利用大量地下数据和相关的不确定性来构建高质量的3D结构模型

S. Aidarbayev, Mohamed Kamel Ouldamer, Guillaume Masson, J. Codo
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引用次数: 0

摘要

在棕地开发阶段,处理的数据种类繁多,数量庞大,很难将所有数据以一致的方式整合起来,从而建立一个基本的结构模型。与许多其他油田一样,该油田由几个具有许多断层的叠层油藏组成,并有近千口井的垂直、倾斜和水平轨迹。最重要的是,许多水平井瞄准的是薄碳酸盐层,这些层的数据点间隔很紧,往往导致观测结果相互矛盾。因此,水平井和斜井通常从结构建模中被丢弃,留下大量有价值的信息未被使用。其中一些井可以通过引入伪井来间接计算,这使得建模工作流程繁琐,依赖于用户,因此难以重复。“近似正确比完全错误要好”,这是18世纪Carveth Read的名言。因此,每一项物理测量,即使是最现代和最精密的工具,也会受到一些不确定性的影响。因此,评估与每个输入数据相关的不确定性在这种方法中是至关重要的。地质学家、地球物理学家和钻井专家之间的综合团队合作,可以对每个数据进行彻底的分析,为结构模型构建过程提供支持,同时提供最佳的不确定性估计。该范围指定为~ 1000个井眼轨迹、~ 16000个地质标志、3个地震走时图、3个层速图和59个厚度图。以一致的方式使用所有可用数据,以尽量减少深度不确定性。通过将多层模型中的所有表面连接在一起,进一步提高了精度。此外,该方法既考虑了捕捉构造趋势的大尺度空间连续性,也考虑了由沉积学控制的井间厚度变化。在采用这种方法之后,所有地下数据开始趋于一致,并产生了更多的地质结构。例如,图1显示了在B4目标层平均厚度为6英尺的井的横截面。如左图所示,使用直井和一些伪井的传统工作流程导致了异常的上拉结构和整体波浪状的非地质几何形状。此外,根据区域测井解释,该井虽处于储层中,但仍处于非储层致密层。然而,右图显示,考虑水平井和不确定性有助于整合所有地下数据,提高一致性,使结构模型更平滑,更具地质意义,并且井位于目标储层中。该方法将使该油田成为第一个以一致的方式合并所有数据而不需要伪井来建立三维结构模型的棕地之一。它将为减少后续属性和动态建模阶段的不确定性带来相当大的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging Large Subsurface Data and Associated Uncertainties to Build High Quality 3D Structural Model
At brownfield development stage, dealing with diverse and large amount of data makes it challenging to integrate them all in a consistent manner to build a prime structural model. Like many others, the studied field consists of several-stacked reservoirs featuring many faults and close to a thousand drilled wells with vertical, slanted and horizontal trajectories. On top of that, many horizontal wells are targeting thin carbonate layers for which tightly spaced data points often result in conflicting observations. Consequently, horizontal and deviated wells are commonly discarded from structural modelling, leaving substantial and valuable information unused. Some of these wells may be indirectly accounted through the introduction of pseudo-wells, making the modelling workflow tedious, user-dependent and therefore difficult to repeat. ’It's better to be approximately right than exactly wrong’ quoted by Carveth Read, 18th century. Accordingly, every physical measurement, even from the most modern and sophisticated tools, is subject to some uncertainty. Therefore, assessing the uncertainty related to each input data is paramount in this method. Integrated teamwork between geologists, geophysicists and drilling specialists lead to a thorough analysis of each data feeding the process of structural model building while providing best uncertainty estimates. The ranges were specified for ∼1000 well trajectories, ∼16000 geological markers, 3 seismic travel time maps, 3 interval velocities and 59 thickness maps. All available data are used in a consistent manner to minimize the depth uncertainty. The accuracy is further improved by linking together all surfaces in a multi-layered model. In addition, this methodology considers both large scale spatial continuity capturing structural trends and more local scale incorporating inter-well variations of thickness due to sedimentological controls. After following this approach, all subsurface data started to come in agreement and resulted in more geological architectures. As an example, Figure 1 shows a cross-section along a well that drilled in B4 target layer which average thickness of 6 ft. As illustrated in the left figure, classical workflow using vertical wells and some pseudo-wells resulted in an anomalous pull-up structure and overall wavy non-geological geometry. Moreover, the well shows that it is in non-reservoir dense layer even though the well in the reservoir based on the zone log interpretation. However, the right figure shows that considering horizontal wells and uncertainties help to integrate all subsurface data with improved consistency where the structure model is smoother & more geological, plus the well is correctly placed in the targeted reservoir. This approach will make the studied field one of the first brownfields that incorporated all data in consistent manner without pseudo-wells to build 3D structural model. It will bring considerable value to reduce uncertainties during subsequent property and dynamic modelling stages.
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