Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation
Abstract
:1. Introduction
2. Related Work
2.1. Mathematical Description of Optimization Problems
2.2. Key Parameters of Drilling Trajectory
3. Borehole Trajectory Optimization Model
4. Algorithm Design Process
5. Results
5.1. Algorithm Performance Test
5.2. Actual Application Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbreviation | Full Term | Description |
KOP(m) | Kick-Off Point (meters) | The depth at which the wellbore begins to deviate from vertical drilling. |
NSGA-II | Non-Dominated Sorting Genetic Algorithm II | A widely used multi-objective evolutionary algorithm. |
MOGWO | Non-Dominated Sorting Genetic Algorithm II | A widely used multi-objective evolutionary algorithm. |
MOLPB | Multi-Objective Linear Programming-Based Optimization | An optimization approach based on linear programming. |
MOPSO | Multi-Objective Particle Swarm Optimization | A PSO-based multi-objective optimization method. |
TOPSIS | Technique for Order Preference by Similarity to Ideal Solution | A ranking-based decision-making method. |
HV | Hypervolume | A performance metric measuring the dominated volume in objective space. |
IGD | Inverted Generational Distance | Evaluates the proximity of solutions to the true Pareto front. |
Spacing | Spacing Metric | Measures the uniformity of solutions in the Pareto front. |
PSO | Particle Swarm Optimization | A stochastic optimization technique inspired by social behavior in swarms. |
BHA | Bottom Hole Assembly | The lower part of the drill string including the bit, stabilizers, and other components. |
ACO | Ant Colony Optimization | A bio-inspired heuristic algorithm based on the foraging behavior of ants. |
TMD | True Measured Depth | The actual length of the wellbore along its drilled path. |
FAQGA | Fast Adaptive Quantum Genetic Algorithm | A hybrid optimization approach integrating quantum computing and genetic algorithms. |
CORA | Constrained Optimization of Resource Allocation | A multi-constraint optimization framework. |
References
- Holland, J.H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence; The MIT Press: Cambridge, MA, USA, 1992. [Google Scholar] [CrossRef]
- Sha, L.X.; Zhang, Q.Z.; Li, L.; Qiu, S. Complex Wellbore Trajectory Optimization Method Based on Fast Adaptive Quantum Genetic Algorithm. CN 201710132117, 6 November 2024. [Google Scholar]
- Yavari, H.; Qajar, J.; Aadnøy, B.; Khosravanian, R. Selection of Optimal Well Trajectory Using Multi-Objective Genetic Algorithm and TOPSIS Method. Arabian J. Sci. Eng. 2023, 48, 16831–16855. [Google Scholar] [CrossRef]
- Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of ICNN’95—International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995; IEEE: Piscataway, NJ, USA, 1995; Volume 4, pp. 1942–1948. [Google Scholar]
- Ding, H. Research on Optimization Design and Decision-Making Analysis of Nonlinear Well Trajectory. Master’s Thesis, Dalian University of Technology, Dalian, China, 2004. [Google Scholar]
- Atashnezhad, A.; Wood, D.A.; Fereidounpour, A.; Khosravanian, R. Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. J. Nat. Gas Sci. Eng. 2014, 21, 1184–1204. [Google Scholar] [CrossRef]
- Sha, L.X.; Li, W.Y.; Zhang, Q.Z.; Lin, L. Complex Wellbore Trajectory Optimization Method Based on Improved Multi-ObjectiveParticle Swarm Optimization Algorithm. CN 201910410779.7, 6 November 2024. [Google Scholar]
- Chen, B.; Wen, G.; He, X.; Liu, X.; Liu, H.; Cheng, S. Application of adaptive grid-based multi-objective particle swarm optimization algorithm for directional drilling trajectory design. Geoenergy Sci. Eng. 2023, 222, 211431. [Google Scholar] [CrossRef]
- Dorigo, M.; Maniezzo, V.; Colorni, A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1996, 26, 29–41. [Google Scholar] [CrossRef] [PubMed]
- Liu, D.H.; Li, G.; Yuan, S.C. Chaos-Based Ant Colony Hybrid Optimization Algorithm. Comput. Eng. Appl. 2011, 47, 42–45+102. [Google Scholar]
- Chen, M.J.; Huang, B.C.; Zhang, M. Function Optimization with Hybrid and Improved Ant Colony Algorithm. CAAI Trans. Intell. Syst. 2012, 7, 370–376. [Google Scholar]
- Luo, X.; Wu, X.J. Research on the Application of Ant Colony Optimization Algorithm in WSN Routing. Comput. Eng. Sci. 2015, 37, 740–746. [Google Scholar]
- Li, C.Y. Research on the Application of Ant Colony Algorithm in Wellbore Trajectory Planning. Master’s Thesis, Xi’an Shiyou University, Xi’an, China, 2019. [Google Scholar]
- Jiang, S.Z. Research on Optimal Control Model, Algorithm, and Application of Nonlinear Well Trajectory. Master’s Thesis, Dalian University of Technology, Dalian, China, 2002. [Google Scholar]
- Yang, H.C. Optimization and Control Technology for Unconventional Oil and Gas Well Trajectories Under the “Well Factory” Model. Inner Mong. Petrochem. Ind. 2013, 39, 114–115. [Google Scholar]
- Yin, S. Optimization Design of Horizontal Wellbore Trajectory for Shale Gas in Southern Sichuan. Master’s Thesis, Southwest Petroleum University, Chengdu, China, 2014. [Google Scholar]
- Li, C.F.; Ran, F.; Wen, S.Z.; Ke, G.; Chen, L. Optimization Technology and Application of Horizontal Well Trajectory for Ultra-Deep Thin Reservoirs in Yuanba Gas Field, Sichuan Basin. Glob. Geol. 2021, 40, 354–363+374. [Google Scholar]
- Gong, F.J.; Wu, J.Z.; Wang, W. Multi-Objective Optimization of Well Trajectory Design under Geological Uncertainty. Petrochem. Appl. 2016, 35, 18–20. [Google Scholar]
- Liu, M.S.; Fu, J.H.; Bai, J. Optimization Design and Application of Shale Gas Dual-Dimensional Horizontal Well Trajectory. Special Oil Gas Reserv. 2016, 23, 147–150+158. [Google Scholar]
- Bai, J.P. Research on Three-Dimensional Wellbore Trajectory Optimization Design Modeling and Application Based on Factory Operation. Master’s Thesis, Xi’an Shiyou University, Xi’an, China, 2017. [Google Scholar]
- Zhang, L.; Zhang, Y.C.; Dong, P.H.; Yue, M.; Hou, X. Research on Key Technologies for Drilling of Shallow Large Displacement Horizontal Wells in Bohai Oilfield. Unconv. Oil Gas 2022, 9, 10–17. [Google Scholar] [CrossRef]
- Huang, W.D. Multi-Objective Optimization of Geological Drilling Trajectories with Nonlinear Constraints and Parameter Uncertainty. Master’s Thesis, China University of Geosciences, Wuhan, China, 2022. [Google Scholar] [CrossRef]
- Liu, X.L.; Qiang, Z.Z.; Huang, Y.G.; Fei, S. Application of 3D Geological Modeling Technology for Horizontal Well Based on Data Fusion of Multiple Sources. In Proceedings of the International Field Exploration and Development Conference, Urumqi, China, 15–18 November 2022; Springer: Singapore, 2022. [Google Scholar] [CrossRef]
- Fang, C.; Wang, Q.; Jiang, H.; Chen, Z.; Wang, Y.; Zhai, W.; Chen, S. Shale Wellbore Stability and Well Trajectory Optimization: A Case Study from Changning, Sichuan, China. Pet. Sci. Technol. 2022, 41, 564–585. [Google Scholar] [CrossRef]
- Qin, Z.L. Research on Horizontal Well Trajectory Design and Control in Loose Sandstone and Mudstone Formations in Niger. Master’s Thesis, China University of Petroleum (Beijing), Beijing, China, 2023. [Google Scholar]
Method | Computational Complexity | Adaptability | Solution Diversity | Applicable Scenarios |
---|---|---|---|---|
NSGA-II | High | Medium | High | Suitable for low-dimensional optimization problems, ideal for high-precision Pareto solutions. |
TOPSIS | Low | Low | Low | Suitable for quick decision-making, lacks adaptability. |
Improved MOPSO | Medium | High | High | Suitable for complex geological optimization problems, highly adaptable. |
Test Problem | Variable Dimension | The Goal Dimension | Constraint Condition Characteristics |
---|---|---|---|
Schaffer | 2 | 2 | |
Kursawe | 3 | 2 | |
ZDT6 | 30 | 2 | |
ZDT3 | 10 | 2 |
Function | Evaluating Indicator | NSGAII | MOGWO | MOLPB | MOPSO | New_MOPSO |
---|---|---|---|---|---|---|
Schaffer | IGD_score (Lower is better) | 0.0044786 | 0.0027075 | 0.0029799 | 0.011328 | 0.00248 |
HV_score (Higher is better) | 0.68306 | 0.684 | 0.7128 | 0.67968 | 0.86038 | |
Spacing_score (Lower is better) | 0.0031164 | 0.0013585 | 0.0015345 | 0.006672 | 0.00157 | |
Kursawe | IGD_score (Lower is better) | 0.00539 | 0.0030685 | 0.0031977 | 0.03456 | 0.0030041 |
HV_score (Higher is better) | 0.43218 | 0.4218 | 0.43956 | 0.408 | 0.50421 | |
Spacing_score (Lower is better) | 0.002793 | 0.0015105 | 0.0018018 | 0.0014784 | 0.0014957 | |
ZDT6 | IGD_score (Lower is better) | 0.003822 | 0.0027455 | 0.0029205 | 0.0024672 | 0.0020447 |
HV_score (Higher is better) | 0.58604 | 0.56905 | 0.594 | 0.576 | 0.63572 | |
Spacing_score (Lower is better) | 0.0033026 | 0.001824 | 0.0020097 | 0.0029568 | 0.0022616 | |
ZDT3 | IGD_score (Lower is better) | 0.27048 | 0.027835 | 0.037917 | 0.50784 | 0.002601 |
HV_score (Higher is better) | 0.4018 | 0.0028025 | 0.66924 | 0.16224 | 0.25023 | |
Spacing_score (Lower is better) | 0.013622 | 0.0093385 | 0.0050688 | 0.011616 | 0.0040176 |
Function | Comparison | Mean ± SD | p-Value (Pairwise t-Test) | Statistically Significant (p < 0.05)? |
---|---|---|---|---|
ZDT3 | New_MOPSO vs. NSGA-II | 0.002601 ± 0.00015 | 0.0123 | Yes |
New_MOPSO vs. MOGWO | 0.002601 ± 0.00015 | 0.0205 | Yes | |
New_MOPSO vs. MOLPB | 0.002601 ± 0.00015 | 0.0152 | Yes | |
ZDT6 | New_MOPSO vs. NSGA-II | 0.0020447 ± 0.00011 | 0.0054 | Yes |
New_MOPSO vs. MOGWO | 0.0020447 ± 0.00011 | 0.0032 | Yes | |
New_MOPSO vs. MOLPB | 0.0020447 ± 0.00011 | 0.0021 | Yes |
Parameter | Optimization Results of Conventional Methods | The Optimization Results of the Method in this Paper |
---|---|---|
KOP(m) | ||
Deviation angle | ||
Azimuth | ||
Build angle rate | ||
Length of stable inclined section | ||
Total length of wellbore trajectory |
Parameter | Scope of Initial Values |
---|---|
KOP(m) | |
Deviation angle | |
Azimuth | |
Build angle rate | |
Length of stable inclined section |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gu, H.; Yan, T.; Wu, Y. Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation. Appl. Sci. 2025, 15, 1364. https://doi.org/10.3390/app15031364
Gu H, Yan T, Wu Y. Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation. Applied Sciences. 2025; 15(3):1364. https://doi.org/10.3390/app15031364
Chicago/Turabian StyleGu, Haipeng, Tie Yan, and Yang Wu. 2025. "Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation" Applied Sciences 15, no. 3: 1364. https://doi.org/10.3390/app15031364
APA StyleGu, H., Yan, T., & Wu, Y. (2025). Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation. Applied Sciences, 15(3), 1364. https://doi.org/10.3390/app15031364