Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Olusegun Stanley Tomomewo
 -- 1243 2023-10-06 12:30:40 |
2 update references and layout
Rita Xu
 Meta information modification 1243 2023-10-07 03:36:55 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Khalifa, H.; Tomomewo, O.S.; Ndulue, U.F.; Berrehal, B.E. Lithology Prediction. Encyclopedia. Available online: https://encyclopedia.pub/entry/49874 (accessed on 18 May 2024).
Khalifa H, Tomomewo OS, Ndulue UF, Berrehal BE. Lithology Prediction. Encyclopedia. Available at: https://encyclopedia.pub/entry/49874. Accessed May 18, 2024.
Khalifa, Houdaifa, Olusegun Stanley Tomomewo, Uchenna Frank Ndulue, Badr Eddine Berrehal. "Lithology Prediction" Encyclopedia, https://encyclopedia.pub/entry/49874 (accessed May 18, 2024).
Khalifa, H., Tomomewo, O.S., Ndulue, U.F., & Berrehal, B.E. (2023, October 06). Lithology Prediction. In Encyclopedia. https://encyclopedia.pub/entry/49874
Khalifa, Houdaifa, et al. "Lithology Prediction." Encyclopedia. Web. 06 October, 2023.
Lithology Prediction
Edit
This entry is adapted from the peer-reviewed paper 10.3390/eng4030139

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This research presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification.

lithology prediction machine learning drilling data

1. Introduction

Since the early days of the oil industry, human ingenuity has been at work to overcome various challenges. Among these challenges, real-time and precise determination of the tops of the formation and its lithology whereas drilling is of utmost importance to guarantee efficient and safe drilling operations [1]. Accurate information about the formation tops is essential when designing a casing program, as it is necessary to select the right depths for placing the casing to ensure efficient zonal separation, and to correctly design the correct mud weight in order to keep the wellbore conditions in check [2][3][4].
Drilling engineers in oil fields use four distinct methods to identify different reservoir zones or formation tops: rate of penetration (ROP) charts, gamma-ray (GR) logs, formation cuttings, and mud logging [5][6][7]. These techniques are helpful for drillers to delineate the formation tops, but each one has drawbacks such as high costs, lower accuracy, and substantial labor. Moreover, the majority of these measurements encounter temporal or depth-related delays, which restrict the ability to instantly estimate the formation tops. These limitations pose challenges to the feasibility and efficacy of current techniques employed for formation top determination.
Although lithology significantly impacts the ROP, other drilling parameters also exert considerable influence on ROP fluctuations [8]. Consequently, relying solely on ROP for estimating lithology changes or formation tops is inadequate, particularly when other drilling parameters experience significant variability. Furthermore, as the wellbore depth increases, there is a noticeable time lag in obtaining geophysical logs or drilled cuttings, which delays the prediction of the currently drilled formation [6]. Employing techniques such as GR, measurement, or logging while drilling, or mud logging in each section is also economically impractical and does not offer prompt and essential information.
On the other hand, to address these challenges, researchers from the realm of the oil and gas industry have harnessed the power of ML to transform key aspects of this industry. In drilling optimization, Berrehal et al. (2022) [9] proposed an ML-based approach for a real-time mechanical earth model, enhancing wellbore stability in the Volve field. Similarly, Al-Sudani et al. (2017) [10] introduced a control engineering system for real-time monitoring of drilling mechanical energy and bit wear, optimizing drilling performance. Meanwhile, for fracturing, Erofeev et al. (2021) [11] predicted post-hydraulic fracturing oil and liquid production with 80% accuracy, enabling real-time HF candidate selection. In the domain of oil recovery, Ouadi et al. (2023) [12] introduced high-accuracy models for predicting gas well productivity using Fishbone drilling, demonstrating its potential to enhance hydrocarbon recovery and reduce environmental impact. Although Ahmed et al. (2017) [13] showcased the potential of AI techniques in estimating oil recovery factors in early-time reservoirs, surpassing existing correlations. Additionally, Hamadi et al. (2023) [14] presented a robust machine-learning framework to predict key parameters in CO2 -enhanced oil recovery, delivering superior accuracy and insights for efficient CO2-EOR design. Furthermore, Mouedden et al. (2022) [15] proposed FIScreT, a decision-making tool based on fuzzy logic for automating candidate well selection in stimulation processes.

2. Lithology Prediction

Lithology prediction using machine learning has rapidly evolved from early foundational models to an array of sophisticated techniques, integrating real-time data and achieving high accuracy. The initial explorations into the field of lithology prediction were laid by Rogers et al. (1992) [16], Benaouda et al. (1999) [17], and Wang and Zhang (2008) [18]. Utilizing well-log data, these pioneers developed predictive models; however, they faced challenges in predicting thin formations and dealing with missing density logs. As the field progressed, Qi and Carr (2006) [19] and Al-Anazi and Gates (2010) [20] contributed to the development of machine-learning models for lithofacies and permeability prediction, using refined artificial neural network (ANN) and support vector machine (SVM) techniques. Moazzeni and Haffar (2015) [21] highlighted the impact of external factors on drilling parameters, revealing the need for further refinement of these machine-learning techniques. Addressing this need, Raeesi et al. (2012) [22], Wang and Carr (2012) [23], and Al-Mudhafar (2017) [24] introduced the use of Artificial Neural Networks (ANNs) and comprehensive integrated workflows, which significantly improved lithofacies classification.
The challenge of real-time lithology prediction during drilling operations was addressed by Mohamed et al. (2019) [25] and Nanjo and Tanaka (2019, 2020) [26], utilizing machine-learning models and image analysis methods. Elkatatny et al. (2019) [27] took a significant step towards real-time prediction, using ANN models to determine formation tops based on drilling parameters. Gupta et al. (2020) [28] designed a real-time machine-learning workflow for lithology prediction during drilling, marking a milestone in the field.
In their research, Zhang and Baines (2021) [29] explored machine-learning models such as ANN, SVM, and CNN, which yielded promising results, especially the 2D CNN combined with PCA feature extraction. Similarly, Wei Zhoucheng et al. (2019) [30] proposed a multi-well lithology identification method that involved feature engineering, machine-learning model training, and optimal model selection. Additionally, Aniyom et al. (2022) [31] demonstrated the potential of ensemble methods to improve lithology prediction performance through the development of a voting classifier machine-learning model.
Several studies have integrated additional features or methods into their models to improve classification performance. Xi Chen et al. (2020) [32] combined the Reducing Error Correcting Output Code algorithm with the Kernel Fisher Discriminant Analysis, outperforming conventional methods. Jiang et al. (2021) [33] introduced a stratigraphic unit as an additional feature, significantly improving lithology classification. Zerui Li et al. (2020) [34] proposed a semi-supervised lithology identification workflow using a Laplacian support vector machine, enhancing classification performance by utilizing feature and depth similarities.
Mou et al. (2016) [35] employed support vector machine models to estimate volcanic lithology in the Liaohe Basin, China, achieving high accuracy. In another study, Moazzeni et al. (2015) [21] accurately predicted formation and lithology in the South Pars gas field, Iran, using artificial neural networks. Similarly, Wang De-ping et al. (2007) [36] attained a 96% correctness rate in predicting lithology in the Bayantala oil field by utilizing an SVM model. These studies focused on specific geological contexts and demonstrated impressive accuracy levels.
Flexible and adaptive models have been explored for lithology prediction. Jia et al. (2012) [37] demonstrated the efficacy of an adaptive neuro-fuzzy inference system for lithology identification from well-log data. Cheng et al. (2010) [38] combined a particle swarm optimization (PSO) algorithm with the least squares support vector machines (LSSVM) for higher precision lithology identification.
Sebtosheikh and Salehi (2015) [39] employed support vector machines (SVMs) with various kernel functions for accurate lithology prediction in a multilayered carbonate reservoir in Iran. Building on this, Avanzini et al. (2016) [40] presented a workflow using cluster analysis to identify productive sweet spots in unconventional reservoirs, focusing on the Barnett Shale Formation. In a different approach, Gu et al. (2019) [41] achieved high (>75%) lithology prediction accuracies by integrating CRBMs and PSO into PNNs. Additionally, Imamverdiyev and Sukhostat (2019) [42] introduced a deep learning 1D CNN model that outperformed other methods in geological facies classification.
Moazzeni et al. (2019) [43] made significant advancements through their research about real-time prediction models by developing an ANN model optimized with a genetic algorithm and Taguchi experimental design for lithology and formation prediction. In a related study, Zhang and Baines (2021) [29] demonstrated the potential of real-time models with their 2D CNN model, achieving over 90% accuracy in identifying four lithology classes. These notable developments have contributed to the progress of real-time prediction techniques.
Finally, recent studies have demonstrated the feasibility of rapid, automated lithology prediction. Popescu et al. (2020) [44] created a supervised machine-learning pipeline that enabled rapid, scalable, and confident lithology prediction. Ao et al. (2019) [45] combined mean–shift feature extraction and random forest classification to improve prediction accuracy. Zhang et al. (2017) [46] used a convolutional neural network for accurate lithology identification from borehole images with a success rate of about 95%.

References

  1. Al-AbdulJabbar, A.; Elkatatny, S.; Mahmoud, M.E.; Abdelgawad, K.; Al-Majed, A. A robust rate of penetration model for carbonate formation. J. Energy Resour. Technol.-Trans. ASME 2018, 141, 042903.
  2. Bourgoyne, A.T., Jr.; Chenevert, M.E.; Millheim Keith, K.; Young, F.S., Jr. Applied Drilling Engineering; Elsevier: Amsterdam, The Netherlands, 1986; ISBN 978-1-55563-001-0. Available online: http://refhub.elsevier.com/S0920-4105(21)00234-5/sref14 (accessed on 23 June 2023).
  3. Rabia, H. Determination of lithology from well logs using a neural network. In Well Engineering & Construction; Entrac Consulting Limited: London, UK, 2002; Volume 76, pp. 1–650. ISBN 9780954108700.
  4. Hossain, M.E.; Al-Majed, A.A. Fundamentals of Sustainable Drilling Engineering; Wiley: Hoboken, NJ, USA, 2015.
  5. Holstein, E.D.; Warner, H.R. Overview of Water Saturation Determination for the Ivishak (Sadlerochit) Reservoir, Prudhoe Bay Field. In Proceedings of the SPE Annual Technical Conference and Exhibition, New Orleans, LA, USA, 25–28 September 1994.
  6. Crain, E.R. Crain’s Petrophysical Handbook (3rd Millennium). Spectrum 2000 Mindware. 2010. Available online: https://www.spec2000.net/08-mud.htm (accessed on 23 June 2023).
  7. Zhu, L.; Li, H.; Yang, Z.; Li, C.; Ao, Y. Intelligent logging lithological interpretation with convolution neural networks. Petrophysics 2018, 59, 799–810.
  8. Elkatatny, S. New approach to optimize the rate of penetration using artificial neural network. Arab. J. Sci. Eng. 2017, 43, 6297–6304.
  9. Berrehal, B.E.; Laalam, A.; Chemmakh, A.; Ouadi, H.; Merzoug, A.; Djezzar, S.; Boualam, A. A new perspective for the conception of mechanical earth model using machine learning in the Volve Field, Norwegian North Sea. In Proceedings of the 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, NM, USA, 26–29 June 2022.
  10. Al-Sudani, J.A. Real-time monitoring of mechanical specific energy and bit wear using control engineering systems. J. Pet. Sci. Eng. 2017, 149, 171–182.
  11. Erofeev, A.; Orlov, D.; Perets, D.S.; Koroteev, D. AI-Based Estimation of Hydraulic fracturing Effect. SPE J. 2021, 26, 1812–1823.
  12. Ouadi, H.; Laalam, A.; Hassan, A.; Chemmakh, A.; Rasouli, V.; Mahmoud, M. Design and performance analysis of dry gas fishbone wells for lower carbon footprint. Fuels 2023, 4, 92–110.
  13. Ahmed, A.A.; Elkatatny, S.; Abdulraheem, A.; Mahmoud, M. Application of artificial intelligence techniques in estimating oil recovery factor for water derive sandy reservoirs. In Proceedings of the SPE Kuwait Oil & Gas Show and Conference, Kuwait City, Kuwait, 15–18 October 2017.
  14. Hamadi, M.; Mehadji, T.E.; Laalam, A.; Zeraibi, N.; Tomomewo, O.S.; Ouadi, H.; Dehdouh, A. Prediction of key parameters in the design of CO2 miscible injection via the application of machine learning algorithms. Eng 2023, 4, 1905–1932.
  15. Mouedden, N.; Laalam, A.; Mahmoud, M.; Rabiei, M.; Merzoug, A.; Ouadi, H.; Boualam, A.; Djezzar, S. A screening methodology using fuzzy logic to improve the well stimulation candidate selection. In Proceedings of the 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, NM, USA, 26–29 June 2022.
  16. Rogers, S.J.; Fang, J.H.; Karr, C.L.; Stanley, D.A. Determination of lithology from well logs using a neural network. AAPG Bull. 1992, 76, 731–739.
  17. Benaouda, D.; Wadge, G.; Whitmarsh, R.B.; Rothwell, R.G.; MacLeod, C.J. Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: An example from the Ocean Drilling Program. Geophys. J. Int. 1999, 136, 477–491.
  18. Wang, K.; Zhang, L. Predicting formation lithology from log data by using a neural network. Pet. Sci. 2008, 5, 242–246.
  19. Qi, L.; Carr, T.R. Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas. Comput. Geosci. 2006, 32, 947–964.
  20. Al-Anazi, A.F.; Gates, I.D. A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng. Geol. 2010, 114, 267–277.
  21. Moazzeni, A.; Haffar, M. Artificial Intelligence for Lithology Identification through Real-Time Drilling Data. J. Earth Sci. Clim. Chang. 2015, 6, 265.
  22. Raeesi, M.; Moradzadeh, A.; Ardejani, F.D.; Rahimi, M. Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks. J. Pet. Sci. Eng. 2012, 82–83, 151–165.
  23. Wang, G.; Carr, T.R. Methodology of organic-rich shale lithofacies identification and prediction: A case study from Marcellus Shale in the Appalachian basin. Comput. Geosci. 2012, 49, 151–163.
  24. Al-Mudhafar, W.J. Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. J. Pet. Explor. Prod. Technol. 2017, 7, 1023–1033.
  25. Mohamed, I.M.; Mohamed, S.A.; Mazher, I.; Chester, P. Formation Lithology Classification: Insights into Machine Learning Methods. In Proceedings of the SPE Annual Technical Conference and Exhibition, Calgary, AB, Canada, 30 September–2 October 2019.
  26. Nanjo, T.; Tanaka, S. Carbonate Lithology Identification with Generative Adversarial Networks. In Proceedings of the International Petroleum Technology Conference, Dhahran, Saudi Arabia, 13–15 January 2020.
  27. Elkatatny, S.; Al-AbdulJabbar, A.; Mahmoud, A.A. New robust model to estimate formation tops in real time using artificial neural networks (ANN). Petrophysics 2019, 60, 825–837.
  28. Gupta, I.; Tran, N.L.; Devegowda, D.; Jayaram, V.; Rai, C.; Sondergeld, C.H.; Karami, H. Looking ahead of the bit using surface drilling and petrophysical data: Machine-Learning-Based Real-Time geosteering in Volve Field. SPE J. 2020, 25, 990–1006.
  29. Zhang, J.; Baines, G. Probability Distribution Assessment for Classifying Subterranean Formations Using Machine Learning US Patent Publication Number 20220004919, 6 January 2022. Available online: https://patents.google.com/patent/US20220004919A1/en (accessed on 23 June 2023).
  30. Zhoucheng, W.; Zhizhang, W.; Ruyi, W.; Shengjie, P.; Xiao, Y.; Weifang, W.; Xiaojian, X.; Bingtao, L.; Xianghui, L. A Multi-Well Complex Lithology Intelligent Identification Method and System Based on Logging Data (CN 109919184 A); National Intellectual Property Administration: Beijing, China, 2019; Available online: https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&FT=D&date=20190621&CC=CN&NR=109919184A&KC=A# (accessed on 24 June 2023).
  31. Aniyom, E.; Chikwe, A.; Odo, J. Hybridization of Optimized Supervised Machine Learning Algorithms for Effective Lithology. In Proceedings of the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 1–3 August 2022.
  32. Chen, X.; Cao, W.; Gan, C.; Hu, W.; Wu, M. A Hybrid Reducing Error Correcting Output Code for Lithology Identification. IEEE Trans. Circuits Syst. II Express Briefs 2020, 67, 2254–2258.
  33. Jiang, C.; Zhang, D.; Chen, S. Lithology identification from well log curves via neural networks with additional geological constraint. Geophysics 2021, 86, IM85–IM100.
  34. Li, Z.; Kang, Y.; Feng, D.; Wang, X.; Lv, W.; Chang, J.; Zheng, W. Semi-supervised learning for lithology identification using Laplacian support vector machine. J. Pet. Sci. Eng. 2020, 195, 107510.
  35. Mou, D.; Wang, Z. A comparison of binary and multiclass support vector machine models for volcanic lithology estimation using geophysical log data from Liaohe Basin, China. Explor. Geophys. 2016, 47, 145–149.
  36. De-ping, W. A New Identification Method for Complex Lithology with Support Vector Machine. J. Daqing Pet. Inst. 2007. Available online: https://api.semanticscholar.org/CorpusID:111435892 (accessed on 24 June 2023).
  37. Jia, H. The application of Adaptive Neuro-Fuzzy Inference System in lithology identification. In Proceedings of the 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Nanjing, China, 18–20 October 2012; pp. 966–968.
  38. Cheng, G.; Guo, R.; Wu, W. Petroleum Lithology Discrimination Based on PSO-LSSVM Classification Model. In Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation, Sanya, China, 22–24 January 2010; Volume 4, pp. 365–368.
  39. Sebtosheikh, M.; Salehi, A. Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. J. Pet. Sci. Eng. 2015, 134, 143–149.
  40. Avanzini, A.; Balossino, P.; Brignoli, M.; Spelta, E.; Tarchiani, C. Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir. Interpretation 2016, 4, SL21–SL31.
  41. Gu, Y.; Bao, Z.; Song, X.; Patil, S.; Ling, K. Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization. J. Pet. Sci. Eng. 2019, 179, 966–978.
  42. Imamverdiyev, Y.; Sukhostat, L.V. Lithological facies classification using deep convolutional neural network. J. Pet. Sci. Eng. 2019, 174, 216–228.
  43. Moazzeni, A.; Khamehchi, E. Drilling Rate Optimization by Automatic Lithology Prediction Using Hybrid Machine Learning. Dir. Open Access J. 2019, 9, 77–88.
  44. Popescu, M.; Head, R.; Ferriday, T.; Evans, K.; Montero, J.; Zhang, J.; Jones, G.; Kaeng, G. Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data. In Proceedings of the SPE Eastern Europe Subsurface Conference, Kyiv, Ukraine, 23–24 November 2021.
  45. Ao, Y.; Li, H.; Zhu, L.; Ali, S.; Yang, Z. Logging Lithology Discrimination in the Prototype Similarity Space with Random Forest. IEEE Geosci. Remote Sens. Lett. 2019, 16, 687–691.
  46. Zhang, P.; Sun, J.; Jiang, Y.; Gao, J. Deep Learning Method for Lithology Identification from Borehole Images. In Proceedings of the 79th EAGE Conference and Exhibition, Paris, France, 12–15 June 2017; pp. 1–5.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Engineering, Petroleum
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Houdaifa Khalifa
,
Olusegun Stanley Tomomewo
,
Uchenna Frank Ndulue
,
Badr Eddine Berrehal
View Times: 168
Revisions: 2 times (View History)
Update Date: 07 Oct 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback