Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immens...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 14; p. 5326
Main Authors:	Ibrahim, Nehad M., Alharbi, Ali A., Alzahrani, Turki A., Abdulkarim, Abdullah M., Alessa, Ibrahim A., Hameed, Abdullah M., Albabtain, Abdullaziz S., Alqahtani, Deemah A., Alsawwaf, Mohammad K., Almuqhim, Abdullah A.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 16-07-2022 MDPI
Subjects:	Accuracy Algorithms Artificial intelligence artificial neural network Classification Data analysis Datasets Deep learning Economic forecasting Energy industry Generalized linear models Machine learning Mean square errors Neural networks Oil wells oil, gas and water production prediction Petroleum production random forest recurrent neural network Reservoirs Simulation Variables
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Summary:	In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s22145326