A review of research on reservoir porosity prediction by machine learning based on real-time drilling data

  • Affiliations:

    Hanoi University of Mining and Geology, Hanoi, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 21st-May-2024
  • Revised: 18th-Aug-2024
  • Accepted: 23rd-Aug-2024
  • Online: 1st-Oct-2024
Pages: 10 - 20
Views: 586
Downloads: 9
Rating: , Total rating: 0
Yours rating

Abstract:

To exploit oil effectively, it is necessary to determine the reservoir parameters of the formation. One of the important parameters that needs to be determined is porosity. Porosity prediction helps evaluate the reservoir's production performance, select the location of the production well, design enhanced oil recovery and evaluate the reservoir's economic feasibility. Normally, the value of porosity is determined directly by various laboratory core sample tests or indirectly based on the results of interpreting well geophysical measurement documents, and well logs. These traditional identification methods are often time-consuming and expensive. Laboratory testing methods are highly accurate but often require available core samples, require a lot of auxiliary measuring equipment, and sometimes require additional core sample measurement results, which consumes time and sampling costs. Moreover, well logging measurements are not always performed in all production wells. By applying different machine learning techniques such as Artificial Neural Network, Decision Tree, Random Forest, etc., the porosity value is also predicted. These techniques often use input parameters as data from well log curves or drilling data. However, using well geophysical measurement curve data as input parameters for machine learning models often faces limitations from the availability of data sources. Meanwhile, drilling parameters such as rate of penetration, weight on bit, drillstring or drillpipe rotation speed measured in revolutions/minute, torque, pumping rate of the circulation of the drilling fluid measured in gallons/minute, and the resulting standpipe pressure are continuously collected from measured while drilling sensors. The article focuses on evaluating and analyzing scientific works that have been researched on the application of machine learning techniques to predict formation porosity values, based on real-time drilling data.

How to Cite
Le, D.Quang 2024. A review of research on reservoir porosity prediction by machine learning based on real-time drilling data. Journal of Mining and Earth Sciences. 65, 5 (Oct, 2024), 10-20. DOI:https://doi.org/10.46326/JMES.2024.65(5).02.
References

Ahmadi, M. A., and Chen, Z. (2019). Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs. Petroleum, 5(3), 271–284.

Al-AbdulJabbar, A., Al-Azani, K., and Elkatatny, S. (2020). Estimation of Reservoir Porosity From Drilling Parameters Using Artificial Neural Networks. Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 61(3), 318–330.

Alhowaish, H. A., Mezghani, M. M., and Shakriov, A. (2023, October 2). Predicting Porosity from Drilling Data Using Machine Learning – Challenges and Solutions. ADIPEC.

Al-Sabaa, A., Gamal, H., and Elkatatny, S. (2021, October 18). Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence. SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry.

Alyafei, N. (2021). Fundamentals of Reservoir Rock Properties—2nd edition. QScience.com. https://doi.org/10.5339/Fundamentals_of_Reservoir_Rock_Properties_2ndEdition

Andagoya Carrillo, K. I., Avellán, F. J., and Camacho, G. (2015, November 18). ECD and Downhole Pressure Monitoring While Drilling at Ecuador Operations. SPE Latin American and Caribbean Petroleum Engineering Conference.

Angeleri, G. P., and Carpi, R. (1982). Porosity Prediction from Seismic Data. Geophysical Prospecting, 30(5), 580–607.

Barjouei, H. S., Ghorbani, H., Mohamadian, N., Wood, D. A., Davoodi, S., Moghadasi, J., and Saberi, H. (2021). Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. Journal of Petroleum Exploration and Production, 11(3), 1233–1261.

Bonnecaze, R. T., Sharma, M. M., Butler, J. E., and Arboleda, G. (2002, September 29). High Resolution Downhole Measurements of Porosity and Fluid Saturation While Core Drilling. SPE Annual Technical Conference and Exhibition.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.

Chen, T., Zhu, L., Niu, R., Trinder, C. J., Peng, L., and Lei, T. (2020). Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science, 17(3), 670–685.

Denney, D. (2003). A New Approach for Reservoir Characterization. Journal of Petroleum Technology, 55(09), 62–63.

Doyen, P. M. (1988). Porosity from seismic data: A geostatistical approach. GEOPHYSICS, 53(10), 1263–1275.

Elkatatny, S., Tariq, Z., Mahmoud, M., and Abdulraheem, A. (2018). New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs. Petroleum, 4(4), 408–418.

Ellis, D. V., Case, C. R., and Chiaramonte, J. M. (2004). Porosity from Neutron Logs II: Interpretation. Petrophysics - The SPWLA Journal of Formation Evaluation and Reservoir Description, 45(01).

Fischetti, A. I., and Andrade, A. (2002). Porosity images from well logs. Journal of Petroleum Science and Engineering, 36(3), 149–158.

Fu, B., Liu, M., He, H., Lan, F., He, X., Liu, L., Huang, L., Fan, D., Zhao, M., and Jia, Z. (2021). Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data. International Journal of Applied Earth Observation and Geoinformation, 104, 102553.

Gamal, H., and Elkatatny, S. (2021). Prediction Model Based on an Artificial Neural Network for Rock Porosity. Arabian Journal for Science and Engineering, 47(9), 11211–11221.

Gamal, H., Elkatatny, S., and Mahmoud, A. A. (2021). Machine learning models for generating the drilled porosity log for composite formations. Arabian Journal of Geosciences, 14(23), 2700.

Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., and Mohamadian, N. (2020). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, 6(4), 404–414.

Gurina, E., Klyuchnikov, N., Zaytsev, A., Romanenkova, E., Antipova, K., Simon, I., Makarov, V., and Koroteev, D. (2020). Application of machine learning to accidents detection at directional drilling. Journal of Petroleum Science and Engineering, 184, 106519.

Hamada, G. M., and Elshafei, M. A. (2009, May 9). Neural Network Prediction of Porosity and Permeability of Heterogeneous Gas Sand Reservoirs. SPE Saudi Arabia Section Technical Symposium.

Hassaan, S., Mohamed, A., Ibrahim, A. F., and Elkatatny, S. (2024). Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters. ACS Omega, acsomega.3c08795.

Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7), 1527–1554.

Kane, J. A., and Jennings, J. W. (2005, October 9). A Method to Normalize Log Data by Calibration to Large-Scale Data Trends. SPE Annual Technical Conference and Exhibition.

Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2), 4–22.

Lucia, F. J., Kerans, C., and Jennings, J. W., Jr. (2003). Carbonate Reservoir Characterization. Journal of Petroleum Technology, 55(06), 70–72.

Ma, Y., and Guo, G. (2014). Support Vector Machines Applications. Springer Science and Business Media.

Nakamoto, P. (2018). Neural Networks and Deep Learning: Neural Networks and Deep Learning, Deep Learning Explained to Your Granny. CreateSpace Independent Publishing Platform.

Nallathambi, S., and Ramasamy, K. (2017). Prediction of electricity consumption based on DT and RF: An application on USA country power consumption. 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), 1–7.

Nguyễn, T. M. H., vàLê, H. A. (2014). Dự báo độ rỗng trầm tích Miocen khu vực lô 103. Tạp chí Khoa học Kỹ thuật Mỏ - Địa chất.

Noshi, C. I., and Schubert, J. J. (2018, October 7). The Role of Machine Learning in Drilling Operations; A Review. SPE/AAPG Eastern Regional Meeting.

Nyein, C. Y., and Ali Hamada, G. M. M. (2018). Artificial Neural Network (ANN) Prediction of Porosity and Water Saturation of Shaly Sandstone Reservoirs. 2018 AAPG/EAGE/MGS Myanmar Oil and Gas Conference: A Global Oil and Gas Hotspot: Unleashing the Petroleum Systems Potential. 2018 AAPG/EAGE/MGS Myanmar Oil and Gas Conference: A Global Oil and Gas Hotspot: Unleashing the Petroleum Systems Potential, Yangon, Myanmar.

Olatunji, S. O., Selamat, A., and Abdulraheem, A. (2011). Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems. Computers in Industry, 62(2), 147–163.

Patle, A., and Chouhan, D. S. (2013). SVM kernel functions for classification. 2013 International Conference on Advances in Technology and Engineering (ICATE), 1–9.

Ramana, Y. V., and Venkatanarayana, B. (1971). An air porosimeter for the porosity of rocks. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 8(1), 29–53.

Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., and Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379–391.

Sun, J., Zhang, R., Chen, M., Chen, B., Wang, X., Li, Q., and Ren, L. (2021). Identification of Porosity and Permeability While Drilling Based on Machine Learning. Arabian Journal for Science and Engineering, 46(7), 7031–7045.

Wood, D. A. (2020). Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data. Journal of Petroleum Science and Engineering, 184, 106587.

Yazmyradova, G., Hermana, M., and Soleimani, H. (2022). Estimation of porosity from well logs and seismic using artificial neural network. IOP Conference Series: Earth and Environmental Science, 1003(1), 012017.

Zerrouki, A. A., Aïfa, T., and Baddari, K. (2014). Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria. Journal of Petroleum Science and Engineering, 115, 78–89.

Other articles