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A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada
Petroleum Exploration and Development ( IF 7.0 ) Pub Date : 2021-04-23 , DOI: 10.1016/s1876-3804(21)60032-0
Lixin WANG , Yanshu YIN , Hui WANG , Changmin ZHANG , Wenjie FENG , Zhenkun LIU , Pangen WANG , Lifang CHENG , Jiong LIU

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies; the 2D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3D multi-point pattern probabilities at the unknown points, to realize the reconstruction of a 3D model from 2D cross-section. To solve the problems of reducing pattern variability in the 2D training image and increasing sampling uncertainty, an adaptive spatial sampling method is introduced, and an iterative simulation strategy is adopted, in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability. The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics, and accurately reflect the morphology and distribution pattern of the lateral accretion layer. Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape, spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect. The test by sparse wells shows that the simulation accuracy is above 85%, and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.



中文翻译:

基于自适应空间采样的2D地质剖面重建3D模型的方法:以加拿大某地区的白垩纪McMurray油藏为例

根据测井和沉积相的地质分析结果,构建正交二维训练图像。通过线性池化方法得到三个方向的二维概率,然后通过对数线性池化求和,确定未知点处的3D多点图案概率,从而实现从2D截面重建3D模型。为了解决减少二维训练图像中的图案变化和增加采样不确定性的问题,提出了一种自适应空间采样方法,并采用了一种迭代仿真策略,在该策略中,来自先前仿真结果的具有较高可靠性的区域的采样点为在下面的仿真中将其提取为附加条件点,以提高模式概率采样的稳定性。侧向吸积层概念模型的比较表明,采用自适应空间采样的重构算法可以提高模式采样的准确性和空间结构特征的合理性,并准确反映侧向吸积层的形态和分布格局。该方法在加拿大白垩纪麦克默里组曲流河水库重建中的应用表明,该新方法可以在潮汐作用下准确地再现曲流河水库中复杂的横向增生层的形状,空间分布格局和发育特征。疏井试验表明,模拟精度在85%以上,新钻水平井解释与预测结果的符合率高达80%。

更新日期:2021-04-23
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