Haibin Di is a Senior Data Scientist in the Subsurface Data Science team at Schlumberger. His research interest is in implementation of machine learning algorithms particularly deep neural networks into multiple seismic applications, including stratigraphy interpretation, property estimation, denoising, and seismic-well tie. He has published more than 70 papers in seismic interpretation and is of 7 patents on machine learning-assisted subsurface data analysis. Haibin received his PhD degree in Geology from West Virginia University in 2016, worked as a postdoctoral researcher at Georgia Institute of Technology in 2016-2018, and joined Schlumberger in 2018.
Robust estimation of rock properties, such as porosity, density, etc., from geophysical data, i.e. seismic and well logs, is essential in the process of subsurface modeling and reservoir engineering workflows. We present a semi-supervised learning workflow for static reservoir property estimations from 3D seismic and sparse wells that are available in a given study area. The method consists of two steps: (1) unsupervised feature engineering on the 3D seismic and (2) supervised integration of seismic with well logs, each of which is implemented using convolutional neural networks (CNNs). Specifically, the first CNN aims at understanding the 3D seismic data in an unsupervised way and extracting the regional features present in the study area, while the second CNN aims at constructing the optimal non-linear mapping between the wells and the seismic patterns at the well locations. Both components are connected with embedding the first CNN into the second CNN, which enforces the use of regional seismic features while building the seismic-well mapping relationship and thus helps significantly reduce the risk of overfitting due to limited wells as well as improve the quality of machine prediction. Moreover, the proposed workflow allows incorporating any additional information (e.g., structure) as constraints, which is expected to further improve the machine learning prediction, particularly in the case when wells are limited. The proposed workflow is applied to multiple datasets for performance evaluation. The good match between the machine prediction and the well logs verifies the capability of the proposed workflow in providing reliable seismic and well integration and delivering reliable reservoir property models. it provides nearly one-click solution to obtain 3D rock property distribution from seismic and well data in a study area.