Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Network Computing and Applications
  4. Vol 10, Issue 1, 2025

Research on Efficiency Driven Classification in Petroleum Engineering Based on Big Data Algorithm

Download as PDF

DOI: 10.23977/jnca.2025.100105 | Downloads: 3 | Views: 127

Author(s)

Xiaoyu Jia 1

Affiliation(s)

1 Sinopec Huabei Oil & Gas Company Gas Production Plant No.1, Zhengzhou, 450006, Henan, China

Corresponding Author

Xiaoyu Jia

ABSTRACT

Existing petroleum engineering big data algorithms have issues like poor efficiency, insufficient classification accuracy, and poor algorithm adaptability in classification tasks by application field. In order to resolve these issues, this paper proposes an RNN (Recurrent Neural Network) algorithm, which improves the performance of the model in multi-category classification by introducing a combination of ReLU activation function and Softmax output layer. By extracting features and optimizing data from different application scenarios in the field of petroleum engineering, the algorithm effectively improves the classification accuracy and application efficiency of the model. Specifically, this paper uses different application fields in petroleum engineering big data as classification labels, uses the architecture of a neural network with multiple layers, and combines it with the Adam optimizer to improve the training speed and stability of the model by adjusting and fine-tuning the model parameters layer by layer. In the training process at each stage, special emphasis is placed on the adjustment of hyperparameters and the alleviation of the gradient vanishing problem, guaranteeing the effectiveness and precision of the classification results in multi-domain data. The findings from the experiments demonstrate that the enhanced algorithm has strong future potential and practical value, and that it can effectively boost the computation efficiency of huge amounts of data in oil and gas engineering as well as the accuracy of classification assignments in real-world applications. In the comparison of different activation functions, the ReLU activation function (improved model) performed best, with a classification accuracy of 0.852, a training time of 125 seconds, an F1-Score of 0.81, and an AUC-ROC of 0.9.

KEYWORDS

Petroleum Engineering Big Data; Application Field Classification; Deep Learning; Neural Network; Optimization Algorithm

CITE THIS PAPER

Xiaoyu Jia, Research on Efficiency Driven Classification in Petroleum Engineering Based on Big Data Algorithm. Journal of Network Computing and Applications (2025) Vol. 10: 29-38. DOI: http://dx.doi.org/10.23977/jnca.2025.100105.

REFERENCES

[1] Sadat Lavasani M, Raeisi Ardali N, Sotudeh-Gharebagh R, et al. Big data analytics opportunities for applications in process engineering[J]. Reviews in Chemical Engineering, 2023, 39(3): 479-511.
[2] Xie C, Du S, Wang J, et al. Intelligent modeling with physics-informed machine learning for petroleum engineering problems[J]. Advances in Geo-Energy Research, 2023, 8(2): 71-75.
[3] Bahaloo S, Mehrizadeh M, Najafi-Marghmaleki A. Review of application of artificial intelligence techniques in petroleum operations[J]. Petroleum Research, 2023, 8(2): 167-182.
[4] Ozowe W, Daramola G O, Ekemezie I O. Petroleum engineering innovations: Evaluating the impact of advanced gas injection techniques on reservoir management[J]. Magna Sci. Adv. Res. Rev, 2024, 11(1): 299-310.
[5] Mohammed M A, Akawee M M, Saleh Z H, et al. The effectiveness of big data classification control based on principal component analysis[J]. Bulletin of Electrical Engineering and Informatics, 2023, 12(1): 427-434.
[6] Ali A H, Mohammed M A, Hasan R A, et al. Big data classification based on improved parallel k-nearest neighbor[J]. TELKOMNIKA (Telecommunication Computing Electronics and Control), 2023, 21(1): 235-246.
[7] Wang H, Adenutsi C D, Wang C, et al. Construction and Application of a Big Data System for Regional Lakes in Coalbed Methane Development[J]. ACS omega, 2023, 8(20): 18323-18331.
[8] James G G, Oise G P, Chukwu E G, et al. Optimizing business intelligence system using big data and machine learning[J]. Journal of Information Systems and Informatics, 2024, 6(2): 1215-1236.
[9] Ohalete N C, Aderibigbe A O, Ani E C, et al. Advancements in predictive maintenance in the oil and gas industry: A review of AI and data science applications[J]. World J. Adv. Res. Rev, 2023, 20(3): 167-181.
[10] Melberg K, Gressgård L J. Digitalization and changes to work organization and management in the Norwegian petroleum industry[J]. Cognition, Technology & Work, 2023, 25(4): 447-460.
[11] Arinze C A, Izionworu V O, Isong D, et al. Integrating artificial intelligence into engineering processes for improved efficiency and safety in oil and gas operations[J]. Open Access Research Journal of Engineering and Technology, 2024, 6(1): 39-51.
[12] Weijermars R, Waheed U, Suleymanli K. Will ChatGPT and related AI-tools alter the future of the geosciences and petroleum engineering?[J]. First Break, 2023, 41(6): 53-61.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 1475
Visits: 138952

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.