsidence of the area. FSM prediction combined with regional seismic, cores and well log data have provided a robust scenario of reservoir characteristics for static model. The results of the study detailed high-resolution sequence stratigraphy, significant changes in the depositional system and sand accumulation through time. The results of FSM were quality-checked with the A field well dataset for consistency. After performance of sensitivity analysis, the best-matched model was chosen for subsequent static model building process. In generating static depo- and rock type models, the FSM result were compared with the Geostatistical Stochastic Inversion (GSI) for property distribution away from the well control. The result of FSM guided model building showed A field D reservoirs as relatively having better sand quality with good lateral connectivity. A field E sand however is a more complex reservoir with limited areal and vertical connectivity. Overall, the total STOIIP for D reservoirs improved significantly while E reservoirs are comparable with existing model. The dynamic modelling was calibrated to field and wells performance (production history, MDT, DST, etc.) taking into account main remaining uncertainties and risks and evaluation of multiple field development options. With thorough integrated analysis of A field and its surroundings, integrated FSM and GSI derived static model reflects accurate facies distribution of the area compared with conventional workflows. It was used as an aid for Field A development optimization and increased the probability to find good reservoir facies as proven from findings of recently drilled development wells.
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