| Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models C Hegde, H Daigle, H Millwater, K Gray Journal of petroleum science and Engineering 159, 295-306, 2017 | 190 | 2017 |
| Use of machine learning and data analytics to increase drilling efficiency for nearby wells C Hegde, KE Gray Journal of Natural Gas Science and Engineering 40, 327-335, 2017 | 162 | 2017 |
| Using trees, bagging, and random forests to predict rate of penetration during drilling C Hegde, S Wallace, K Gray SPE Middle East Intelligent Oil and Gas Symposium, D011S001R003, 2015 | 133 | 2015 |
| Evaluation of coupled machine learning models for drilling optimization C Hegde, K Gray Journal of Natural Gas Science and Engineering 56, 397-407, 2018 | 115 | 2018 |
| Performance comparison of algorithms for real-time rate-of-penetration optimization in drilling using data-driven models C Hegde, H Daigle, KE Gray Spe Journal 23 (05), 1706-1722, 2018 | 80 | 2018 |
| Rate of penetration (ROP) modeling using hybrid models: deterministic and machine learning C Hegde, C Soares, K Gray Unconventional Resources Technology Conference, Houston, Texas, 23-25 July …, 2018 | 57 | 2018 |
| Classification of drilling stick slip severity using machine learning C Hegde, H Millwater, K Gray Journal of Petroleum Science and Engineering 179, 1023-1036, 2019 | 45 | 2019 |
| Fully coupled end-to-end drilling optimization model using machine learning C Hegde, M Pyrcz, H Millwater, H Daigle, K Gray Journal of Petroleum Science and Engineering 186, 106681, 2020 | 44 | 2020 |
| Rate of penetration (ROP) optimization in drilling with vibration control C Hegde, H Millwater, M Pyrcz, H Daigle, K Gray Journal of natural gas science and engineering 67, 71-81, 2019 | 44 | 2019 |
| A critical comparison of regression models and artificial neural networks to predict ground vibrations K Ram Chandar, VR Sastry, C Hegde Geotechnical and geological engineering 35, 573-583, 2017 | 35 | 2017 |
| Real time prediction and classification of torque and drag during drilling using statistical learning methods C Hegde, S Wallace, K Gray SPE Eastern Regional Meeting, SPE-177313-MS, 2015 | 35 | 2015 |
| Acoustic fingerprinting for rock identification during drilling S Shreedharan, C Hegde, S Sharma, H Vardhan International Journal of Mining and Mineral Engineering 5 (2), 89-105, 2014 | 34 | 2014 |
| A system for real-time drilling performance optimization and automation based on statistical learning methods SP Wallace, CM Hegde, KE Gray SPE Middle East Intelligent Oil and Gas Symposium, D011S002R002, 2015 | 32 | 2015 |
| Use of regression and bootstrapping in drilling inference and prediction CM Hegde, SP Wallace, KE Gray SPE Middle East Intelligent Oil and Gas Symposium, D011S012R002, 2015 | 28 | 2015 |
| Prediction of peak particle velocity using multi regression analysis: case studies K Ram Chandar, VR Sastry, C Hegde, S Shreedharan Geomechanics and Geoengineering 12 (3), 207-214, 2017 | 10 | 2017 |
| Application of statistical learning techniques for rate of penetration (ROP) prediction in drilling C Hegde The University of Texas at Austin, 2016 | 10 | 2016 |
| Classification of stability of highwall during highwall mining: a statistical adaptive learning approach K Ram Chandar, C Hegde, M Yellishetty, B Gowtham Kumar Geotechnical and Geological Engineering 33, 511-521, 2015 | 10 | 2015 |
| Application of Real-Time Video Streaming and Analytics to Breakdown Rig Connection Process C Hegde, O Awan, T Wiemers Offshore Technology Conference, D031S031R002, 2018 | 5 | 2018 |
| Application of statistical learning models to predict and optimize rate of penetration of drilling CM Hegde | 5 | 2016 |
| End-to-end drilling optimization using machine learning CM Hegde | 3 | 2018 |
