Ph.D Defense by Tobias Kasper Skovborg Ritschel: Nonlinear Model Predictive Control for Oil Reservoirs

Wednesday 12 December 2018, 13.00 – 16.00, The Technical University of Denmark, Building 303A, room 49

Principal supervisor: Associate Professor John Bagterp Jørgensen, DTU Compute.
Co-Supervisors: Associate Professor Niels Kjølstad Poulsen, DTU Compute and Freelance computational scientist Andrea Capolei.

Examiners: Associate Professor Mirza Karamehmedovic, DTU Compute.
Associate Professor John Hedengren, Brigham Young University, United States of America.
Associate Professor Jiri Mikyška, Czech Technical University in Prague, Czech Republic.

Moderator: Assistant Professor Dimitri Boiroux, DTU Compute

Oil remains the world’s leading fuel, and it is expected to account for a significant part of the world’s energy consumption for several decades. However, current recovery techniques do not recover all of the oil that is present in the oil reservoirs. Furthermore, the production of oil from a given reservoir may be uneconomical depending on the oil price which is very volatile.
In this PhD project, we consider nonlinear model predictive control (NMPC) for improving the economics of oil recovery processes. The objective of NMPC for oil reservoirs management is to compute a field- wide closed-loop feedback control strategy which optimizes a long-term financial measure of the recovery process, e.g. the total amount of recovered oil or the net present value over the life-time of the reservoir. Whenever new measurements become available, the NMPC algorithm uses 1) state estimation to estimate the state of the reservoir (as well as parameters in the model) and 2) dynamic optimization to compute a new updated field-wide production strategy. Reservoir flow models are used in both the state estimation and the dynamic optimization. Accurate reservoir flow models are therefore key to the effectiveness of the NMPC algorithm.
In this work, we present thermodynamically rigorous models of thermal (varying temperature) and isothermal (constant temperature) compositional reservoir flow processes. Models of such processes are based on two main principles: 1) conservation of mass and energy, and 2) phase equilibrium. The conservation of energy is related to the first law of thermodynamics, and phase equilibrium is related to the second law of thermodynamics. The phase equilibrium problems that are relevant to the thermal and the isothermal models are the UV flash and the VT flash, respectively. We formulate these phase equilibrium problems as equality cons trained optimization problems and the phase equilibrium conditions as the first order optimality conditions. Furthermore, we demonstrate that the thermal and the isothermal models are in a semi-explicit differential-algebraic form, and we formulate algorithms for state estimation, dynamic optimization, and NMPC of systems in this form. These algorithms are relevant to other phase equilibrium processes as well because it is natural to model such processes using differential-algebraic equations in the same semi-explicit form.


A copy of the PhD thesis is available for reading at the department

All are welcome


Wed 12 Dec 18
13:00 - 16:00


DTU Compute


DTU, Building 303A, room 49