Well control and field development optimization using new surrogate treatments
Abstract/Contents
- Abstract
- The optimization of reservoir performance is a key component of reservoir management. This optimization can involve determining well settings for existing wells, or finding the number, types and locations of new wells, along with their settings. In either case, the goal is to maximize a predefined cost function, such as net present value (NPV) or cumulative oil production. Because large numbers of flow simulations are typically required for these optimizations, they can be computationally demanding. This is especially true for field development optimization, where new well types, locations and controls are determined, since this entails the solution of a mixed integer nonlinear programming problem. For either type of optimization, the availability of fast (and sufficiently accurate) surrogate or proxy treatments can potentially accelerate the computations substantially. The development and application of such surrogate treatments is the goal of this thesis. We first introduce a new two-step surrogate treatment (ST) that reduces the computational expense associated with well control optimization. The method is applicable for oil production via waterflood, with well rates optimized at a single control period. The two-step ST entails two separate optimizations, which can both be performed very efficiently. In the first optimization, optimal well-rate ratios (i.e., the fraction of total injection or production associated with each well) are determined such that a measure of velocity variability over the field is minimized, leading to more uniform sweep. In the second step, overall injection and production rates are determined. The flow physics in the first step is highly simplified, while the actual physical system is simulated in the second step. Near-globally-optimal results can be determined in both steps (though this does not mean we find the global optimum for the actual problem), as the first optimization is posed as a QP problem, and the second step entails just a single optimization variable. Under full parallelization, the overall elapsed time for the ST corresponds to the runtime for 1-2 full-order simulations. Optimization results are presented for multiple well configurations, including 2D and 3D channelized models. Comparisons with formal optimization procedures (mesh adaptive direct search or MADS, and an adjoint-gradient method) are also conducted. Three different fluid mobility ratios (M=1, 3 and 5) are considered. Optimization results for well control optimization demonstrate that the two-step ST provides results in reasonable agreement with those from MADS and adjoint-gradient methods, with speedups of a factor of 5 or more. We next adapt this two-step ST for use in field development optimization problems. The basic ST for this case is an even more efficient version of the ST developed for optimizing well rates. This ST is incorporated into the inner loop of a nested particle swarm optimization (PSO) framework. Simplified physics (specifically the use of unit-mobility-ratio flow and transport) is considered in this initial PSO step. Full problem physics is introduced in subsequent optimization steps. Three approaches are considered for these steps - one involves the application of a surrogate treatment used for well control optimization, and two entail the use of MADS. The ST-based procedures are evaluated for two different 3D problems involving waterflood (with mobility ratios of 2 and 5) and water-alternating-gas (WAG) injection. The surrogate treatments are compared to standard approaches involving PSO, MADS, and a PSO-MADS hybrid. Extensive optimization results demonstrate that the ST-based methods provide consistent improvement in optimizer performance. For example, in the WAG case, the ST-based approach gives an optimal net present value that is 3.2% higher than that achieved using standard PSO-MADS, while also providing a computational speedup of a factor of 2.4.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ullmann de Brito, Daniel |
|---|---|
| Degree supervisor | Durlofsky, Louis |
| Thesis advisor | Durlofsky, Louis |
| Thesis advisor | Horne, Roland N |
| Thesis advisor | Volkov, Oleg |
| Degree committee member | Horne, Roland N |
| Degree committee member | Volkov, Oleg |
| Associated with | Stanford University, Department of Energy Resources Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Daniel Ullmann de Brito. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Daniel Ullmann de Brito
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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