Machine Learning Classifies Data for Early Warning of Stuck Pipe Detection in Geothermal Drilling

Diyah Rosiani; Zulfan; Bambang Yudho Suranta; Akhmad Sofyan; Fandika Galih Pradana; Redha Bhawika Putra

doi:10.18517/ijaseit.15.1.20333

DOI : https://doi.org/10.18517/ijaseit.15.1.20333

Machine Learning Classifies Data for Early Warning of Stuck Pipe Detection in Geothermal Drilling

Diyah Rosiani ⁽¹⁾, Zulfan ⁽²⁾, Bambang Yudho Suranta ⁽³⁾, Akhmad Sofyan ⁽⁴⁾, Fandika Galih Pradana ⁽⁵⁾, Redha Bhawika Putra ⁽⁶⁾

(1) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu, Indonesia

(2) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu, Indonesia

(3) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu, Indonesia

(4) Department of Mineralogy, Geochemistry and Petrology, Faculty of Earth Science, University of Szeged, Szeged, Hungary

(5) PT Arka Data Pratama, Infiniti Office, Bellezza BSA 1st Floor, Jakarta Selatan, Indonesia

(6) PT Arka Data Pratama, Infiniti Office, Bellezza BSA 1st Floor, Jakarta Selatan, Indonesia

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D. Rosiani, Zulfan, B. Y. Suranta, A. Sofyan, F. G. Pradana, and R. B. Putra, “Machine Learning Classifies Data for Early Warning of Stuck Pipe Detection in Geothermal Drilling”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 44–51, Feb. 2025.

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Stuck pipe is a common issue encountered during geothermal well drilling which often disrupts operations and potentially leads to the suspension of drilling activities. This issue commonly occurs in lost circulation conditions within the reservoir zone, which reduce the capacity to lift cuttings and destabilize the drill hole due to inadequate drilling fluid performance. Therefore, this study aimed to propose an early warning system for detecting stuck pipe anomalies in geothermal drilling using machine learning as an Artificial Intelligence (AI) technique to expedite detection and response times. Time-based mud logging unit sensor data was used in the study collected from the drilling of five wells in the MGL field, all of which experienced stuck pipe incidents. The analysis further analyzed significant drilling parameters such as gas rate (considered for the first time), weight on bit (WOB), rotations per minute (RPM), standpipe pressure, mud flow rate, torque, hook load, rate of penetration (ROP), return, and condition readings. The dataset was evaluated using Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to predict and classify conditions as normal, pre-stuck, or stuck. The results showed that SVM provided accuracy, precision, and recall of 0.99, 0.98, and 0.97, respectively outperforming ANN scoring 0.99, 0.98, and 0.89. This implied that SVM could provide better prediction results than ANN, offering a fast and effective method for early detection by improving response times and accuracy in preventing stuck pipe incidents.

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