dc.contributor.advisor | Foss, Bjarne | |
dc.contributor.advisor | Camponogara, Eduardo | |
dc.contributor.author | Codas Duarte, Andrés | |
dc.date.accessioned | 2016-03-30T11:30:03Z | |
dc.date.available | 2016-03-30T11:30:03Z | |
dc.date.issued | 2016 | |
dc.identifier.isbn | 978-82-326-1483-7 | |
dc.identifier.issn | 1503-8181 | |
dc.identifier.uri | http://hdl.handle.net/11250/2383090 | |
dc.description.abstract | This thesis covers methods for optimization of oil production in three time-scales. In the
long-term perspective, years, it is desired to maximize the economic return of the field
operation, or alternatively, it is desired to maximize the oil recovery factor. In the middleterm
perspective, days, optimal scheduling and allocation of the production facilities are
desired. In the short-term perspective, minutes, it is desired to maintain the process
operating at a stable optimal set-point. The integration of the optimization solutions
that tackle each of the layers independently is a formidable challenge. This requires the
development of mathematical models and efficient optimization algorithms to deliver
solutions in real-time.
This research focuses on efficient optimization algorithms and suitable simulation
models for oil production optimization. The emphasis is on the integration of the decision
process for different time-scales. To this end, this research studies each individual
time-scale and proposes tools that lead to the desired integration. The work is divided
into five parts.
Chapter 2 formulates and solves the reservoir control optimization problem applying
the direct multiple shooting (MS) method. This method divides the prediction horizon
into smaller intervals which can be evaluated in parallel. Further, output constraints
are easily established on each interval boundary and as such hardly affect computation
time. This opens new opportunities to include state constraints on a much broader scale
than what is common in reservoir optimization today. However, multiple shooting deals
with a large number of variables since it decides on the boundary state variables of each
interval. Therefore, we exploit the structure of the reservoir simulator to conceive a
variable reduction technique to solve the optimization problem with a reduced sequential
quadratic programming algorithm. We discuss the optimization algorithm building
blocks and focus on structure exploitation and parallelization opportunities. To demonstrate
the method’s capabilities to handle output constraints, the optimization algorithm
is interfaced to an open-source reservoir simulator. Then, based on a widely used reservoir
model, we evaluate performance, especially related to output constraints. The performance
of the proposed method is qualitatively compared to a conventional method.
Chapter 3 solves a black-oil reservoir optimal control problem with MS. The black-oil
fluid model, considering volatile oil or wet gas, requires a change of primary variables
for simulation. This is a consequence of the absence of a fluid phase due to dissolution
or vaporization. Therefore, reservoir simulators parametrize the states with an augmented vector and select primary variables accordingly. However, the augmented state
vector and the corresponding change of primary variables are not suitable for the application
of MS because the optimization problem formulation must change according to
the change of variables. Thus, we propose a minimal state-space variable representation
that prevents this shortcoming. We show that there is a bijective mapping between the
proposed state-space representation and the augmented state-space. The minimal representation
is used for optimization and the augmented representation for simulation,
thereby keeping the simulator implementation unchanged. Therefore, the proposed solution
is not invasive. Finally, the application of the method is exemplified with benchmark
cases involving live oil or wet gas. Both examples emphasize the requirement of
output constraints which are efficiently dealt by the MS method.
The production life of oil reservoirs starts under significant uncertainty regarding the
actual economical return of the recovery process due to the lack of oil field data. Consequently,
investors and operators make management decisions based on a limited and
uncertain description of the reservoir. Chapter 4 proposes a new formulation based on
MS for robust optimization of reservoir well controls. This formulation exploits coherent
risk measures, a concept traditionally used in finance, to deal with the uncertainty.
A variable elimination procedure allows to solve this problem in a reduced space and
an active-set method helps to handle a large set of inequality constraints. Finally, we
demonstrate the application of constraints to limit the risk of water production peaks on
a standard test case.
Chapter 5 addresses the middle-term perspective and develops a framework for integrated
production optimization of complex oil fields such as Petrobras’ Urucu field,
which has a gathering system with complex routing degree of freedom, limited processing
capacity, pressure constraints, and wells with gas-coning behavior. The optimization
model integrates simplified well deliverability models, vertical lift performance relations,
and the flowing pressure behavior of the surface gathering system. The framework relies
on analytical models which are history matched to field data and simulators tuned
to reflect operating conditions. A Mixed-Integer Linear Programming (MILP) problem
is obtained by approximating these models with piecewise-linear functions. Procedures
are developed to obtain simplified piecewise-linear approximations that ensure a given
accuracy with respect to complex and precise models. Computational experiments show
that the integrated production optimization problem can be solved sufficiently fast for
real-time applications. Further, the operational conditions calculated with the simplified
models during the optimization process match the precise models.
Chapter 6 studies the short-term problem and presents control and optimization of
a network consisting of two gas-lifted oil wells, a common pipeline-riser system and a
separator. The gas-lifted oil wells may be open-loop unstable. The regulatory layer stabilizes
the system by cascade control of wellhead pressure measurements without needing
bottom hole sensing devices. An economic Nonlinear Model Predictive Control (NMPC)
based on MS is applied for optimization of the network operations. The optimization layer thus provides optimal settings for the regulatory controllers. The control structure
has been validated by using the realistic OLGA simulator as the process, and using
simplified models for Kalman filtering and the NMPC design. The simplified models are
implemented in Modelica and fit to the OLGA model to represent the main dynamics
of the system. The proposed two-layer controller was able to stabilize the system and
increase the economical outcome. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | NTNU | nb_NO |
dc.relation.ispartofseries | Doctoral thesis at NTNU; | |
dc.relation.ispartofseries | ;2016:72 | |
dc.relation.haspart | Codas Duarte, Andres; Foss, Bjarne Anton; Camponogara, Eduardo.
Output-Constraint Handling and Parallelization for Oil-Reservoir Control Optimization by Means of Multiple Shooting. SPE Journal 2015 ;Volum 20.(04) s. 856-871 -
Is not included due to copyright avialable at <a href="http://dx.doi.org/10.2118/174094-pa" target="_blank"> http://dx.doi.org/10.2118/174094-pa</a> | |
dc.relation.haspart | Codas, A. et al. (2016a). ‘Black-oil minimal fluid state parametrization for constrained
reservoir control optimization’. In: Journal of Petroleum Science and Engineering
143, pp. 35–43.
<a href="http://dx.doi.org/10.1016/j.petrol.2016.01.034" target="_blank"> http://dx.doi.org/10.1016/j.petrol.2016.01.034</a>
© 2016 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.relation.haspart | Codas, A. et al. (2016b). ‘Multiple Shooting applied to robust reservoir control optimization
including output constraints on coherent risk measures.’ | |
dc.relation.haspart | Codas Duarte, Andres; Campos, Sthener; Camponogara, E; Gunnerud, Vidar; Sunjerga, Snjezana.
Integrated production optimization of oil fields with pressure and routing constraints: The Urucu field. Computers and Chemical Engineering 2012 ;Volum 46. s. 178-189
<a href="http://dx.doi.org/10.1016/j.compchemeng.2012.06.016" target="_blank"> http://dx.doi.org/10.1016/j.compchemeng.2012.06.016</a>
© 2012 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.relation.haspart | Codas, A. et al. (2016c). ‘A two-layer structure for stabilization and optimization of
an oil gathering network’. In: 11th IFAC Symposium on Dynamics and Control of Process
Systems, including Biosystems. | |
dc.title | Contributions to production optimization of oil reservoirs | nb_NO |
dc.type | Doctoral thesis | nb_NO |
dc.subject.nsi | VDP::Technology: 500::Information and communication technology: 550::Technical cybernetics: 553 | nb_NO |