Breaking the Chain, Novel Use of Adaptive Model Approach for Reservoir Modelling Reduces Time Duration of Project and Improves Collaboration

A. R. Thompson, C. E. Z. Bolivar
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Abstract

This paper details how the reservoir modelling workflow can be accelerated, and uncertainty reduced even for challenging Green Field prospects by constructing multiple small fit for purpose integrated adaptive models instead of just one all-encompassing model. Adaptive models are models built with a single purpose as compared to more conventional reservoir models which are designed to be flexible and be used for multiple purposes. By only having a single task, these adaptive models can be built more quickly and since they are used as tool, not the product, they do not need to be reviewed. Multiple different adaptive models are built to handle different questions ranging from production data QC, different facies models going through to local history matches. As the study progresses, these adaptive models can be combined to consider multiple components and their influence on each other to ensure compatibility and to possibly feed information to the modeling effort for the full field model. As the project finalizes, these adaptive models work like jigsaw pieces to come together to create the finalized picture/model. In the application of adaptive models within an integrated reservoir study, the most obvious benefit is being able to complete the static and dynamic aspects of the model building in parallel and subsequently be able to move more quickly to the deployment of the model for placing wells. Another key benefit was that it was possible to evaluate and defend new concepts or methods in reservoir modeling. Integrating new components into an existing modeling doctrine is challenging as significant time has to be expended and this has to be justified in advance of knowing the impact of the new item. With adaptive models, it is possible to evaluate out of doctrine ideas in parallel and off critical path. If the results are positive, the adaptive model is available to justify the benefit of the change and to also act as a comparison to the result following the doctrine. This innovative workflow enables more efficient working by reducing the number of items on the critical path while simultaneously improving integration by enabling static and dynamic data to be integrated before the static model is finalized. In addition, this approach is a driver for innovation as it enables new ideas to be evaluated more rapidly and without having to impact the critical path of the project.
打破链条,将自适应模型方法用于储层建模的新颖做法缩短了项目时间并改善了合作关系
本文详细介绍了如何通过构建多个小型综合适应性模型,而不是一个包罗万象的模型,来加快储层建模工作流程,并减少不确定性,即使是对具有挑战性的绿油田前景也是如此。自适应模型是以单一目的建立的模型,与之相比,传统的油藏模型设计灵活,可用于多种目的。由于只有单一的任务,这些自适应模型可以更快地建立起来,而且由于它们是作为工具而不是产品使用,因此不需要对其进行审核。建立多个不同的自适应模型来处理不同的问题,从生产数据质量控制、不同的地层模型到局部历史匹配。随着研究的深入,可以将这些自适应模型结合起来,考虑多个组成部分及其相互影响,以确保兼容性,并可能将信息反馈给完整的现场模型建模工作。随着项目的最终完成,这些自适应模型就会像拼图一样拼接在一起,形成最终的图景/模型。在综合油藏研究中应用自适应模型,最明显的好处是能够同时完成模型构建的静态和动态部分,随后能够更快地部署模型,进行油井定位。另一个主要好处是可以对油藏建模的新概念或新方法进行评估和辩护。将新的组成部分整合到现有的建模理论中具有挑战性,因为必须花费大量的时间,而且必须在了解新项目的影响之前进行论证。有了适应性模型,就有可能在平行和非关键路径上对理论之外的想法进行评估。如果评估结果是积极的,适应性模型就能证明变革的好处,并与遵循理论的结果进行比较。这种创新的工作流程可以减少关键路径上的项目数量,从而提高工作效率,同时还可以在静态模型最终确定之前整合静态和动态数据,从而改进整合工作。此外,这种方法还能推动创新,因为它能更快地评估新想法,而不必影响项目的关键路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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