Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

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Application of the dynamic transformer model with well logging data for formation porosity prediction

Sun,

Pang,

Zhang

et al. 2024

Physics of Fluids

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Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, the Dynamic Transformer model in machine learning was introduced in this study, aiming to improve the accuracy and generalization ability of logging porosity prediction. Dynamic Transformer is a deep learning model based on the self-attention mechanism. Compared with traditional sequence models, Dynamic Transformer has a better ability to process time series data and is able to focus on different parts of the input sequence in different locations, so as to better capture global information and long-term dependencies. This is a significant advantage for logging tasks with complex geological structures and time series data. In addition, the model introduces Dynamic Convolution Kernels to increase the model coupling, so that the model can better understand the dependencies between different positions in the input sequence. The introduction of this module aims to enhance the model's ability to model long-distance dependence in sequences, thereby improving its performance. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the Dynamic Transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the Dynamic Transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

show abstract

Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

Cited by 4 publications

References 32 publications

Deep learning-aided simultaneous missing well log prediction in multiple stratigraphic units: a case study from the Bhogpara oil field, Upper Assam, Northeast India

Deep learning-aided simultaneous missing well log prediction in multiple stratigraphic units: a case study from the Bhogpara oil field, Upper Assam, Northeast India

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Application of the dynamic transformer model with well logging data for formation porosity prediction

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