[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wang et al., 2022 - Google Patents

Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs

Wang et al., 2022

Document ID
15217316567185474994
Author
Wang L
Yao Y
Wang K
Adenutsi C
Zhao G
Lai F
Publication year
Publication venue
Energy

External Links

Snippet

Forecasting productivity of shale gas has gained much attention in recent years. However, few researchers considered forecasting absolute open flow potential (AOFP). The conventional method of determining the AOFP of gas wells is through a systematic well …
Continue reading at www.sciencedirect.com (other versions)

Similar Documents

Publication Publication Date Title
Wang et al. Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs
Akbar et al. Scheduling for sustainable manufacturing: A review
Bhagat et al. Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater
Palmer Renewables rise above fossil fuels
Kong et al. Machine learning-assisted production data analysis in liquid-rich Duvernay Formation
Kamari et al. Evaluating the unloading gradient pressure in continuous gas-lift systems during petroleum production operations
Wang et al. A new approach for predicting oil mobilities and unveiling their controlling factors in a lacustrine shale system: Insights from interpretable machine learning model
Kim et al. Using transformer and a reweighting technique to develop a remaining useful life estimation method for turbofan engines
Paraskevas et al. Current status, future expectations and mitigation potential scenarios for China's primary aluminium industry
Xia et al. Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model
CN111694856B (en) Reservoir sensitivity intelligent prediction method and device
CN113738353B (en) Method, system, equipment and storage medium for predicting movable oil quantity of oil-containing shale
Qian et al. Optimization of shale gas fracturing parameters based on artificial intelligence algorithm
Li et al. Productivity forecast for multi-stage fracturing in shale gas wells based on a random forest algorithm
Fan et al. Forecasting the self-sufficiency rate of China’s energy by the hybrid gray models
Nemer Oil and gas production forecasting using decision trees, random forst, and XGBoost
Xinling et al. Influencing factors and prediction methods for production of tight oil reservoir in Pingbei Oilfield
CN115017827A (en) A method and system for predicting gas reservoir development law based on deep learning
CN111694855B (en) Reservoir sensitivity intelligent prediction data processing method and device
Guo Evaluation of oil Wells performance ranking in high water cut stage
Xiao et al. Development effect evaluation and gas injection adaptability for high dip reservoir
Tretyakov et al. Technology predictions for arctic hydrocarbon development: Digitalization potential
Zhang et al. Study on Influencing Factors and Prediction Methods of Initial Productivity of Volumetric Fracturing Wells
Chatterjee et al. Machine Learning Based Prediction of Energy Consumption
Zhu Research on the Whole Life Cycle Management of Water Conservancy Project Based on K-means Algorithm