A Unified Conceptual Framework for AI-Enhanced Reservoir Characterization and Predictive Field Decision Systems

References

[1] Ahmadi, M. A., & Chen, Z. (2019). Comparison of machine learning methods for estimating permeability of a tight gas reservoir. Journal of Petroleum Science and Engineering, 175, 563-574.

[2] Al-Mudhafar, W. J., & Mohamed, L. (2020). Automated lithofacies classification and seismic reservoir characterization through deep learning. Interpretation, 8(4), SQ45-SQ56.

[3] Al-Qasim, A., Al-Zahrani, A., & Kokal, S. (2021). Artificial intelligence applications in upstream oil and gas: A review. SPE Journal, 26(06), 3600-3620.

[4] Anemangely, M., Ramezanzadeh, A., & Tokhmechi, B. (2019). A new approach for determination of rock types using a combination of clustering and deep learning. Journal of Petroleum Science and Engineering, 174, 524-539.

[5] Arias, J., & Wang, J. (2022). Physics-informed neural networks for reservoir simulation: A comprehensive review. Geoenergy Science and Engineering, 218, 110945.

[6] Bello, O., Holzmann, J., Yaqoob, C., & Teodoriu, C. (2019). Application of machine learning in oil and gas upstream: A review. Journal of Petroleum Science and Engineering, 173, 1100-1115.

[7] Chen, Q., Cui, Z., & Liu, G. (2022). SA-VAE: A novel approach for reservoir characterization based on variational auto-encoder and self-attention. Journal of Petroleum Exploration and Production Technology, 12, 1541-1556.

[8] Crnkovic-Friis, L., & Erlandson, M. (2019). Deep learning for geological prediction from seismic data. Leading Edge, 38(3), 200-206.

[9] Dankwa, O. K., & Anyan, O. (2020). Predictive analytics for reservoir management: Integrating AI with traditional simulation. Journal of Natural Gas Science and Engineering, 81, 103445.

[10] Ekechi, A. T. & Fasasi, T. S. (2020). Conceptual Framework for Sustainable Gas Processing and Dehydration Efficiency in Offshore Facilities. International Journal of Multidisciplinary Futuristic Development, 1(5), 340–357.

[11] Ekechi, A. T. & Fasasi, T. S. (2022). Conceptual Framework for Process Safety and Operational Reliability in FPSO-based Energy Systems. International Journal of Artificial Intelligence Engineering and Transformation, 3(1), 19 - 37.

[12] Ekechi, A. T. (2022). Model for Reconstitution of Drilling Mud Using Surfactants for Enhanced Drilling Performance. International Journal of Multidisciplinary Research and Growth Evaluation, 3(5), 655-671.

[13] Ekechi, A. T. (2022). Model for Reconstitution of Drilling Mud Using Surfactants for Enhanced Drilling Performance. International Journal of Multidisciplinary Research and Growth Evaluation, 3(5), 655–671.

[14] Elebe, O., & Imediegwu, C. C. (2022). A Dashboard Framework for Executive Decision-Making in Loan Origination Teams.

[15] Elkatatny, S. (2019). Developing a new model for sonic transit time using artificial intelligence for carbonate reservoirs. Journal of Petroleum Exploration and Production Technology, 9, 21-29.

[16] Fang, L., et al. (2021). Deep learning-based seismic inversion for reservoir parameter estimation. Geophysical Prospecting, 69(6), 1250-1265.

[17] Gholami, R., et al. (2021). Machine learning in geomechanics: A review. Earth-Science Reviews, 218, 103661.

[18] Hassan, A., Al-Ahmadi, H., & Mahmoud, M. (2021). Applications of machine learning in oil and gas industry: A review of reservoir characterization. Petroleum, 7(4), 384-396.

[19] Hegde, C., & Gray, K. (2019). Use of machine learning and data analytics in drilling operations: A review. Journal of Petroleum Science and Engineering, 174, 431-439.

[20] Huang, R., et al. (2020). Deep reinforcement learning for automated well placement optimization. SPE Journal, 25(05), 2410-2425.

[21] Jia, N., et al. (2023). A new paradigm for physics-informed AI-driven reservoir research: From multiscale characterization to intelligent seepage simulation. Energies, 16(1), 270.

[22] Jiang, W. D., Zou, H. L., & Yan, J. (2023). Transforming oil and gas operations through AI-driven innovations: Field applications and economic impact. Journal of Industrial Information Integration, 32, 100435.

[23] Karimpouli, S., & Tahmasebi, P. (2019). Physics-informed machine learning: Case study for predicting geomechanical properties. Journal of Petroleum Science and Engineering, 182, 106348.

[24] Kim, Y., et al. (2020). Deep learning for rock facies classification using logging-while-drilling data. Journal of Petroleum Science and Engineering, 186, 106692.

[25] Lee, K., et al. (2022). Real-time predictive maintenance for offshore oil and gas facilities using deep reinforcement learning. Ocean Engineering, 254, 111320.

[26] Li, H., & Misra, S. (2019). Long short-term memory and gated recurrent unit neural networks for production forecasting from shale gas reservoirs. Journal of Petroleum Science and Engineering, 173, 252-264.

[27] Liang, J., et al. (2023). Data-driven surrogate modeling for reservoir simulation using deep convolutional neural networks. Petroleum Science, 20(2), 415-429.

[28] Liu, W., et al. (2021). A review of artificial intelligence applications in the petroleum industry. Journal of Petroleum Science and Engineering, 200, 108353.

[29] Ma, Y., et al. (2022). Intelligent reservoir characterization using graph neural networks. Journal of Marine Science and Engineering, 10(4), 512.

[30] Mamo, S., & Dennis, J. (2020). Deep learning for production forecasting in water-injected reservoirs using Bayesian regularization. Petroleum Exploration and Development, 47(1), 160-170.

[31] Masoudi, R., et al. (2021). A unified framework for integrated asset management and field development optimization. Journal of Natural Gas Science and Engineering, 95, 104167.

[32] Miranda, A. S., et al. (2022). Machine learning applied to reservoir engineering: A systematic review. Journal of Petroleum Science and Engineering, 212, 110265.

[33] Mohammadpoor, M., & Torabi, F. (2020). Big Data analytics in oil and gas industry: Determining the most-influential metrics in production forecasting. Journal of Natural Gas Science and Engineering, 77, 103258.

[34] Morteza, A., et al. (2021). Intelligent techniques for predicting the rate of penetration: A review. Journal of Petroleum Science and Engineering, 196, 107699.

[35] Noshi, C. I., & Schubert, J. J. (2019). The role of machine learning in drilling operations; A review. SPE Western Regional Meeting. doi:10.2118/195311-MS

[36] Ofoedu, A. T., Ozor, J. E., Sofoluwe, O., & Jambol, D. D. (2023). A holistic control system framework for managing complex multistream crude processing in floating production units. International Journal of Scientific Research in Civil Engineering, 7(3), 83–101.

[37] Ogbodu, S., Tichelaar, B., Amadi, C., Anijekwu, C., Eke, K., Alli-Oluwafuyi, J., & Schutjens, P. (2023). Drilling through shales below depleted sands: Case study of a Niger Delta HPHT gas development well. Society of Petroleum Engineers. https://doi.org/10.2118/217195-MS

[38] Okonkwo, C. S., et al. (2023). Conceptual model for asset lifecycle management and predictive analytics systems. International Journal of Scientific Research in Computer Science.

[39] Oladoye, S. O., Bamigwojo, O. V., James, A. O., & Ijiga, O. M. (2021). AI-driven predictive maintenance modeling for high-voltage distribution assets using sensor fusion and time-series degradation analysis. International Journal of Scientific Research in Science, Engineering and Technology, 11(2), 387-411.

[40] Olukoga, T. A., & Feng, Y. (2021). Deep reinforcement learning for automated well-control and production optimization. Journal of Petroleum Science and Engineering, 202, 108528.

[41] Onwuchekwa, C. (2020). Artificial intelligence in oil and gas: A review of current status and future prospects. Petroleum Exploration and Development, 47(4), 785-794.

[42] Osimobi, J. C., Amadi, C., Chejieh, C., Okereke, N., & Kanu, M. (2022). Building realistic velocity model and providing high-end imaging to the business: A case study of KoloCreek, Nigeria, onshore Niger Delta. The Shell Petroleum Development Company of Nigeria Limited.

[43] Pang, S., et al. (2023). Federated learning for privacy-preserving reservoir characterization in multi-operator fields. Applied Energy, 331, 120456.

[44] Priyadarshini, S., & Ghassemian, H. (2021). Deep learning for seismic facies classification: A review. Computers & Geosciences, 156, 104889.

[45] Qadeer, A., et al. (2021). Machine learning-based lithology classification: A review. Earth Science Informatics, 14, 567-582.

[46] Rammay, M. H., & Abdulraheem, A. (2022). Deep learning-based surrogate modeling for reservoir simulation: A review. Journal of Petroleum Exploration and Production Technology, 12, 2301-2315.

[47] Saini, G., et al. (2020). Data-driven predictive field management: An AI approach. Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference. doi:10.2118/202314-MS

[48] Shah Rukh, Oziri, S. T., & Seyi-Lande, O. B. (2022). Framework design for machine learning adoption in enterprise performance optimization. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(3), 798–830.

[49] Shah Rukh, S. T., Oziri, S. T., & Seyi-Lande, O. B. (2023). Framework for enhancing marketing strategy through predictive and prescriptive analytics. Shodhshauryam, International Scientific Refereed Research Journal, 6(4), 531-569.

[50] Shah, S. S., et al. (2023). A review of AI and machine learning in smart oilfields. Energy Reports, 9, 125-140.

[51] Shi, X., et al. (2021). Deep learning for identifying reservoir fracture networks from seismic data. Journal of Geophysics and Engineering, 18(2), 241-255.

[52] Sircar, A., et al. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(3), 181-191.

[53] Sun, J., et al. (2020). Deep learning for reservoir property estimation from seismic data. Geophysics, 85(4), WA25-WA35.

[54] Tariq, Z., et al. (2021). A systematic review of data-driven methods in reservoir engineering. Journal of Petroleum Exploration and Production Technology, 11, 2671-2692.

[55] Temizel, C., et al. (2020). A review of applications of artificial intelligence and machine learning in reservoir engineering. SPE Europec. doi:10.2118/200587-MS

[56] Tian, C., et al. (2022). Physics-guided neural networks for predicting reservoir pressure and saturation. Journal of Computational Physics, 468, 111495.

[57] Verma, S., & Marfurt, K. (2022). Machine learning in seismic data processing and interpretation. Interpretation, 10(3), SE1-SE15.

[58] Wang, J., et al. (2021). Physics-constrained deep learning for high-dimensional surrogate modeling of reservoir simulation. Journal of Computational Physics, 430, 110099.

[59] Wang, S., et al. (2019). Deep learning for image-based petrophysical property prediction. Petrophysics, 60(2), 212-225.

[60] Wood, D. A. (2020). Predictive modeling of total organic carbon in shale reservoirs using machine learning. Journal of Natural Gas Science and Engineering, 76, 103215.

[61] Wu, X., et al. (2020). Deep learning for seismic interpretation: A review. IEEE Geoscience and Remote Sensing Magazine, 8(4), 10-33.

[62] Xu, W., et al. (2022). Intelligent drilling trajectory optimization using deep reinforcement learning. Journal of Petroleum Science and Engineering, 215, 110651.

[63] Yan, B., et al. (2021). A deep learning surrogate model for reservoir simulation with varying well locations. Journal of Petroleum Science and Engineering, 205, 108842.

[64] Yang, J., et al. (2020). Automated seismic fault detection using convolutional neural networks. Geophysics, 85(2), N17-N27.

[65] Yin, S., et al. (2022). Deep learning for multi-scale reservoir characterization. Frontiers in Earth Science, 10, 894562.

[66] Yu, S., et al. (2021). Deep learning for well-log interpretation: A review. Artificial Intelligence Review, 54, 5717-5753.

[67] Yuan, Y., et al. (2023). Physics-informed machine learning for reservoir flow simulation. Water Resources Research, 59(3), e2022WR033568.

[68] Zhang, D., & Chen, Y. (2022). Machine learning in reservoir simulation: A survey of surrogate modeling. Advances in Geo-Energy Research, 6(1), 1-15.

[69] Zhang, L., et al. (2023). Intelligent reservoir production optimization via multi-agent reinforcement learning. Journal of Petroleum Science and Engineering, 220, 111158.

[70] Zhang, Y., Morris, S., & Yu, H. (2023). Reservoir characterization by deep-learning model: Detection of true sand thickness. EAGE GeoTech. doi:10.3997/2214-4609.202352004

[71] Zhong, Z., et al. (2020). Predict production and optimize fracture designs in unconventional reservoirs using deep learning. Journal of Petroleum Science and Engineering, 191, 107124.

[72] Zhou, J., et al. (2021). Application of machine learning in mineral resource estimation: A review. Earth-Science Reviews, 216, 103597.

@article{1716525,
      author = {Chibueze Marcellus Amadi, Oluwaseyi Ayotunde Akano, Alexander Onwumere},
      title = {A Unified Conceptual Framework for AI-Enhanced Reservoir Characterization and Predictive Field Decision Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {637-653},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716525.pdf},
      abstract = {The integration of artificial intelligence (AI) into reservoir characterization and field decision systems has significantly transformed exploration and production strategies in the oil and gas industry. This paper presents a comprehensive review of AI-enhanced reservoir characterization and predictive field decision systems, focusing on their methodologies, architectures, and practical implications for improving subsurface understanding and operational efficiency. Traditional reservoir characterization techniques often rely on deterministic models and manual interpretation of geological, geophysical, and petrophysical data, which can be time-consuming and prone to uncertainty. In contrast, AI-driven approaches leverage machine learning, deep learning, and data-driven analytics to process large volumes of heterogeneous data, including seismic surveys, well logs, and production data, enabling more accurate and automated reservoir modeling. The review examines the application of advanced AI techniques such as convolutional neural networks for seismic interpretation, recurrent neural networks for production forecasting, and clustering algorithms for facies classification. It also explores the integration of digital twin technologies and real-time data analytics for predictive decision-making in field operations. By combining data assimilation, uncertainty quantification, and optimization algorithms, AI-enhanced systems provide dynamic and adaptive decision support for reservoir management, including well placement, enhanced oil recovery strategies, and production optimization. Furthermore, the paper highlights challenges associated with data quality, model interpretability, and system integration, emphasizing the need for robust governance frameworks and explainable AI techniques. Emerging trends such as edge computing, cloud-based analytics, and hybrid physics-informed machine learning models are also discussed as key enablers of next-generation reservoir management systems. This review contributes to the field by synthesizing current advancements and identifying research gaps, providing a structured foundation for developing intelligent, scalable, and data-driven reservoir characterization and decision systems that enhance operational performance and resource recovery.},
      keywords = {Reservoir Characterization, Artificial Intelligence in Oil and Gas, Predictive Field Decision Systems, Seismic Data Analytics, Digital Twin Technology, Production Optimization.},
      month = {December},
  }