[1]
Y. Cheng, Y. Wang, D. A. McVay, and W. J. Lee, "Practical application of a probabilistic approach to estimate reserves using production decline data," SPE Economics & Management, vol. 2, no. 01, p.19–31, 2010.
DOI: 10.2118/95974-pa
Google Scholar
[2]
J. J. Arps, "Analysis of decline curves," Transactions of the AIME, vol. 160, no. 01, p.228–247, 1945.
DOI: 10.2118/945228-g
Google Scholar
[3]
M. J. Fetkovich, E. J. Fetkovich, and M. D. Fetkovich, "Useful concepts for decline-curve forecasting, reserve estimation, and analysis," SPE Reservoir Engineering, vol. 11, no. 01, p.13–22, 1996.
DOI: 10.2118/28628-pa
Google Scholar
[4]
E. E. Okoro, A. Okoh, E. B. Ekeinde, and A. Dosunmu, "Reserve estimation using decline curve analysis for boundary-dominated flow dry gas wells," Arab J Sci Eng, vol. 44, no. 6, p.6195–6204, 2019.
DOI: 10.1007/s13369-019-03749-2
Google Scholar
[5]
D. Rahm, "Regulating hydraulic fracturing in shale gas plays: The case of Texas," Energy Policy, vol. 39, no. 5, p.2974–2981, 2011.
DOI: 10.1016/j.enpol.2011.03.009
Google Scholar
[6]
K. Wang et al., "Rapid and accurate evaluation of reserves in different types of shale-gas wells: production-decline analysis," Int J Coal Geol, vol. 218, p.103359, 2020.
DOI: 10.1016/j.coal.2019.103359
Google Scholar
[7]
A. Satter and G. M. Iqbal, "Decline curve analysis for conventional and unconventional reservoirs," Reservoir. Eng, p.211–232, 2016.
DOI: 10.1016/b978-0-12-800219-3.00013-9
Google Scholar
[8]
A. N. Duong, "Rate-decline analysis for fracture-dominated shale reservoirs: part 2," in SPE/CSUR Unconventional Resources Conference–Canada, OnePetro, 2014.
Google Scholar
[9]
R. S. Thompson, J. D. Wright, and S. A. Digert, "The Error in estimating reserves using decline curves," in SPE Hydrocarbon Economics and Evaluation Symposium, OnePetro, 1987.
DOI: 10.2118/16295-ms
Google Scholar
[10]
J. Zhang and P. S. Yu, "Viral Marketing," in Broad Learning Through Fusions, Springer, 2019, p.351–384.
Google Scholar
[11]
"Delta Field (Niger Delta) - Wikipedia." https://en.wikipedia.org/wiki/Delta_Field_ (Niger_Delta) (accessed Sep. 17, 2022).
Google Scholar
[12]
M. A. Adegbite and G. E. Okiemute, "Opportunities and Challenges of Effective Inspection Industry in Nigerian Petroleum Sector," in 17th World Conference on Non-Destructive Testing, Shanghai, China, 2008.
Google Scholar
[13]
X. Jia and F. Zhang, "Applying data-driven method to production decline analysis and forecasting," in SPE Annual Technical Conference and Exhibition, OnePetro, 2016.
Google Scholar
[14]
J. Sun, X. Ma, and M. Kazi, "Comparison of decline curve analysis DCA with recursive neural networks RNN for production forecast of multiple wells," in SPE Western Regional Meeting, OnePetro, 2018.
DOI: 10.2118/190104-ms
Google Scholar
[15]
N. C. Chakra, K.-Y. Song, M. M. Gupta, and D. N. Saraf, "An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs)," J Pet Sci Eng, vol. 106, p.18–33, 2013.
DOI: 10.1016/j.petrol.2013.03.004
Google Scholar
[16]
A. Suhag, R. Ranjith, and F. Aminzadeh, "Comparison of shale oil production forecasting using empirical methods and artificial neural networks," in SPE Annual Technical Conference and Exhibition, OnePetro, 2017.
DOI: 10.2118/187112-ms
Google Scholar
[17]
M. A. Ahmadi, M. Ebadi, A. Shokrollahi, and S. M. J. Majidi, "Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir," Appl Soft Comput, vol. 13, no. 2, p.1085–1098, 2013.
DOI: 10.1016/j.asoc.2012.10.009
Google Scholar
[18]
J. K. Ali, "Neural networks: a new tool for the petroleum industry?," in European petroleum computer conference, OnePetro, 1994.
Google Scholar
[19]
A. Ramgulam, T. Ertekin, and P. B. Flemings, "An artificial neural network utility for the optimisation of history matching process," in Latin American & Caribbean Petroleum Engineering Conference, OnePetro, 2007.
Google Scholar
[20]
Y. Liu and R. N. Home, "Interpreting Pressure and Flow Rate Data from Permanent Downhole Gauges Using Data Mining Approaches," Proceedings - SPE Annual Technical Conference and Exhibition, vol. 5, p.4062–4077, Oct. 2011.
DOI: 10.2118/147298-MS
Google Scholar
[21]
Y. Liu and R. N. Horne, "Interpreting pressure and flow rate data from permanent downhole gauges using convolution-Kernel-based data mining approaches," in SPE Western Regional & AAPG Pacific Section Meeting 2013 Joint Technical Conference, OnePetro, 2013.
DOI: 10.2118/165346-ms
Google Scholar
[22]
S. Qin, J. Liu, X. Yang, Y. Li, L. Zhang, and Z. Liu, "Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models," Math Probl Eng, vol. 2021, p.1–15, 2021.
DOI: 10.1155/2021/5558623
Google Scholar
[23]
I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. Martinez-Muñoz, "Multilayer neural network with multi-valued neurons in time series forecasting of oil production," Neurocomputing, vol. 175, p.980–989, 2016.
DOI: 10.1016/j.neucom.2015.06.092
Google Scholar
[24]
T. Bikmukhametov and J. Jäschke, "Oil production monitoring using gradient boosting machine learning algorithm," Ifac-Papersonline, vol. 52, no. 1, p.514–519, 2019.
DOI: 10.1016/j.ifacol.2019.06.114
Google Scholar
[25]
X. Ma and Z. Liu, "Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method," Neural Comput Appl, vol. 29, p.579–591, 2018.
DOI: 10.1007/s00521-016-2721-x
Google Scholar
[26]
M. R. Khan, S. Alnuaim, Z. Tariq, and A. Abdulraheem, "Machine learning application for oil rate prediction in artificial gas lift wells," in SPE middle east oil and gas show and conference, OnePetro, 2019.
DOI: 10.2118/194713-ms
Google Scholar
[27]
R. de Oliveira Werneck et al., "Data-driven deep-learning forecasting for oil production and pressure," J Pet Sci Eng, vol. 210, p.109937, 2022.
Google Scholar
[28]
Z. Zhong, A. Y. Sun, Y. Wang, and B. Ren, "Predicting field production rates for waterflooding using a machine learning-based proxy model," J Pet Sci Eng, vol. 194, p.107574, 2020.
DOI: 10.1016/j.petrol.2020.107574
Google Scholar
[29]
F. Abdullayeva and Y. Imamverdiyev, "Development of oil production forecasting method based on deep learning," Statistics, Optimisation & Information Computing, vol. 7, no. 4, p.826–839, 2019.
DOI: 10.19139/soic-2310-5070-651
Google Scholar
[30]
U. Shahzad, T. Sengupta, A. Rao, and L. Cui, "Forecasting carbon emissions future prices using the machine learning methods," Ann Oper Res, p.1–32, 2023.
DOI: 10.1007/s10479-023-05188-7
Google Scholar
[31]
X. Kong, Y. Liu, L. Xue, G. Li, and D. Zhu, "A Hybrid Oil Production Prediction Model Based on Artificial Intelligence Technology," Energies (Basel), vol. 16, no. 3, p.1027, 2023.
DOI: 10.3390/en16031027
Google Scholar
[32]
C. Wang, G. Ma, J. Li, Z. Dai, and J. Liu, "Prediction of corrosion rate of submarine oil and gas pipelines based on ia-svm model," in IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2019, p.022023.
DOI: 10.1088/1755-1315/242/2/022023
Google Scholar
[33]
N. M. Ibrahim et al., "Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production," Sensors, vol. 22, no. 14, p.5326, 2022.
DOI: 10.3390/s22145326
Google Scholar
[34]
H. Li, C. Gong, S. Liu, J. Xu, and G. Imani, "Machine Learning-Assisted Prediction of Oil Production and CO2 Storage Effect in CO2-Water-Alternating-Gas Injection (CO2-WAG)," Applied Sciences, vol. 12, no. 21, p.10958, 2022.
DOI: 10.3390/app122110958
Google Scholar
[35]
K. M. Hanga and Y. Kovalchuk, "Machine learning and multi-agent systems in oil and gas industry applications: A survey," Comput Sci Rev, vol. 34, p.100191, 2019.
DOI: 10.1016/j.cosrev.2019.08.002
Google Scholar
[36]
D. Xin, H. Miao, A. Parameswaran, and N. Polyzotis, "Production machine learning pipelines: Empirical analysis and optimisation opportunities," in Proceedings of the 2021 International Conference on Management of Data, 2021, p.2639–2652.
DOI: 10.1145/3448016.3457566
Google Scholar
[37]
S. Mayer, T. Classen, and C. Endisch, "Modular production control using deep reinforcement learning: proximal policy optimisation," J Intell Manuf, vol. 32, no. 8, p.2335–2351, 2021.
DOI: 10.1007/s10845-021-01778-z
Google Scholar
[38]
H. Rahmanifard and T. Plaksina, "Application of artificial intelligence techniques in the petroleum industry: a review," Artif Intell Rev, vol. 52, no. 4, p.2295–2318, 2019.
DOI: 10.1007/s10462-018-9612-8
Google Scholar
[39]
E. López-Iñesta, F. Grimaldo, and M. Arevalillo-Herráez, "Combining feature extraction and expansion to improve classification based similarity learning," Pattern Recognit Lett, vol. 93, p.95–103, 2017.
DOI: 10.1016/j.patrec.2016.11.005
Google Scholar
[40]
X. Shi, Y. Feng, J. Zeng, and K. Chen, "Chaos time-series prediction based on an improved recursive Levenberg–Marquardt algorithm," Chaos Solitons Fractals, vol. 100, p.57–61, 2017.
DOI: 10.1016/j.chaos.2017.04.032
Google Scholar
[41]
X. Xiong and K. J. Lee, "Data-driven modeling to optimise the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost," Energy Exploration & Exploitation, vol. 38, no. 6, p.2413–2435, 2020.
DOI: 10.1177/0144598720927470
Google Scholar
