Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

Abstract

Three-dimensional (3D) models provide the most intuitive representation of geological conditions. Traditional modeling methods heavily depend on technicians’ expertise and lack ease of updating. In this study, we introduce a deep learning-based method for 3D geological implicit modeling, leveraging a substantial dataset of geological drilling data. By applying resampling and normalization techniques, we standardize drilling data and significantly expand the dataset, making it suitable for training deep neural networks. Utilizing the characteristics of the sample data, we design and establish the network structure, loss function, and parameter configurations, resulting in the training of a deep neural network with high accuracy and robust generalization capability. Ultimately, we utilize the dataset generated from the network’s predictions to render and construct the 3D geological model. The research in this paper demonstrates the significant promise of deep neural networks in addressing geological challenges. The deep learning-based implicit 3D modeling method surpasses traditional approaches in terms of generalization, convenience, and adaptability.

Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru

“…Accuracy Deep Neural Network [3] 84.4 Naï ve Bayes [41] 54.0 Support Vector Machines [41] 65.0 Random Forest [41] 76.0 Multilayer Perceptron Artificial Neural Network 66.0…”

“…In this context, categorical geological models stand out for their importance by directly correlating with metallurgical processes and determining plant dispatch strategies in mine planning [2]. Although various neural network techniques, such as deep neural networks [3] and graphical networks [4], have been explored in this field, the application of Multi-Layer Perceptron Artificial Neural Networks (ANN-MLP) emerges as a significant innovation. The ANN-MLP [5] is distinguished by its ability to efficiently process and classify large volumes of geological data, surpassing conventional methods and playing a crucial role in optimizing mining planning and operation.…”

Development of Software for 3D Well Visualization Modeling Using Acoustic, Gamma, Neutron and Density Logging for Fossil Energy Sources Sustainable Production

“…The first group includes an analysis of the possibilities for integrating logging data from various sources [20], including for the 3D modeling of hydrogeological objects [21], using neural networks for these purposes [22], the application of the finite element method [23], integration of logging and seismic monitoring data in reservoir structure modeling [24].…”