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Joint Optimization of Well Placement and Controls for Petroleum Field Development
Get PDFIn this thesis we optimize the drilling location and operational controls of wells in a joint
manner to improve the overall development strategy for a petroleum field. In particular,
in this thesis we treat the integrated problem of searching for an improved well placement
configuration while also taking into account the control settings of the production
and/or injector wells planned for the development of the hydrocarbon asset. In oil field
development, the well placement and well control problems are commonly performed in a
sequential manner. However, this type of sequential approach cannot be expected to yield
optimal solutions because it relies on handling well production controls using heuristic
techniques during the well placement part of the procedure. In this work, we develop a
nested (joint) optimization approach that seeks to capture the interdependency between
the well configuration and the associated controls during the optimization search.
This thesis summarizes the development of the joint approach; from establishing the
methodology while using relatively simple cases and performing thorough comparisons
against sequential approaches, to further extending and finally testing the methodology
using a real field case model. This progression naturally divides the work in this thesis
into two parts with different research focus. The first part of this work (Chapter 2)
focuses chiefly on creating proper definitions and on establishing the proposed methodology
against common approaches. The second part of this thesis (Chapters 3 and 4), on
the other hand, focuses mainly on applying the developed methodology within a real field
case scenario involving the North Sea Martin Linge oil reservoir. The dual aim of this
application work is both to further develop the methodology, and to produce and test optimization
solutions that may serve as decision–support to engineering efforts within the
development work process of the Martin Linge field.
Chapter 2 establishes the core of the methodology followed in this thesis. This chapter
introduces the joint and sequential approaches as different ways to solve for the coupled
well placement and control problem. The joint approach embeds the well control
optimization within the search for optimum well placement configurations. Derivative–
free methods based on pattern search are used to solve for the well–positioning part of
the problem, while the well control optimization is solved by sequential quadratic programming
using gradients efficiently computed through adjoints. Compared to reasonable
sequential approaches, the joint optimization yields a significant increase in net present
value of up to 20%. Compared to the sequential procedures, though, the joint approach
requires about an order of magnitude increase in the total number of reservoir simulations
performed during optimization. This increase, however, is somewhat mitigated by the parallel implementation of some of the pattern search algorithms used in this work.
Chapter 3 focuses on extending and applying the methodology developed in the previous
chapter within a real field development scenario. A work process loop is set up to
guide the entire application effort; from work model validation and problem definition,
to optimization effort and solution testing. Results from the optimization effort, using an
approximated work model, yield a mean increase in FOPT of close to 33% for solutions
developed using the joint approach. In comparison, solutions developed using a sequential
approach yield a mean increase in FOPT of almost 26%. Moreover, cost function
evolution data for the joint runs using the field case work model, show that the performance
of less promising locations during the well placement search may be improved
significantly due to the embedded control routine. This supports the notion that the nested
routine may contribute to a smoothing of the outer loop optimization surface with respect
to the well placement variables, and that this smoothing may add some robustness to the
well placement search conducted by the joint approach. Furthermore, as seen previously
in Chapter 2, the cost of the joint approach is still substantially higher compared to the
computation required by the sequential alternatives. For the application work in Chapter
3, the mean total number of reservoir simulations required by the joint runs is almost 7
times higher than the mean total number of reservoir simulations needed by the sequential
runs.
In Chapter 4, the well placement solutions obtained using the work model are transferred
and tested on to the original field case model for the Martin Linge oil reservoir. A
main result from the overall testing shows that those well placement configurations with
B wells that aggressively target the eastern lobe of the Martin Linge oil reservoir yield
the greatest increases in field oil production total. Furthermore, the various well placement
solutions are tested for two realistic field development considerations that were not
included in the optimization effort, i.e., the original larger production time frame and a
multiple realizations field case scenario. Compared to the initial well configuration for the
single–realization case, the best–performing well placement solution yields an increase in
field oil production total of, respectively, 25.5% for the time frame used in the optimization
procedure, and of 13% for the original (larger) production horizon. However, we note
that we performed the optimization effort using only a single realization. Consequently,
the solutions developed from this procedure are observed to lose most of their gains once
these well configurations are implemented for both the larger production time frame and
over the multiple realization case. This final test result underscores the importance for
future work of both improving the computational performance of the overall optimization
procedure (e.g., through surrogate techniques) and of including geological uncertainty
within the search routine
FieldOpt: Enhanced Software Framework for Petroleum Field Optimization - Development of Software Support System for the Integration of Oil Production Problems with Optimization Methodology
No full textThis thesis concerns the development of FieldOpt, a software framework that aims at being a common platform for MSc. and Ph.D. students of optimization theory and petroleum engineering to conduct research. With FieldOpt, we seek to simplify and speed up the process of developing and applying new optimization methodologies to interesting petroleum cases. We achieve this by structuring the source code in a way that facilitates modifications and additions through the use of abstract classes and clearly defined interfaces, and by implementing process-level parallelization.
We present some background on topics relevant to our work. We start with a general description of optimization algorithms, in particular pattern search algorithms, focusing on the compass search algorithm that we implemented in FieldOpt. Other key topics are reservoir simulation; parallel computing, focusing on process-level parallelization in distributed systems; and software architecture, which is crucial to the implementation of any software framework. We then go on to describe how FieldOpt was implemented, using class- and sequence diagrams to describe the structure of the code and the sequence of instructions during execution. We also present a case study examining the correctness and performance of our implementation under various conditions.
The optimization interface we created is simple and well defined. It should be flexible enough to facilitate implementation of most pattern search algorithms, but it will likely need some modifications in the future. We find that our implementation exhibits exceptional performance for large problems and that the performance scales very well when a large number of processors are available. When run in parallel, our implementation reaches a solution more than 90% faster than when run in serial for large problems. However, we also identify some issues linked to our implementation of workload scheduling, caused by irregular behavior in the reservoir simulator. These irregularities may cause the program to have a very long execution time for some problems, and a systematic handling of them should be implemented
Implementation of Trust-region Algorithm for Well Placement Optimization in FieldOpt Framework
No full textOil field development is a challenging engineering task that involves optimization
problems such as the types, numbers, scheduling, location, and controls of wells.
Optimization algorithm is the keyfactor for optimization model. Each algorithm
has its own strengths and weaknesses, hence we need to apply efficent and
effective methodologies to solve optimizatiom problems. This thesis concerns
the implementation of local search trust-region derivative-free algorithm for well
placement into the FieldOpt optimization framework developed at the Petroleum
Cybernetics Group, NTNU.
The C++-written FieldOpt software program serves as a framework and
enabler for algorithm development aimed at petroleum problems that require the
efficient computations of reservoir simulations for cost function evaluation. The
reservoir simulation works as a Black Box , but the FieldOpt software allows for
a straightforward one-to-one communication between the algorithm logic and the
multiple driver files and settings needed by the simulator.
We start with general introduction of well placement optimization and widely
used algorithms for optimizaton problems. Gradients with respect to well place-
ment variables are commonly not available and therefore derivative-free methods
are often proposed for well placement optimization. The trust region method
implemented in this work does not require cost function derivatives, but instead
constructs surrogate models of the objective function obtained during the optimiza-
tion process. Our approach is based on building a quadratic interpolation model,
which reasonably reflects the local behavior of the original objective function in a
subregion.
After implementation of the algorithm in FieldOpt, we apply test functions
to validate the performance of the algorithm. One of them is the plated-shaped
Matyas function and the other one is the valley-shaped Rosen-brock function.
We used Cauchy point calculation and Dogleg method to solve the optimization
problem and analyzed their convergence properties. The results show that the
convergence of the Cauchy point algorithm by taking the steepest descent direction
is inefficient in some cases. A future improvement could be achieved by using
the Dogleg method. We also notice that values of predetermined parameters
can affect algorithm performance. Therefore, it is also important to choose a
proper parameter value to improve the convergence of method. Then, we apply
the model-based trust-region method to solve some well placement optimization
cases, for exapmle single producer or five-spot model
Implementation of Linear Constraint Handling Techniques for Generating Set Search Method for Well Placement Optimization Problem on FieldOpt Platform
Get PDFLiterature review shows that few researchers have focused on GSS method to solve well placement optimization problem and even fewer researchers have approximated the feasible domain with piecewise linear constraints. In our work, we have succeeded in implementing Linear Constraint Handling Techniques for GSS Method for Well Placement Optimization Problem on FieldOpt Platform. According to our test, by configuring larger expansion and contraction factor, the problem of getting trapped into local optimum can be alleviated, as well as using Compass pattern instead of the Fast pattern. Also, by introducing linear constraints to divide the feasible region out, the total number of simulations can be reduced significantly with the same, or even better solution. Finally, the application of our algorithm in OLYMPUS model increases the oil production significantly, which indicates a good performance and applicability of our algorithm
Implementation of Trust-region Algorithm for Well Placement Optimization in FieldOpt Framework
Get PDFOil field development is a challenging engineering task that involves optimization
problems such as the types, numbers, scheduling, location, and controls of wells.
Optimization algorithm is the keyfactor for optimization model. Each algorithm
has its own strengths and weaknesses, hence we need to apply efficent and
effective methodologies to solve optimizatiom problems. This thesis concerns
the implementation of local search trust-region derivative-free algorithm for well
placement into the FieldOpt optimization framework developed at the Petroleum
Cybernetics Group, NTNU.
The C++-written FieldOpt software program serves as a framework and
enabler for algorithm development aimed at petroleum problems that require the
efficient computations of reservoir simulations for cost function evaluation. The
reservoir simulation works as a Black Box , but the FieldOpt software allows for
a straightforward one-to-one communication between the algorithm logic and the
multiple driver files and settings needed by the simulator.
We start with general introduction of well placement optimization and widely
used algorithms for optimizaton problems. Gradients with respect to well place-
ment variables are commonly not available and therefore derivative-free methods
are often proposed for well placement optimization. The trust region method
implemented in this work does not require cost function derivatives, but instead
constructs surrogate models of the objective function obtained during the optimiza-
tion process. Our approach is based on building a quadratic interpolation model,
which reasonably reflects the local behavior of the original objective function in a
subregion.
After implementation of the algorithm in FieldOpt, we apply test functions
to validate the performance of the algorithm. One of them is the plated-shaped
Matyas function and the other one is the valley-shaped Rosen-brock function.
We used Cauchy point calculation and Dogleg method to solve the optimization
problem and analyzed their convergence properties. The results show that the
convergence of the Cauchy point algorithm by taking the steepest descent direction
is inefficient in some cases. A future improvement could be achieved by using
the Dogleg method. We also notice that values of predetermined parameters
can affect algorithm performance. Therefore, it is also important to choose a
proper parameter value to improve the convergence of method. Then, we apply
the model-based trust-region method to solve some well placement optimization
cases, for exapmle single producer or five-spot model
On the Importance of Expert Knowledge in Well Placement Optimisation
Get PDFAs of today, well placement optimisation in field development planning is most commonly based on the trial-and-error approach that requires the evaluation of numerous field development scenarios. This approach is not particularly efficient because it requires a lot of repetitive work to be done by reservoir engineers. Moreover, such approach can lead to overlooked opportunities and suboptimal well placement, which, in turn, can have a negative economic impact on a project in the long-term perspective.
This work describes an effort to better understand the role of reservoir engineering expert knowledge in well placement optimisation at the stage of field development planning and figure out whether an appropriate choice of optimiser parameters can help reduce the dependence on experience-based knowledge. The research question is thus formulated as:
What is the role of expert knowledge in the process of well placement optimisation, and how to reduce the dependence on such knowledge?
An experimental approach is taken. Numerical experiments are conducted on a two-dimensional reservoir model to understand how the quality of initial approximation and two main optimiser parameters affect the optimisation results.
A purpose-specific optimiser-simulator interface is developed to integrate the HOPSPACK optimiser with the ECLIPSE reservoir simulator.
The interpretation of numerical experiments leads to the following two major conclusions for this particular two-dimensional optimisation problem:
Reservoir engineering expert knowledge plays an important role in the process of well placement optimisation by providing a good quality initial approximation;
A proper choice of the optimiser parameters that allow for a wide exploration of the search space may significantly reduce the dependence on such expert knowledge.
It is demonstrated that for the relatively simple reservoir model considered in this study, optimisation techniques can enhance the well placement optimisation workflow. Further research focused on more complex three-dimensional reservoir models may contribute to dispelling the general scepticism towards the use of optimisation techniques for real life situations
Implementation of Particle Swarm Optimization Algorithm within FieldOpt Optimization Framework - Application of the algorithm to well placement optimization
Get PDFIn the present work, particle swarm optimization algorithm is applied in well placement
optimization problem using FieldOpt, a software framework that aims at being common
platform for field development optimization. Different types of particle swarm optimization algorithm have been developed and integrated inside FieldOpt optimization framework in order to increase the capabilities of the framework for field development optimization problems. The algorithm that has been implemented in this work is simple and flexible and it can be used easily as hybrid with other algorithms in the future. We have applied particle swarm optimization algorithm to optimize the locations of vertical and horizontal wells in simple and realistic reservoir models. A dynamic penalty function was coupled inside the algorithm in order to treat the non-linear constraints, such as well length, inter-well distance and reservoir boundary constraints.
We first apply the algorithm on a simple two dimensional five spot model that consists of four vertical injectors and one horizontal producer. We run particle swarm optimization in order to check the performance of the algorithm and determine optimal parameters by performing a sensitivity analysis. Three cases are run on the simple model and the cases vary in terms of the type of constraints implemented. In all the cases, we use fixed control parameters for injectors and producers. In the first case, we optimize the location of one production well that is subject to the well length constraint only. In the second case, we use both well length and reservoir boundary constraints. In the third case, we use all the constraints in order to optimize the location of two horizontal producers. The results suggest that the algorithm converged to an optimum solution with given constraints and assumptions for the simple five spot model.
After getting satisfactory results on the simple reservoir model, we apply particle swarm optimization on a new benchmark known as OLYMPUS. This is a new field development
challenge that is launched by TNO (Netherlands Organization for Applied Scientific Re-
search) within the context of the ISAPP (International Scientific Association for Probiotics and Prebiotics) research program. The reservoir model is characterized with faults, horizontal barriers across the different zones and high permeability channels, which makes the optimization problem more complex than the simple five spot model. The reference operating strategy of the field consists of six injectors and eight producers that are controlled by a pressure constraint. We optimize the locations of first five production wells, while fixing the locations and operating parameters of the other wells as same as in the reference case. Because of high computational demand and time limitations, we could not use enough simulations to reach an optimum solution in this case. However, we compare the optimization runs and give suggestions for future work for the well placement part of the OLYMPUS field development plan
Implementation of Linear Constraint Handling Techniques for Generating Set Search Method for Well Placement Optimization Problem on FieldOpt Platform
No full textLiterature review shows that few researchers have focused on GSS method to solve well placement optimization problem and even fewer researchers have approximated the feasible domain with piecewise linear constraints. In our work, we have succeeded in implementing Linear Constraint Handling Techniques for GSS Method for Well Placement Optimization Problem on FieldOpt Platform. According to our test, by configuring larger expansion and contraction factor, the problem of getting trapped into local optimum can be alleviated, as well as using Compass pattern instead of the Fast pattern. Also, by introducing linear constraints to divide the feasible region out, the total number of simulations can be reduced significantly with the same, or even better solution. Finally, the application of our algorithm in OLYMPUS model increases the oil production significantly, which indicates a good performance and applicability of our algorithm
FieldOpt: Enhanced Software Framework for Petroleum Field Optimization - Development of Software Support System for the Integration of Oil Production Problems with Optimization Methodology
Get PDFThis thesis concerns the development of FieldOpt, a software framework that aims at being a common platform for MSc. and Ph.D. students of optimization theory and petroleum engineering to conduct research. With FieldOpt, we seek to simplify and speed up the process of developing and applying new optimization methodologies to interesting petroleum cases. We achieve this by structuring the source code in a way that facilitates modifications and additions through the use of abstract classes and clearly defined interfaces, and by implementing process-level parallelization.
We present some background on topics relevant to our work. We start with a general description of optimization algorithms, in particular pattern search algorithms, focusing on the compass search algorithm that we implemented in FieldOpt. Other key topics are reservoir simulation; parallel computing, focusing on process-level parallelization in distributed systems; and software architecture, which is crucial to the implementation of any software framework. We then go on to describe how FieldOpt was implemented, using class- and sequence diagrams to describe the structure of the code and the sequence of instructions during execution. We also present a case study examining the correctness and performance of our implementation under various conditions.
The optimization interface we created is simple and well defined. It should be flexible enough to facilitate implementation of most pattern search algorithms, but it will likely need some modifications in the future. We find that our implementation exhibits exceptional performance for large problems and that the performance scales very well when a large number of processors are available. When run in parallel, our implementation reaches a solution more than 90% faster than when run in serial for large problems. However, we also identify some issues linked to our implementation of workload scheduling, caused by irregular behavior in the reservoir simulator. These irregularities may cause the program to have a very long execution time for some problems, and a systematic handling of them should be implemented
On the Importance of Expert Knowledge in Well Placement Optimisation
No full textAs of today, well placement optimisation in field development planning is most commonly based on the trial-and-error approach that requires the evaluation of numerous field development scenarios. This approach is not particularly efficient because it requires a lot of repetitive work to be done by reservoir engineers. Moreover, such approach can lead to overlooked opportunities and suboptimal well placement, which, in turn, can have a negative economic impact on a project in the long-term perspective.
This work describes an effort to better understand the role of reservoir engineering expert knowledge in well placement optimisation at the stage of field development planning and figure out whether an appropriate choice of optimiser parameters can help reduce the dependence on experience-based knowledge. The research question is thus formulated as:
What is the role of expert knowledge in the process of well placement optimisation, and how to reduce the dependence on such knowledge?
An experimental approach is taken. Numerical experiments are conducted on a two-dimensional reservoir model to understand how the quality of initial approximation and two main optimiser parameters affect the optimisation results.
A purpose-specific optimiser-simulator interface is developed to integrate the HOPSPACK optimiser with the ECLIPSE reservoir simulator.
The interpretation of numerical experiments leads to the following two major conclusions for this particular two-dimensional optimisation problem:
Reservoir engineering expert knowledge plays an important role in the process of well placement optimisation by providing a good quality initial approximation;
A proper choice of the optimiser parameters that allow for a wide exploration of the search space may significantly reduce the dependence on such expert knowledge.
It is demonstrated that for the relatively simple reservoir model considered in this study, optimisation techniques can enhance the well placement optimisation workflow. Further research focused on more complex three-dimensional reservoir models may contribute to dispelling the general scepticism towards the use of optimisation techniques for real life situations
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