Linear Multivariable Control of Underbalanced-Drilling Operations
- Torbjørn Pedersen (Norwegian University of Science and Technology) | John-Morten Godhavn (Statoil)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- December 2017
- Document Type
- Journal Paper
- 301 - 311
- 2017.Society of Petroleum Engineers
- Multivariable control, Underbalanced drilling, Pressure management, Simulation, Model predictive control
- 3 in the last 30 days
- 255 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
This work presents a new multivariable controller for management of topside and bottomhole objectives during underbalanced drilling (UBD). A model predictive control (MPC) solution is used to control pressures, rate of penetration (ROP), and flow downhole while also ensuring that the topside processing constraints are respected.
With automated control, it is possible to reduce nonproductive time (NPT), improve safety, and operate closer to the process constraints. MPC is a good fit for UBD because of its easy inherent handling of multiple objectives and constraints. With good pressure control, it is in some cases also possible to reduce the number of casing strings.
The control solution is evaluated through simulations in a high-fidelity multiphase-flow oil and gas simulator (OLGA). It is shown that we can meet multiple objectives both at the surface and at different locations in the well. The optimization problem is solved with good results well within the given time constraints.
The used linear prediction models are relatively easy to understand and maintain. They are also fast and well-suited for optimization and predictions. However, the process is nonlinear, and the linear models will be less accurate as the process conditions change. Retuning or model adaptation might be required to obtain the desired performance. It is possible to include nonlinear models in the control framework, referred to as nonlinear MPC (NMPC), but this will add complexity and require more computational power.
|File Size||1 MB||Number of Pages||11|
Aarsnes, U. J. F., Meglio, F. D., Aamo, O. M. et al. 2014. Fit-for-Purpose Modeling for Automation of Underbalanced Drilling Operations. Presented at the SPE/IADC Managed Pressure Drilling & Underbalanced Operations Conference & Exhibition, Madrid, Spain, 8–9 April 2014. SPE-168955-MS. https://doi.org/10.2118/168955-MS.
Asgharzadeh, R. S., Hubbell, C., Perez, H. D. et al. 2015. Multivariate Control for Managed-Pressure-Drilling Systems by Use of High-Speed Telemetry. SPE J. 21 (2): 459–470. SPE-170962-PA. https://doi.org/10.2118/170962-PA.
Bendiksen, K. H., Maines, D., Moe, R. et al. 1991. The Dynamic Two-Fluid Model OLGA: Theory and Application. SPE Res Eng 6 (2):171–180. SPE-19451-PA. https://doi.org/10.2118/19451-PA.
Breyholtz, Ø. and Nikolaou, M. 2012. Drilling Automation: Presenting a Framework for Automated Operations. SPE Drill & Compl 27 (1): 118–126. SPE-158109-PA. https://doi.org/10.2118/158109-PA.
Breyholtz, Ø., Nygaard, G., Godhavn, J.-M. et al. 2009. Evaluating Control Designs for Co-Ordinating Pump Rates and Choke Valve During Managed Pressure Drilling Operations. Presented at the 18th IEEE International Conference on Control Applications, St. Petersburg, Russia, 8–10 July. https://doi.org/10.1109/CCA.2009.5281013.
Dake, L. ed. 1998. Fundamentals of Reservoir Engineering, Vol. 8. Amsterdam: Elsevier Science.
Eaton, A. N., Beal, L. D. R., Thorpe, S. D. et al. 2015. Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174969-MS. https://doi.org/10.2118/174969-MS.
Finley, D., Shayegi, S., Ansah, J. et al. 2006. Reservoir Knowledge and Drilling-Benefits Comparison for Underbalanced and Managed Pressure Drilling Operations. Presented at the SPE/IADC Indian Drilling Technology Conference and Exhibition, Mumbai, 16–18 October. SPE-104465-MS. https://doi.org/10.2118/104465-MS.
Gavrilyuk, S. and Fabre, J. 1996. Lagrangian Coordinates for a Drift-Flux Model of a Gas-Liquid Mixture. Int. J. Multiphas. Flow 22 (3): 453–460. https://doi.org/10.1016/0301-9322(95)00085-2.
Godhavn, J.-M. and Knudsen, K. A. 2010. High Performance and Reliability for MPD Control System Ensured by Extensive Testing. Presented at the IADC/SPE Drilling Conference and Exhibition, New Orleans, 2–4 February. SPE-128222-MS. https://doi.org/10.2118/128222-MS.
International Association of Drilling Contractors (IADC). 2011. UBD and MPD Glossary, December 2011.
Lage, A. C. V.M., Fjelde, K. K., and Time, R. W. 2003. Underbalanced Drilling Dynamics: Two-Phase Flow Modeling and Experiments. SPE J. 8 (1): 61–70. SPE-83607-PA. https://doi.org/10.2118/83607-PA.
Lipták, B. ed. 2006. Instrument Engineers’ Handbook: Process Control and Optimization, Vol. 2. Boca Raton, Florida: CRC Press.
Ljung, L. 1999. System Identification: Theory for the User, second edition. Upper Saddle River, New Jersey: Prentice Hall.
Lorentzen, R. J. and Fjelde, K. K. 2005. Use of Slopelimiter Techniques in Traditional Numerical Methods for Multi-Phase Flow in Pipelines and Wells. Int. J. Numer. Meth. Fl. 48 (7): 723–745. https://doi.org/10.1002/fld.952.
Maciejowski, J. M. 2002. Predictive Control With Constraints. Upper Saddle River, New Jersey: Prentice Hall.
Macpherson, J. D., de Wardt, J. P., Florence, F. et al. 2013. Drilling-Systems Automation: Current State, Initiatives, and Potential Impact. SPE Drill & Compl 28 (4): 296–308. SPE-166263-PA. https://doi.org/10.2118/166263-MS.
MathWorks. 2015. Matlab version 8.6 (R2015b). Natick, Massachusetts: MathWorks.
McLennan, J., Carden, R. S., Curry, D. et al. 1997. Underbalanced Drilling Manual. Chicago: Gas Research Institute.
Mendes, P., Normey-Rico, J., Plucenio, A. et al. 2012. Disturbance Estimator Based Nonlinear MPC of a Three Phase Separator. IFAC Proc. 45 (15): 101–106. https://doi.org/10.3182/20120710-4-SG-2026.00060.
Mitchell, R. F. 1988. Dynamic Surge/Swab Pressure Predictions. SPE Drill Eng 3 (3): 325–333. SPE-16156-PA. https://doi.org/10.2118/16156-PA.
Morari, M. and Lee, J. H. 1999. Model Predictive Control: Past, Present and Future. Comput. Chem. Eng. 23 (4–5): 667–682. https://doi.org/10.1016/S0098-1354(98)00301-9.
Nagy, Z. K. and Braatz, R. D. 2003. Robust Nonlinear Model Predictive Control of Batch Processes. AIChE J. 49 (7): 1776–1786. https://doi.org/10.1002/aic.690490715.
Nygaard, G. and Nævdal, G. 2006. Nonlinear Model Predictive Control Scheme for Stabilizing Annulus Pressure During Oil Well Drilling. J. Process Contr. 16 (7): 719–732. https://doi.org/10.1016/j.jprocont.2006.01.002.
Pauchon, C. and Dhulesia, H. 1994. TACITE: A Transient Tool for Multiphase Pipeline and Well Simulation. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 25–28 September. SPE-28545-MS. https://doi.org/10.2118/28545-MS.
Pedersen, T., Godhavn, J.-M., and Schubert, J. 2015. Supervisory Control for Underbalanced Drilling Operations. IFAC-PapersOnline 48 (6): 120–127. https://doi.org/10.1016/j.ifacol.2015.08.019.
Perez-Tellez, C. 2003. Improved Bottomhole Pressure Control for Underbalanced Drilling Operations. PhD dissertation, Louisiana State University, Baton Rouge, Louisiana. http://digitalcommons.lsu.edu/gradschool_dissertations/1636/.
Rehm, B., Schubert, J., Haghshenas, A. et al. ed. 2008. Managed Pressure Drilling, sixth edition. Houston: Gulf Publishing Company.
Rehm, B., Haghshenas, A., Paknejad, A. et al. ed. 2012. Underbalanced Drilling: Limits and Extremes. Houston: Gulf Publishing Company.
Strand, S. and Sagli, J. 2004. MPC in Statoil: Advantages with In-House Technology. Proc., International Symposium on Advanced Control of Chemical Processes (ADCHEM), 97–103.
Sugiura, J., Samuel, R., Oppelt, J. et al. 2015. Drilling Modeling and Simulation: Current State and Future Goals. Presented at the SPE/IADC Drilling Conference and Exhibition, London, 17–19 March. SPE-173045-MS. https://doi.org/10.2118/173045-MS.