Abstract：
The formation dip angle is an important characteristic parameter that reflects underground structures, and it is widely used in seismic exploration and geological interpretation. However, the routine dip angle calculation method suffers from the problems of artifact interference and insufficient accuracy, which restricts the development of fine exploration technology. More accurately obtaining the formation dip angle has become a general concern. To solve these problems, this paper proposes a method for calculating the formation dip angle using deep learning. The dip angle calculation is considered a regression problem. By establishing a database of synthetic seismic data and dip data tags, datadriven fitting of nonlinear functional relationships between the seismic data and dip angles are achieved, and intelligent dip calculations are realized. The method is verified using the synthetic sedimentary model and actual data and is compared with the mainstream dip angle calculation method in the industry. The results show that this method more realistically reflects the undulating characteristics of a structure with both high accuracy and anti-interference ability.
基金资助:This study is funded by the CNPC (China National Petroleum Corporation) Scientific Research and Technology Development Project (Grant No.2021DJ05).
作者简介: Feng Chao graduated from China University of Petroleum (East China) with a master's degree in 2015. Now he is an engineer engaged in the research of oil and gas geophysics and reservoir prediction technology in the Northwest Branch of China Petroleum Exploration and
Development Research Institute.
2024, Vol. 21 
