Research on oil and gas production prediction process based on machine learning

Zhenzhi Liu; Sanshan Li; Lu Li

doi:10.54097/ije.v2i2.7773

Authors

Zhenzhi Liu
Sanshan Li
Lu Li

DOI:

https://doi.org/10.54097/ije.v2i2.7773

Keywords:

Production forecast, Machine learning, Oil and gas, Artificial intelligence

Abstract

In recent years, the development trend of artificial intelligence is getting better and better. It has been widely used not only in the fields of big data analysis, automobile automatic driving, intelligent robot and face recognition, but also in various fields of oil and gas industry. Oil and gas production prediction is an important part of reservoir engineering, which is very important for the future production and development of strata, and can give developers some development suggestions. At present, the methods used in oil and gas production prediction are mainly traditional means such as numerical simulation and history matching. With the application of artificial intelligence in various fields of oil and gas industry, the use of machine learning models for oil and gas production prediction has become the direction of development and research. This paper summarizes the basic process and main technical means of applying machine learning model to predict oil and gas production by investigating the research of domestic and foreign scholars on artificial intelligence in oil and gas production prediction in recent years. It provides ideas and lays a foundation for future researchers to study this aspect, and also contributes to the development of smart oil fields in the future.

References

MIN C, DAI B R, ZHANG X H, et al. A Review of the Application Progress of Machine Learning in Oil and Gas Industry[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 1- 15.

Kuang L C, Liu H, Ren Y L, et al. Application and development trend of artificial intelligence in petroleum exploration and development[J]. Petroleum Exploration and Development. 2021, 48(1): 1- 11.

Cheng Y F,Yang Y. Application of Artificial Intelligence in Oil Well Production Prediction[J]. Chemical Engineering Design Communications, 2021, 47(01): 125- 126.

Guo Y. Feature recognition from potential fields using neural networks[J]. SEG Technical Program Expanded Abstracts, 1949, 11(1): 1410.

Gupta K D, Vallega V, Maniar H, et al. A deep-learning approach for borehole image interpretation[C]//SPWLA 60th Annual Logging Symposium. OnePetro, 2019.

Ashena R, Rabiei M, Rasouli V, et al. Drilling parameters optimization using an innovative artificial intelligence model[J]. Journal of Energy Resources Technology, 2021, 143(5).

Aliyev R, Paul D. A novel application of artificial neural networks to predict rate of penetration[C]//SPE Western Regional Meeting. OnePetro, 2019.

Kiss A, Fruhwirth R K, Pongratz R, et al. Formation breakdown pressure prediction with artificial neural networks[C]//SPE International Hydraulic Fracturing Technology Conference and Exhibition. OnePetro, 2018.

Lin X, Liu Z S, Gao Y, et al. Analysis of main control factors of oil production based on machine learning [J]. China CIO News, 2019.

Gu J W, Ren Y L, Wang Y K, et al. Prediction methods of remaining oil plane distribution based on machine learning[J]. Journal of China University of Petroleum (Edition of Natural Science), 2020, 44(4): 39- 46.

Xu Z J. Forecastion oil price trends with news sentiment[D]. Beijing University of Chemical Technology, 2016.

Li T, Tan Y, Ahmad F A, et al. A new method to production prediction for the shale gas reservoir[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: 1- 14.

Lin B, Guo J, Liu X, et al. Prediction of flowback ratio and production in Sichuan shale gas reservoirs and their relationships with stimulated reservoir volume[J]. Journal of Petroleum Science and Engineering, 2020, 184: 106529.

Meng M, Zhong R, Wei Z. Prediction of methane adsorption in shale: Classical models and machine learning based models[J]. Fuel, 2020, 278: 118358.

Amirian E, Dejam M, Chen Z. Performance forecasting for polymer flooding in heavy oil reservoirs[J]. Fuel, 2018, 216: 83- 100.

A Amirian E, Dejam M, Chen Z. Performance forecasting for polymer flooding in heavy oil reservoirs[J]. Fuel, 2018, 216: 83-100.

Amirian E, Fedutenko E, Yang C, et al. Artificial neural network modeling and forecasting of oil reservoir performance[J]. Applications of Data Management and Analysis: Case Studies in Social Networks and Beyond, 2018: 43-67.

Li J H, Ji L. Productivity forecast for multi-stage fracturing in shale gas wells based on a random forest algorithm[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: 1-10.

Nguyen-Le V, Shin H, Little E. Development of shale gas prediction models for long-term production and economics based on early production data in barnett reservoir[J]. Energies, 2020, 13(2): 424.

Xue L, Liu Y, Xiong Y, et al. A data-driven shale gas production forecasting method based on the multi-objective random forest regression[J]. Journal of Petroleum Science and Engineering, 2021, 196: 107801.

Wang H L, Mu L X,Shi F G, et al.Production prediction at ultra-high water cut stage via Recurrent Neural Network[J]. Petroleum Exploration and Development, 2020, 47(05): 1084- 1090.

Zhang R, Jia H. Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs[J]. Petroleum Exploration and Development, 2021, 48(01): 175- 184.

Liu W, Liu W, Gu J W. Oil production prediction based on a machine learning method[J]. Oil Drilling & Production Technology, 2020, 42(01): 70- 75.

Sagheer A, Kotb M. Time series forecasting of petroleum production using deep LSTM recurrent networks[J]. Neurocomputing, 2019, 323: 203- 213.

Gu J W, Zhou M, Li Z T, et al. Oil Well Production Forecast with Long-Short Term Memory Network Model Based on Data Mining[J]. Special Oil & Gas Reservoirs, 2019, 26(02): 77- 81+ 131.

Hou C H. New well oil production forecast method based on long-term and short-term memory neural network[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(03): 105- 110.

Hua J C. Production forecast modeling and application based on machine learning [D]. China University of Geosciences (Beijing), 2020.

Lu Z Y. Research on production forecast method of gas well in tight gas reservoir based on big data analysis[D]. Southwest Petroleum University, 2019.