Geological Modeling of the Terminal Fan Reservoir in Hai120 Block of Jilin Oilfield

2010 ◽  
Author(s):  
Jinliang Zhang ◽  
Fang Ding ◽  
Jinkai Wang
Keyword(s):  
Geological Modeling ◽  
Terminal Fan

3D Geological Modeling-While-Drilling for Geosteering

2005 ◽  
Author(s):  
N.F. Najjar ◽  
T. Jerome ◽  
M. Alshammery
Keyword(s):  
Geological Modeling ◽  
3D Geological Modeling

scholarly journals Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1819
Author(s):  
Tiandong Shi ◽  
Deyun Zhong ◽  
Liguan Wang
Keyword(s):  
Point Cloud ◽  
Minimum Spanning Tree ◽  
Point Clouds ◽  
Original Method ◽  
Modeling Method ◽  
Geological Modeling ◽  
Improved Method ◽  
Normal Estimation ◽  
Sampling Points ◽  
Correct Point

The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)

2021 ◽  
pp. 104754
Author(s):  
Ran Jia ◽  
Yikai Lv ◽  
Gongwen Wang ◽  
EmmanuelJohnM. Carranza ◽  
Yongqing Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geological Modeling ◽  
3D Geological Modeling
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