Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN
Abstract
:1. Introduction
2. Artificial Intelligence Algorithm
2.1. Fully Connected Neural Network
2.2. Long Short-Term Memory Neural Network
3. Field Data Processing
3.1. Data Introduction
3.2. Feature Correlation Analysis
3.3. Time Series Data Processing
4. Model Construction
4.1. Model Workflow
4.2. Parameter Optimization
5. File Application
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Diaz, M.B.; Kim, K.Y.; Shin, H.-S.; Zhuang, L. Predicting rate of penetration during drilling of deep geothermal well in Korea using artificial neural networks and real-time data collection. J. Nat. Gas Sci. Eng. 2019, 67, 225–232. [Google Scholar] [CrossRef]
- Zhou, F.; Fan, H.; Liu, Y.; Ye, Y.; Diao, H.; Wang, Z.; Rached, R.; Tu, Y.; Davio, E. Application of Xgboost Algorithm in Rate of Penetration Prediction with Accuracy. In Proceedings of the International Petroleum Technology Conference, Riyadh, Saudi Arabia, 21–23 February 2022. [Google Scholar]
- Zhang, H.; Lu, B.; Liao, L.; Bao, H.; Wang, Z.; Hou, X.; Mulunjkar, A.; Jin, X. Combining Machine Learning and Classic Drilling Theories to Improve Rate of Penetration Prediction. In Proceedings of the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates, 25–27 May 2021. [Google Scholar]
- Lawal, A.I.; Kwon, S.; Onifade, M. Prediction of rock penetration rate using a novel antlion optimized ANN and statistical modelling. J. Afr. Earth Sci. 2021, 182, 104287. [Google Scholar] [CrossRef]
- Elmgerbi, A.M.; Ettinger, C.P.; Tekum, P.M.; Thonhauser, G.; Nascimento, A. Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization. In Proceedings of the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates, 25–27 May 2021. [Google Scholar]
- Hazbeh, O.; Aghdam, S.K.-y.; Ghorbani, H.; Mohamadian, N.; Ahmadi Alvar, M.; Moghadasi, J. Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well. Pet. Res. 2021, 6, 271–282. [Google Scholar] [CrossRef]
- Hegde, C.; Wallace, S.; Gray, K. Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling. In Proceedings of the SPE Middle East Intelligent Oil and Gas Conference and Exhibition, Abu Dhabi, United Arab Emirates, 15–16 September 2015. [Google Scholar]
- Kor, K.; Altun, G. Is Support Vector Regression method suitable for predicting rate of penetration? J. Pet. Sci. Eng. 2020, 194, 107542. [Google Scholar] [CrossRef]
- Abdulmalek, A.S.; Salaheldin, E.; Abdulazeez, A.; Mohammed, M.; Abdulwahab, Z.A.; Mohamed, I.M. Prediction of Rate of Penetration of Deep and Tight Formation Using Support Vector Machine. In Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23–26 April 2018. [Google Scholar]
- Amadi, K.; Iyalla, I.; Prabhu, R. Modeling and Predicting Performance of Autonomous Rotary Drilling System Using Machine Learning Techniques. In Proceedings of the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 1–3 August 2021. [Google Scholar]
- Mahmoud, A.A.; Elkatatny, S.; Al-AbdulJabbar, A.; Moussa, T.; Gamal, H.; Shehri, D.A. Artificial Neural Networks Model for Prediction of the Rate of Penetration While Horizontally Drilling Carbonate Formations. In Proceedings of the 54th U.S. Rock Mechanics/Geomechanics Symposium, Golden, CO, USA, 28 June–1 July 2020. [Google Scholar]
- Al-AbdulJabbar, A.; Elkatatny, S.; Mahmoud, M.; Abdulraheem, A. Predicting Rate of Penetration Using Artificial Intelligence Techniques. In Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23–26 April 2018. [Google Scholar]
- Aliyev, R.; Paul, D. A Novel Application of Artificial Neural Networks to Predict Rate of Penetration. In Proceedings of the SPE Western Regional Meeting, San Jose, CA, USA, 23–26 April 2019. [Google Scholar]
- Negara, A.; Saad, B. Combining Insight from Physics-Based Models into Data-Driven Model for Predicting Drilling Rate of Penetration. In Proceedings of the International Petroleum Technology Conference, Dammam, Saudi Arabia, 13–15 January 2020. [Google Scholar]
- Soares, C.; Gray, K. Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models. J. Pet. Sci. Eng. 2019, 172, 934–959. [Google Scholar] [CrossRef]
- Diaz, M.B.; Kim, K.Y.; Shin, H.S. On-Line Prediction Model for Rate of Penetration (ROP) With Cumulating Field Data in Real Time. In Proceedings of the 4th ISRM Young Scholars Symposium on Rock Mechanics, Jeju, Korea, 10–12 May 2017. [Google Scholar]
- Hong, Y.; Yoo, J.; Wang, S.; Yoon, S. Augmented Machine Learning Approach of Rate of Penetration Prediction for North Sea Oilfield. In Proceedings of the Offshore Technology Conference Asia, Kuala Lumpur, Malaysia, 2–6 November 2020. [Google Scholar]
- Alkinani, H.H.; Al-Hameedi, A.T.T.; Dunn-Norman, S. Data-driven recurrent neural network model to predict the rate of penetration. Upstream Oil Gas Technol. 2021, 7, 100047. [Google Scholar] [CrossRef]
- Li, Y.; Samuel, R. Prediction of Penetration Rate Ahead of the Bit through Real-Time Updated Machine Learning Models. In Proceedings of the SPE/IADC International Drilling Conference and Exhibition, The Hague, The Netherlands, 5–7 March 2019. [Google Scholar]
- Alkinani, H.H.; Al-Hameedi, A.T.; Dunn-Norman, S.; Flori, R.E.; Al-Alwani, M.A.; Mutar, R.A. Dynamic Neural Network Model to Predict the Rate of Penetration Prior to Drilling. In Proceedings of the 53rd U.S. Rock Mechanics/Geomechanics Symposium, New York, NY, USA, 23–26 June 2019. [Google Scholar]
- Bardhan, A.; Kardani, N.; GuhaRay, A.; Burman, A.; Samui, P.; Zhang, Y. Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment. J. Rock Mech. Geotech. Eng. 2021, 13, 1398–1412. [Google Scholar] [CrossRef]
- Li, C.; Cheng, C. Prediction and Optimization of Rate of Penetration using a Hybrid Artificial Intelligence Method based on an Improved Genetic Algorithm and Artificial Neural Network. In Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, United Arab Emirates, 9–12 November 2020. [Google Scholar]
- Elkatatny, S. Rate of Penetration Prediction Using Self-Adaptive Differential Evolution-Artificial Neural Network. In Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23–26 April 2018. [Google Scholar]
- Amadi, K.W.; Iyalla, I.; Liu, Y.; Alsaba, M.; Kuten, D. Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System. In Proceedings of the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates, 25–27 May 2021. [Google Scholar]
- Adetifa, O.; Iyalla, I.; Amadi, K. Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction. In Proceedings of the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 1–3 August 2021. [Google Scholar]
- Zhu, S.; Song, X.; Zhu, Z.; Yao, X.; Liu, M. Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model. Appl. Sci. 2022, 12, 5282. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef] [PubMed]
- Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to Forget: Continual Prediction with LSTM. Neural Comput. 2000, 12, 2451–2471. [Google Scholar] [CrossRef] [PubMed]
Items | Parameter |
---|---|
Geological | Gamma Ray (GR), Spontaneous Potential (SP), Acoustic (AC) |
Engineering | Well Depth, Rotational Speed (RPM), Weight on Bit (WOB), Torque, Hook Load (HL), Riser Pressure (RP), Inlet Flow (IF), Outlet Flow (OF), Inlet Temperature (IT), Outlet Temperature (OT), Inlet Density of Drilling Fluid (ID), Outlet Density of Drilling Fluid (OD), Inlet Conductivity (IC), Outlet Conductivity (OC), Pump Impulse Speed (PIS) |
Trajectory | Deviation Angle, Borehole Curvature |
Drilling Fluid | Density, Viscosity, Plastic Viscosity, Yield |
Drilling Bit | Bit Size, Bit Type, Bit Footage (BF) |
LSTM | FNN | Model Parameters | ||||
---|---|---|---|---|---|---|
Step Length | Layers | Neural | Layer | Neural | Active Function | Learning Rate |
5 | 1 | 10 | 1 | 32 | ReLU | 1 × 10−3 |
10 | 2 | 20 | 2 | 64 | ||
15 | 3 | 30 | 3 | 128 | Tanh | 1 × 10−4 |
20 | 4 | 40 | 4 | 256 |
LSTM | FNN | Model Parameters | ||||
---|---|---|---|---|---|---|
Step Length | Layers | Neural | Layer | Neural | Active Function | Learning Rate |
10 | 2 | 30 | 3 | 128 | ReLU | 1 × 10−3 |
Model | Train Set Well 1 (80%) | Test Set 1 Well 1 (20%) | Test Set 2 Well 2 (100%) | |||
---|---|---|---|---|---|---|
MRE | MRE | MRE | ||||
FNN | 18.12 | 0.72 | 20.02 | 0.69 | 26.30 | 0.58 |
LSTM | 17.23 | 0.83 | 20.03 | 0.71 | 25.56 | 0.65 |
LSTM + FNN | 13.05 | 0.90 | 15.11 | 0.87 | 18.23 | 0.83 |
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Liu, H.; Jin, Y.; Song, X.; Pei, Z. Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN. Appl. Sci. 2022, 12, 7731. https://doi.org/10.3390/app12157731
Liu H, Jin Y, Song X, Pei Z. Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN. Applied Sciences. 2022; 12(15):7731. https://doi.org/10.3390/app12157731
Chicago/Turabian StyleLiu, Hongtao, Yan Jin, Xianzhi Song, and Zhijun Pei. 2022. "Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN" Applied Sciences 12, no. 15: 7731. https://doi.org/10.3390/app12157731
APA StyleLiu, H., Jin, Y., Song, X., & Pei, Z. (2022). Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN. Applied Sciences, 12(15), 7731. https://doi.org/10.3390/app12157731