Compqual-Tgnet: A Novel Hybrid Temporal-Graph Neural Architecture for Analyzing Competency and Quality Metrics in Oil and Gas Operations

Shashank Sawant

doi:10.36948/ijfmr.2025.v07i01.36610

Compqual-Tgnet: A Novel Hybrid Temporal-Graph Neural Architecture for Analyzing Competency and Quality Metrics in Oil and Gas Operations

Author(s)	Shashank Sawant
Country	United States
Abstract	This research launches a novel Competency-Quality Temporal-Graph Network (CompQual-TGNet) which serves as a hybrid deep learning (DL) framework to study oil and gas business operations through competency metrics and quality indicators. The system combines Graph Neural Network (GNN) and Temporal Convolutional Network (TCN) technology to analyze multiple operational datasets and make accurate future performance estimates. The preprocessing system builds strong data connections by standardizing values with Z-score normalization while handling missing values via Multivariate Imputation by Chained Equations (MICE), finding outliers through Interquartile Range (IQR) and matching temporal data patterns across different inputs. The system also enhances outputs with advanced feature engineering methods including feature extraction and fusion. The proposed framework uses Multi-View Matrix Factorization (MVMF) to extract shared latent features, and Cross-Dataset Attention Mechanism (CDAM) to identify correlations. Moreover, Dynamic Feature Importance Network (DFIN) to learn optimal feature combinations across datasets, and Cross-Attention Fusion Layer to create context-aware feature representations are employed for fusion. Besides, this development brings together GNN for mapping interdependencies among operational components and personnel competencies and TCN for monitoring operational history and long-term temporal patterns. The adaptive fusion layer uses weighting and attention modules to unite these models to make clear predictions based on domain-relevant predictions. The proposed framework shows improvement through updating graphs during operations, adjusting loss functions to match target requirements, and transferring learned models for constant updating. Using these data sources, the developed CompQual-TGNet predicts operating efficiency and detects quality problems with 94.2% accuracy and outran existing systems.
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-07
Cite This	Compqual-Tgnet: A Novel Hybrid Temporal-Graph Neural Architecture for Analyzing Competency and Quality Metrics in Oil and Gas Operations - Shashank Sawant - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.36610
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.36610
Short DOI	https://doi.org/g84fbd

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Compqual-Tgnet: A Novel Hybrid Temporal-Graph Neural Architecture for Analyzing Competency and Quality Metrics in Oil and Gas Operations

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