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Keywords: neural network
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Proceedings Papers
Dynamic Response Modeling in Underground Hydrogen Storage Using a Fourier-Integrated Hybrid Neural Framework
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24670-MS
... Abstract Underground hydrogen storage (UHS) is crucial for balancing renewable energy fluctuations, but modeling its dynamic injection and withdrawal cycles introduces sharp fronts and complex behaviors. Traditional neural networks when modeling an underground hydrogen storage operation...
Proceedings Papers
Monetising the Forgotten Oil With Machine Learning Assisted Rock Typing In A Giant Brown Field
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24747-MS
... Abstract An unsupervised clustering for rock typing definition coupled with a feedforward deep learning neural network have been developed in-house for a giant brown field. This field under study has thick reservoir sediment deposition amounting to 7000 ft with more than 200 reservoir units...
Proceedings Papers
Trapped and Movable CO 2 in Geologic Carbon Storage: Deep-Learning Forecasting and Generalization Study
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24804-MS
... on the inference dataset consistently achieved an R 2 of above 0.96, indicating strong generalization capability in predicting complex combinations of geological parameters. deep learning neural network geology co 2 artificial intelligence machine learning climate change scenario saline aquifer...
Proceedings Papers
A New Depth Prediction Technique Based on Particle Swarm Optimization Algorithm with Multiple Main Controlling Factors Constraints
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24820-MS
... by structural trends is established using the Convolutional Neural Network algorithm. Thereafter, the relationships between various principal controlling factors and depth are analyzed. Employing the Particle Swarm Optimization algorithm, which adopts a global optimization scheme to circumvent the entrapment...
Proceedings Papers
Efficient Surrogate Modeling for Subsurface Flow Simulation Using Multi-Fidelity Data with Physical Constraints
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24822-MS
... data and the lack of physical support in surrogate models, providing reliable support for complex reservoir development decisions. deep learning geologist neural network modeling & simulation geology machine learning artificial intelligence physical loss label loss training process...
Proceedings Papers
Bayesian Neural Network Method for Intelligent Drilling Pattern Discovery using Block Geological Information as Prior Knowledge
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24923-MS
... on the geological data within each window, using the Bayesian Information Criterion to assess the distribution of geological attributes within these windows. This distribution is incorporated as geological prior knowledge into a Bayesian neural network (BNN) model, where an online update model for ROP prediction...
Proceedings Papers
Full Borehole Image Data Computation Using GAN Based Model
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24957-EA
.... As a result, the encoder in this work also demonstrates its ability to be an efficient backbone model for extracting significant information from borehole image data. neural network image data deep learning geologist artificial intelligence machine learning borehole image data geology...
Proceedings Papers
Combining Variation Diffusion Model and Multi-Source Experimental Data to Establish Digital Cores for Reservoir Exploration and Development
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24845-MS
... reservoir characteristics, optimizing exploration and development strategies, and enhancing resource utilization efficiency in the oil and gas industry. digital core geologist neural network geology artificial intelligence flow in porous media core sample reconstruction noise pore structure...
Proceedings Papers
Fine Geological Modeling of Fractured Reservoir in Dual-Medium Buried Hill Based on BP Neural Network and Horizontal Well Parameter Correction
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24858-MS
... characteristics and the poor fitting of water cut in some wells affect the prediction accuracy of remaining oil and the adjustment and exploitation potential of the oilfield. To solve the above problems, a set of fine geological modeling method of dual-medium buried hill reservoir based on BP neural network...
Proceedings Papers
Fast AI Fault Prediction Using Sparsely Interpreted Labels
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24864-EA
... artificial intelligence neural network machine learning deep learning reservoir characterization interpretation prediction network model synthetic training data international petroleum technology conference fault pattern fault probability perpendicular prediction result fault interpretation...
Proceedings Papers
Abnormal Events Detection During Drilling Based on Knowledge Embedded Real Time Data Analysis
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24870-MS
... artificial intelligence real time system abnormal event algorithm drilling fluid management & disposal incident operation drilling operation neural network well control detection stand pipe pressure drilling fluids and materials machine learning indicator exhibition detection time outlet...
Proceedings Papers
A Novel Approach for the Prediction of Real-Time Rate of Penetration in Drilling for Petroleum by Combining the Attention-Based Bidirectional-Long Short-Term Memory and Long Short-Term Memory
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24898-MS
... to the prediction of real-time rate of penetration. The results are expected to provide guidance for the further study on the increase of drilling speed and reduction of well costs. geologist neural network deep learning drilling operation geology drilling fluids and materials drilling fluid management...
Proceedings Papers
Fluid-Modulus Inversion for Gas Prediction in the Deep-Water Area of Northern South China Sea
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25013-EA
... using the pre-stack elastic parameters inversion technique, and the elastic modulus K is used as a sensitive parameter for gas prediction. On this basis, neural network is used to find the inverse law of the above rock physical laws acting on the elastic parameters of pre-stack inverse bodies. Thus...
Proceedings Papers
Enhance the Vertical Resolution of Conventional Well Logs Using Auto-Encoder
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25025-EA
...-encoder is a generative artificial neural network capable of learning latent representations of the input data without supervision. First, we construct a suitable filter based on the tool response function of the low-resolution target log and apply this filter to the high-resolution source log...
Proceedings Papers
Multiplex Visibility Graphs-Based Hybrid Deep Learning Method for Recognizing Pipeline Operation Conditions Using Operating Data
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25065-MS
... of pipeline operating status, and the operating data stored in SCADA system lacks operating condition labels, which makes it difficult to explore the potential value of the data. In this work, a hybrid neural network model based on multiplex visibility graphs (MVG) is proposed for operating conditions...
Proceedings Papers
Rock Thin Section Image Search System Using Machine Learning Encoders
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25078-MS
... the closest match to an unlabeled sample. The system utilizes image-processing and neural-network-based encodings to build a database. Using this, it is possible to utilize existing legacy data as a guide to interpret new samples. The advantage of the system is that the associated metadata for the legacy thin...
Proceedings Papers
AI Automation in Civil Infrastructure Asset Management
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Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25080-MS
... enablement and people behaviors in order to successfully develop and implement AI capability in inspection. This is followed by a detailed discussion on AI development methodology and results from data science perspective. work process neural network classifier inspection architecture data...
Proceedings Papers
Coupling Fluid Flow and Geomechanical Deformation Using AI & FEM Approaches
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Paper presented at the International Petroleum Technology Conference, February 12, 2024
Paper Number: IPTC-23358-MS
... machine learning geological subdiscipline almani deformation neural network equation permeability geomechanical deformation implementation international petroleum technology conference approach kumar coupling iteration application permeability multiplier computed accuracy Introduction...
Proceedings Papers
Anomaly Detection of Sensor Measurements During a Turbo-Machine Prototype Testing - An Integrated ML Ops, Continual Learning Architecture
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Paper presented at the International Petroleum Technology Conference, February 12, 2024
Paper Number: IPTC-23326-EA
... shift into account. More in detail, we continuously train a recurrent Deep Neural Network to build a virtual sensor from other signals and we compare the prediction versus the real signal to raise (in case) an anomaly. Furthermore, Kullback-Leibler (KL) divergence is used to estimate the overlap between...
Proceedings Papers
Oil Pipeline Leak Detection Using Deep Learning: A Review on POC Implementation
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Paper presented at the International Petroleum Technology Conference, February 12, 2024
Paper Number: IPTC-24626-MS
... Synthetic-aperture radar (SAR) approaches are limited by their algorithm complexity which difficult to work with imbalanced data sets, doubts to select optimal features, and the relatively slow detection. Using deep learning approach could speed up the oil detection. convolutional neural network U-Net...
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