Deep Learning Framework for Accurate Static and Dynamic Prediction of CO2 Enhanced Oil Recovery and Storage Capacity DOI Open Access

Zhipeng Xiao,

Bin Shen,

Jiguang Yang

и другие.

Processes, Год журнала: 2024, Номер 12(8), С. 1693 - 1693

Опубликована: Авг. 13, 2024

As global warming intensifies, carbon capture, utilization, and storage (CCUS) technology is widely used to reduce greenhouse gas emissions. CO2-enhanced oil recovery (CO2-EOR) has, once again, received attention, which can achieve the dual benefits of CO2 storage. However, flexibly effectively predicting flooding capacity potential reservoirs a major problem. Traditional prediction methods often lack ability comprehensively integrate static dynamic predictions and, thus, cannot fully understand CO2-EOR capacity. This study proposes comprehensive deep learning framework, named LightTrans, based on lightweight gradient boosting machine (LightGBM) Temporal Fusion Transformers, for The model predicts cumulative production, amount, Net Present Value test set with an average R-square (R2) 0.9482 mean absolute percentage error (MAPE) 0.0143. It shows great performance. In addition, its R2 0.9998, MAPE 0.0025. excellent ability. proposed successfully captures time-varying characteristics systems. worth noting that our 105–106 times faster than traditional numerical simulators, again demonstrates high-efficiency value LightTrans model. Our framework provides efficient, reliable, intelligent solution development optimization

Язык: Английский

Comprehensive Assessment of E. coli Dynamics in River Water Using Advanced Machine Learning and Explainable AI DOI

Santanu Mallik,

Bikram Saha,

Krishanu Podder

и другие.

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106816 - 106816

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Boosting-Based Machine Learning Applications in Polymer Science: A Review DOI Open Access
Ivan Malashin, В С Тынченко, Andrei Gantimurov

и другие.

Polymers, Год журнала: 2025, Номер 17(4), С. 499 - 499

Опубликована: Фев. 14, 2025

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest machine learning (ML) methods aid data analysis, material design, predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost LightGBM, have emerged as powerful tools for tackling high-dimensional complex problems science. This paper provides overview applications science, highlighting their contributions areas such structure-property relationships, synthesis, performance prediction, characterization. By examining recent case on techniques this review aims highlight potential advancing characterization, optimization materials.

Язык: Английский

Процитировано

1
Edge-cloud collaboration-driven predictive planning based on LSTM-attention for wastewater treatment DOI
Shuaiyin Ma, Wei Ding,

Yujuan Zheng

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер 195, С. 110425 - 110425

Опубликована: Июль 27, 2024

Язык: Английский

Процитировано

6
Multidimensional Lost Circulation Risk Quantification Assessment Model Based on Ensemble Machine Learning DOI
Haibo Mu, Guancheng Jiang, Wei Zhang

и другие.

SPE Journal, Год журнала: 2025, Номер unknown, С. 1 - 11

Опубликована: Март 1, 2025

Summary The risk of lost circulation is a complex problem that cannot be ignored during drilling operations, and accurate assessment crucial for preventing controlling events. In this study, we establish multidimensional quantitative model based on ensemble machine learning, comprehensively considering three dimensions—formation risk, operation fluid risk. It can effectively capture quantify the interactive relationship between different factors, accuracy efficiency improved when learning algorithms determine dimensional weights. results example verification show threshold index set to 0.55, in 442 samples drilled certain block, 85.02% without 70.21% with circulation. This result reflects uncertainty occurrence events field difference two categories approximately 15%, error within an acceptable range (0.1~0.2). independent variable parameters each dimension adjusted according actual situation blocks, thresholds correction factors set. established has high adaptability, which guide prevention control.

Язык: Английский

Процитировано

0
Physics-Informed Dynamic Bayesian Networks for Time-Dependent Reliability Prediction of Subsea Wellhead Sealing System with Multi-States DOI
Shengnan Wu, Huarong Gong, Shengtao Ma

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Analyzing Key Parameters in Underground Hydrogen Storage Using Machine Learning Surrogate Models DOI

Tanin Esfandi,

Yasin Noruzi,

Mir Saeid Safavi

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review DOI
Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113129 - 113129

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Deep Learning Framework for Accurate Static and Dynamic Prediction of CO2 Enhanced Oil Recovery and Storage Capacity DOI Open Access

Zhipeng Xiao,

Bin Shen,

Jiguang Yang

и другие.

Processes, Год журнала: 2024, Номер 12(8), С. 1693 - 1693

Опубликована: Авг. 13, 2024

As global warming intensifies, carbon capture, utilization, and storage (CCUS) technology is widely used to reduce greenhouse gas emissions. CO2-enhanced oil recovery (CO2-EOR) has, once again, received attention, which can achieve the dual benefits of CO2 storage. However, flexibly effectively predicting flooding capacity potential reservoirs a major problem. Traditional prediction methods often lack ability comprehensively integrate static dynamic predictions and, thus, cannot fully understand CO2-EOR capacity. This study proposes comprehensive deep learning framework, named LightTrans, based on lightweight gradient boosting machine (LightGBM) Temporal Fusion Transformers, for The model predicts cumulative production, amount, Net Present Value test set with an average R-square (R2) 0.9482 mean absolute percentage error (MAPE) 0.0143. It shows great performance. In addition, its R2 0.9998, MAPE 0.0025. excellent ability. proposed successfully captures time-varying characteristics systems. worth noting that our 105–106 times faster than traditional numerical simulators, again demonstrates high-efficiency value LightTrans model. Our framework provides efficient, reliable, intelligent solution development optimization

Язык: Английский

Процитировано

2