On Wednesday December 12, 2018 we have the following 3 presentations in relation to the PhD defense of Tobias Kasper Skovborg Ritschel. Everybody is welcome.
\n10:00-10:45
\nModel Predictive Control with Physics-Based Models for Drilling
John Hedengren
\nBrigham Young University
\n\n
Abstract:
\nThe drilling industry faces challenging market conditions that motivate the use of automation to reduce costs and decrease well manufacturing variability. The objective of this presentation is to motivate automation initiatives that utilize physics-based models for predictive monitoring and control.
\n\n
This presentation explores current progress, challenges, and opportunities to control critical drilling conditions such as downhole pressure in Managed Pressure Drilling (MPD). The 3 essential elements of automation are explored with a perspective on recent advancements in automation due to downhole measurement availability through wired drillpipe.
\n\n
However, only a small fraction of drilling systems currently utilize wired drillpipe. In automated rig systems, there is additional potential to unlock the predictive capabilities of physics-based models to "see" into the near future to optimize and coordinate control actions. A convergence of several key technologies creates an opportunity to use sophisticated mathematical models within automation.
\n\n
A significant challenge is the size of the physics-based models that have too many adjustable parameters or are too slow in simulation to extract actionable information. This presentation shows how fit-for-purpose models can be used directly in the automation solutions.
\n\n
These fit-for-purpose models have unlocked new ways to think about automation in drilling. For example, rate optimization and pressure control have traditionally been separate applications in MPD. Simulation studies suggest significant potential improvement when combining the two applications.
\n\n
\n
10:45-11:30
\nVarious Formulations of Phase Equilibrium of Multicomponent Mixtures for Compositional Reservoir Simulation
\nJiri Mikyska
\nCzech Technical University in Prague
\n\n
Abstract:
\nIn this talk, I am going to present formulations of phase equilibrium of multicomponent mixtures and the way they can be used in the compositional reservoir simulation. The classical formulations of compositional models use pressure, temperature, and overall mixture composition as specification variables (the so-called PTN formulation).
\n\n
Consequently, in compositional modeling the pressure has to be determined before the flash calculation takes place. Possible approaches to this problem are those by Acs et al or by Young and Stevenson. Alternatively, pressure can be determined directly as a part of the flash equilibrium calculation at specified volume, temperature and moles (the so-called VTN formulation).
\n\n
I will discuss our experience gained with such an approach. I will also mention the UVN flash problem (with specified internal energy, volume, and moles) and show how these flash formulations can be described using a unified formulation. Finally, I will discuss applicability of this unified approach in compositional simulation of isothermal and non-isothermal problems.
\n\n
\n
13:00-14:00
\nNonlinear Model Predictive Control for Oil Reservoirs
\nTobias Kasper Skovborg Ritschel
\nTechnical University of Denmark
\n\n
Abstract:
\nThe subject of this thesis is nonlinear model predictive control (NMPC) for closed-loop reservoir management (CLRM). The purpose of NMPC for oil reservoirs management is to compute a field-wide closed-loop feedback control strategy (i.e. an oil production strategy) that optimizes a long-term financial measure of the oil production process, e.g. the total oil recovery or the net present value over the reservoir life-time.
\n\n
We describe algorithms for 1) simulation, 2) state estimation, 3) dynamic optimization, and 4) NMPC of DAEs in this specific semi-explicit form. Numerical methods for simulation, i.e. for numerical solution of initial value problems (IVPs), are central to the state estimation algorithms and the dynamic optimization algorithm (and therefore also to the NMPC algorithm) considered in this thesis.
\n\n
We consider the numerical solution of both deterministic and stochastic IVPs that involve DAEs in the semi-explicit form. We present two approaches for the numerical solution of the deterministic IVPs: 1) a simultaneous approach and 2) a nested approach. Both approaches use Euler’s implicit method. In the simultaneous approach, the differential equations and the algebraic equations are solved simultaneously.
\n\n
In the nested approach, the solution of the algebraic equations is nested into the solution of the differential equations. We present one approach for the numerical solution of the stochastic IVPs. It is a simultaneous approach, and it uses a semi-implicit discretization scheme. We consider the extended Kalman filter (EKF), the unscented Kalman filter (UKF), a particle filter (PF), and the ensemble Kalman filter (EnKF) for state estimation of continuous-discrete DAE systems in the semi-explicit form.
\n\n
Furthermore, we describe an algorithm for gradient-based numerical solution of dynamic optimization problems that involve DAEs in the semi-explicit form. The algorithm uses 1) the single-shooting method and 2) the discrete adjoint method for the computation of gradients. Finally, we describe an NMPC algorithm which combines either of the four state estimation algorithms with the gradient-based dynamic optimization algorithm.
X-ALT-DESC;FMTTYPE=text/html:On Wednesday December 12, 2018 we have the following 3 presentations in relation to the PhD defense of Tobias Kasper Skovborg Ritschel. Everybody is welcome.
\n10:00-10:45
\nModel Predictive Control with Physics-Based Models for Drilling
John Hedengren
\nBrigham Young University
\n\n
Abstract:
\nThe drilling industry faces challenging market conditions that motivate the use of automation to reduce costs and decrease well manufacturing variability. The objective of this presentation is to motivate automation initiatives that utilize physics-based models for predictive monitoring and control.
\n\n
This presentation explores current progress, challenges, and opportunities to control critical drilling conditions such as downhole pressure in Managed Pressure Drilling (MPD). The 3 essential elements of automation are explored with a perspective on recent advancements in automation due to downhole measurement availability through wired drillpipe.
\n\n
However, only a small fraction of drilling systems currently utilize wired drillpipe. In automated rig systems, there is additional potential to unlock the predictive capabilities of physics-based models to "see" into the near future to optimize and coordinate control actions. A convergence of several key technologies creates an opportunity to use sophisticated mathematical models within automation.
\n\n
A significant challenge is the size of the physics-based models that have too many adjustable parameters or are too slow in simulation to extract actionable information. This presentation shows how fit-for-purpose models can be used directly in the automation solutions.
\n\n
These fit-for-purpose models have unlocked new ways to think about automation in drilling. For example, rate optimization and pressure control have traditionally been separate applications in MPD. Simulation studies suggest significant potential improvement when combining the two applications.
\n\n
\n
10:45-11:30
\nVarious Formulations of Phase Equilibrium of Multicomponent Mixtures for Compositional Reservoir Simulation
\nJiri Mikyska
\nCzech Technical University in Prague
\n\n
Abstract:
\nIn this talk, I am going to present formulations of phase equilibrium of multicomponent mixtures and the way they can be used in the compositional reservoir simulation. The classical formulations of compositional models use pressure, temperature, and overall mixture composition as specification variables (the so-called PTN formulation).
\n\n
Consequently, in compositional modeling the pressure has to be determined before the flash calculation takes place. Possible approaches to this problem are those by Acs et al or by Young and Stevenson. Alternatively, pressure can be determined directly as a part of the flash equilibrium calculation at specified volume, temperature and moles (the so-called VTN formulation).
\n\n
I will discuss our experience gained with such an approach. I will also mention the UVN flash problem (with specified internal energy, volume, and moles) and show how these flash formulations can be described using a unified formulation. Finally, I will discuss applicability of this unified approach in compositional simulation of isothermal and non-isothermal problems.
\n\n
\n
13:00-14:00
\nNonlinear Model Predictive Control for Oil Reservoirs
\nTobias Kasper Skovborg Ritschel
\nTechnical University of Denmark
\n\n
Abstract:
\nThe subject of this thesis is nonlinear model predictive control (NMPC) for closed-loop reservoir management (CLRM). The purpose of NMPC for oil reservoirs management is to compute a field-wide closed-loop feedback control strategy (i.e. an oil production strategy) that optimizes a long-term financial measure of the oil production process, e.g. the total oil recovery or the net present value over the reservoir life-time.
\n\n
We describe algorithms for 1) simulation, 2) state estimation, 3) dynamic optimization, and 4) NMPC of DAEs in this specific semi-explicit form. Numerical methods for simulation, i.e. for numerical solution of initial value problems (IVPs), are central to the state estimation algorithms and the dynamic optimization algorithm (and therefore also to the NMPC algorithm) considered in this thesis.
\n\n
We consider the numerical solution of both deterministic and stochastic IVPs that involve DAEs in the semi-explicit form. We present two approaches for the numerical solution of the deterministic IVPs: 1) a simultaneous approach and 2) a nested approach. Both approaches use Euler’s implicit method. In the simultaneous approach, the differential equations and the algebraic equations are solved simultaneously.
\n\n
In the nested approach, the solution of the algebraic equations is nested into the solution of the differential equations. We present one approach for the numerical solution of the stochastic IVPs. It is a simultaneous approach, and it uses a semi-implicit discretization scheme. We consider the extended Kalman filter (EKF), the unscented Kalman filter (UKF), a particle filter (PF), and the ensemble Kalman filter (EnKF) for state estimation of continuous-discrete DAE systems in the semi-explicit form.
\n\n
Furthermore, we describe an algorithm for gradient-based numerical solution of dynamic optimization problems that involve DAEs in the semi-explicit form. The algorithm uses 1) the single-shooting method and 2) the discrete adjoint method for the computation of gradients. Finally, we describe an NMPC algorithm which combines either of the four state estimation algorithms with the gradient-based dynamic optimization algorithm.
URL:https://www.cere.dtu.dk/calendar/2018/12/scientific-talks DTSTAMP:20260513T051700Z UID:{62B89761-6774-449D-B2C7-9D5477F92A7A}-20181212T090000Z-20181212T090000Z LOCATION: Building 303A, Auditorium 049 END:VEVENT END:VCALENDAR