Applied Machine Learning and Deep Learning to Predict Oil and Gas Production

2022 ◽  
pp. 451-458
Author(s):  
Nguyen Van Hung ◽  
Luong Khanh Loc ◽  
Nguyen Thi Thanh Huong ◽  
Nguyen Van Dung ◽  
Nguyen Minh Quy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Oil And Gas ◽  
Gas Production ◽  
Oil And Gas Production ◽  
Applied Machine Learning

ISCA: Quick Real-Time Sand Potential Prediction Simulator based on Critical Drawdown Failure and Machine Learning Model

2021 ◽  
Author(s):  
A. I. Biladi
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
High Efficiency ◽  
Early Stage ◽  
Gas Production ◽  
Control Analysis ◽  
Sand Production ◽  
Oil And Gas Production ◽  
Sand Control ◽  
Prediction Techniques

Sand production is almost an inevitable problem in oil and gas production facilities. As the reservoir depletes, sand grains from the reservoir begin to flow into the wellbore, this can cause serious problems to the wellbore. Excessive sand production can eventually plug and erode tubing, casing, flowlines, and surface equipment or even lead to formation collapse. In general, once sand production has occurred and if it is not handled properly it can end the production life of a reservoir and wells. This problem mostly occurs in mature fields with marginal economics for workover. The more reasonable option is to predict or mitigate the sand production, which can help identify the most economical way of sand control methods at the early stage. Many conventional sand prediction techniques have been developed which are based on field observation and experience, laboratory sand production experiments, and theoretical or numerical modeling. These conventional techniques have proven their effectiveness, but to achieve them can be time-consuming and costly. In this paper, we try to predict sand production with high efficiency and accuracy by using a quick simulator. Integrated Sand Control Analysis or ISCA is a simple simulator to help predict early sand production based on critical borehole and calculate critical drawdown pressure prediction. ISCA is supported by several mathematical models that function to predict various types of formation. Integrated with Machine Learning makes ISCA also compatible with big data analysis. The results in this study show that the combination of Machine Learning and analytical model can achieve accuracy above 90% based on the comparison of laboratory results with software predictions. With a high level of accuracy results this software can be considered as a reliable tool to predict and analyze sand production.

METHANE DETECTION FOR OIL AND GAS PRODUCTION SITES USING PORTABLE DUAL-COMB SPECTROMETRY

2016 ◽  
Author(s):  
Sean Coburn ◽  
Greg Rieker ◽  
Kuldeep Prasad ◽  
Subhomoy Ghosh ◽  
Caroline Alden ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Oil And Gas Production ◽  
Methane Detection

Colorado areas of oil and gas production

1994 ◽  
Author(s):  
Richard F. Mast ◽  
D.H. Root ◽  
L.P. Williams ◽  
W.R. Beeman
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Oil And Gas Production
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