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Keywords: machine learning
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Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25025-EA
... , A. , 2019 . Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . 2nd Edition, O'Reilly Media, Inc. , Sebastopol. Goodfellow , I. , Bengio , Y. , Courville , A. , 2016 . Deep learning . Li , H...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-25046-MS
... In addition to processing power, cloud-based analytics enables companies to run complex simulations, interpret seismic data, and make more informed decisions in exploration and production (E&P). With the integration of machine learning algorithms, cloud platforms allow for deploying advanced...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24834-MS
... zone well logging machine learning upper clastic rock layer poisson strata rock mechanic parameter transit time horizontal geostress strength clastic rock section mpa experiment time difference Introduction Geomechanical parameters typically encompass rock mechanical properties...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24850-MS
.... The tradeoff between data and physics in different models, as shown in Fig. 1 , demonstrates that leveraging physics-based models can strike a balance between speed and accuracy. Physics-informed machine learning (PIML) techniques and physics-informed neural networks (PINNs) enable the model to function...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24687-MS
... pressure data gas lift artificial intelligence modeling & simulation objective function mismatch function presented uncertain parameter embracing greenfield uncertainty production data machine learning variant case international petroleum technology conference static model Introduction...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24705-MS
... drilling drilling fluids and materials risk management natural language cognitive system drilling operation volve field expert system drilling fluid management & disposal sequence npt risk and uncertainty assessment artificial intelligence wellbore design text processing machine learning...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24720-MS
... operation while maximizing the return of investment. trajectory design optimization problem well planning artificial intelligence joint point machine learning evolutionary algorithm surface location pad design collision risk drilling operation trajectory constraint severity...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24769-MS
... loading. Finally, for the liquid loaded wells on well cycling operation (intermittent production), a machine learning model is developed to optimize cycling parameters based on features extracted from a sequence of past cycles. The trained models predict the optimum shut-in and production time to maximize...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24804-MS
... deep learning neural network geology co 2 artificial intelligence machine learning climate change scenario saline aquifer modeling & simulation chemical flooding methods inference dataset geologist subsurface storage enhanced recovery dataset solubility residually accuracy...
Proceedings Papers

Paper presented at the International Petroleum Technology Conference, February 18–20, 2025
Paper Number: IPTC-24946-MS
... analytical formulas and machine learning algorithms to analyze well data and recommend the optimal Rigless ESP system. Engineers input key downhole parameters and production forecasts into the software, which then rigorously analyzes the data to provide precise, evidence-based recommendations. The Advisor's...

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