Reservoir characterization in under-balanced drilling with nonlinear Moving Horizon Estimation with manual and automatic control conditions

Abstract This paper discusses the state estimation of the Under-Balanced Drilling (UBD) system in the presence of parametric uncertainties. During this process, the production indices of oil and gas from the reservoir into the well in the manual and automatic control conditions are estimated by employing the nonlinear Moving Horizon Estimation (MHE) based on a low-order lumped (LOL) model. The LOL model has a low computational load and is suitable for reservoir characterization during UBD operations. The estimation algorithms are tested by using a difficult scenario which is created by the OLGA multiphase flow simulator. The simulation results indicate that the nonlinear MHE has a high performance and can identify the production indices of gas and oil. Moreover, the method has the capability to diagnose the rapid variation of the production constant in different conditions, such as working with a manual or automatic controller. The results of the scenario with the swift change in the production index of gas demonstrate that the nonlinear MHE has a higher performance than the Unscented Kalman Filter (UKF). The effect of uncertainties and errors in the reservoir and well parameters on the nonlinear MHE is evaluated. It is revealed that the presence of parametric uncertainty in the rdeservoir pore pressure can significantly affect the estimators' performance.

[1]  Delphine Bresch-Pietri,et al.  An adaptive observer for hyperbolic systems with application to UnderBalanced Drilling , 2014 .

[2]  Tor Arne Johansen,et al.  Regularized and Adaptive Nonlinear Moving Horizon Estimation of Bottomhole Pressure During Oil Well Drilling , 2011 .

[3]  Hongtao Li,et al.  Reservoir Characterization during Underbalanced Drilling of Horizontal Wells Based on Real-Time Data Monitoring , 2014, J. Appl. Math..

[4]  J. Rawlings,et al.  Nonlinear Moving Horizon State Estimation , 1995 .

[5]  Alexey Pavlov,et al.  Drilling seeking automatic control solutions , 2011 .

[6]  Tor Arne Johansen,et al.  Nonlinear Moving Horizon Observer for Estimation of States and Parameters in Under-Balanced Drilling Operations , 2014, HRI 2014.

[7]  Florent Di Meglio,et al.  Transient Modelling of Influx and Observer Implementation for Estimation While Drilling , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[8]  Randal W. Beard,et al.  Parameter estimation for towed cable systems using moving horizon estimation , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Alexey Pavlov,et al.  Adaptive output regulation for offshore managed pressure drilling , 2017 .

[10]  Randi Moe,et al.  The dynamic two-fluid model OLGA; Theory and application , 1991 .

[11]  Olav Gerhard Haukenes Nygaard,et al.  Multivariable process control in high temperature and high pressure environment using non-intrusive multi sensor data fusion , 2006 .

[12]  C. B. Kardolus,et al.  Formation Testing While Underbalanced Drilling , 1997 .

[13]  Daniel Moos,et al.  Reservoir Characterization Begins at First Contact With the Drill Bit , 2012 .

[14]  Antonios Armaou,et al.  Output feedback receding horizon regulation via moving horizon estimation and model predictive control , 2018, Journal of Process Control.

[15]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[16]  Altan Onat,et al.  A Novel and Computationally Efficient Joint Unscented Kalman Filtering Scheme for Parameter Estimation of a Class of Nonlinear Systems , 2019, IEEE Access.

[17]  Tor Arne Johansen,et al.  Moving horizon observer with regularisation for detectable systems without persistence of excitation , 2011, Int. J. Control.

[18]  Tor Arne Johansen,et al.  State and Parameter Estimation of a Drift-Flux Model for Underbalanced Drilling Operations , 2017, IEEE Transactions on Control Systems Technology.

[19]  Tor Arne Johansen,et al.  Estimation of States and Parameters of a Drift-Flux Model with Unscented Kalman Filter , 2015 .

[20]  S. Mylvaganam,et al.  Evaluating nonlinear Kalman filters for parameter estimation in reservoirs during petroleum well drilling , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[21]  Rudolph van der Merwe,et al.  The Unscented Kalman Filter , 2002 .

[22]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[23]  Rolf Johan Lorentzen,et al.  Reservoir Characterization During Underbalanced Drilling (UBD): Methodology and Active Tests , 2006 .

[24]  Rolf Johan Lorentzen,et al.  Tuning of parameters in a two-phase flow model using an ensemble Kalman filter , 2003 .

[25]  Feng Ding,et al.  Combined state and parameter estimation for a bilinear state space system with moving average noise , 2018, J. Frankl. Inst..

[26]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[27]  Tor Arne Johansen,et al.  Evaluation of Lyapunov-based Adaptive Observer using Low-Order Lumped Model for Estimation of Production Index in Under-balanced Drilling , 2015 .

[28]  Steinar Evje,et al.  Hybrid flux-splitting schemes for a two-phase flow model , 2002 .

[29]  Tor Arne Johansen,et al.  Design and Comparison of Constrained MPC With PID Controller for Heave Disturbance Attenuation in Offshore Managed Pressure Drilling Systems , 2014 .

[30]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[31]  Jing Zhou,et al.  Automatic model-based control scheme for stabilizing pressure during dual-gradient drilling , 2011 .

[32]  Ole Morten Aamo,et al.  Fit-for-Purpose Modeling for Automation of Underbalanced Drilling Operations , 2014 .

[33]  John D. Hedengren,et al.  Real time model identification using multi-fidelity models in managed pressure drilling , 2017, Comput. Chem. Eng..

[34]  Tor Arne Johansen,et al.  Regularized Nonlinear Moving-Horizon Observer With Robustness to Delayed and Lost Data , 2013, IEEE Transactions on Control Systems Technology.

[35]  Tor Arne Johansen,et al.  Reservoir characterization in under-balanced drilling using low-order lumped model , 2018 .

[36]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[37]  G. Nygaard,et al.  Modelling two-phase flow for control design in oil well drilling , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[38]  Rolf Johan Lorentzen,et al.  Reservoir Characterization during UBD: Methodology and Active Tests , 2003 .

[39]  F. Angulo,et al.  A new approach to state estimation for uncertain linear systems in a moving horizon estimation setting , 2016, Int. J. Autom. Comput..

[40]  Tor Arne Johansen,et al.  Design and comparison of adaptive estimators for Under-balanced Drilling , 2014, 2014 American Control Conference.

[41]  Jicheng Liu,et al.  Moving horizon estimation for multirate systems with time-varying time-delays , 2019, J. Frankl. Inst..

[42]  Wendy Kneissl.  Reservoir Characterization Whilst Underbalanced Drilling , 2001 .