Machine Learning-Based Real-Time Prediction of Formation Lithology and Tops Using Drilling Parameters with a Web App Integration

Houdaifa Khalifa; Olusegun Stanley Tomomewo; Uchenna Frank Ndulue; Badr Eddine Berrehal

doi:10.3390/eng4030139

journal article Open Access Sep 21, 2023

Machine Learning-Based Real-Time Prediction of Formation Lithology and Tops Using Drilling Parameters with a Web App Integration

Houdaifa Khalifa

Olusegun Stanley Tomomewo Uchenna Frank Ndulue Badr Eddine Berrehal

Eng Vol. 4 No. 3 pp. 2443-2467 · MDPI AG

View at Publisher Save 10.3390/eng4030139

Abstract

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app “GeoVision”, achieves remarkable performance during its training phase with a mean accuracy of 95% and a precision of 98%. The model successfully predicts claystone, marl, and sandstone classes with high precision scores. Testing on new data yields an overall accuracy of 95%, providing valuable insights and setting a benchmark for future efforts. To address the limitations of current methodologies, such as time lags and lack of real-time data, we utilize drilling data as a unique endeavor to predict lithology. Our approach integrates nine drilling parameters, going beyond the narrow focus on the rate of penetration (ROP) often seen in previous research. The model was trained and evaluated using the open Volve field dataset, and careful data preprocessing was performed to reduce features, balance the sample distribution, and ensure an unbiased dataset. The innovative methodology demonstrates exceptional performance and offers substantial advantages for real-time geosteering. The accessibility of our models is enhanced through the user-friendly web app “GeoVision”, enabling effective utilization by drilling engineers and marking a significant advancement in the field.

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Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

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Metrics

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Citations

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References

Details

Published: Sep 21, 2023
Vol/Issue: 4(3)
Pages: 2443-2467
License: View

Authors

H

Houdaifa Khalifa

Department of Petroleum Engineering, University of North Dakota, Grand Forks, ND 58202, USA

O

Olusegun Stanley Tomomewo

College of Engineering and Mines Energy Studies, University of North Dakota, Grand Forks, ND 58202, USA

U

Uchenna Frank Ndulue

H-PTP Energy Services Limited, Lagos 106104, Nigeria

B

Badr Eddine Berrehal

SLB OFS Base, Doha P.O. Box 8746, Qatar

Cite This Article

Houdaifa Khalifa, Olusegun Stanley Tomomewo, Uchenna Frank Ndulue, et al. (2023). Machine Learning-Based Real-Time Prediction of Formation Lithology and Tops Using Drilling Parameters with a Web App Integration. Eng, 4(3), 2443-2467. https://doi.org/10.3390/eng4030139

Machine Learning-Based Real-Time Prediction of Formation Lithology and Tops Using Drilling Parameters with a Web App Integration

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