Formation lithology predictions based on measurement while drilling (MWD) using gradient boosting algorithms

Lee, H. J.; Lee, Hunjoo

doi:10.1016/j.geoen.2023.211917

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Traditional logging interpretation methods play an important role in the exploration and development stages, and with the development of machine learning and automation technology, these methods are gradually being combined with computer-aided interpretation to improve efficiency and accuracy. Lithology prediction methods based on logging data analysis have been an increasingly popular research topic in the oil and gas exploration field in recent years [12,13]. Researchers have aimed to improve the accuracy of predicting the rock type or lithology in blind wells without core data by combining artificial intelligence techniques with information from logging data.…”

Section: Introductionmentioning

confidence: 99%

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Xie,

Liu,

Wang

et al. 2024

Symmetry

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Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

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Section: Introductionmentioning

confidence: 99%