A Two-Level Strategy to Realize Life-Cycle Production Optimization in an Operational Setting

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1057-1066 ◽  
Author(s):  
G.M. M van Essen ◽  
P.M.J.. M.J. Van den Hof ◽  
J.-D.. -D. Jansen
Keyword(s):  
Life Cycle ◽  
Reference Values ◽  
Large Scale ◽  
Production Optimization ◽  
Data Driven ◽  
Model Errors ◽  
Reservoir Model ◽  
Life Cycle Optimization ◽  
Upper Level

Summary We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level, these profiles are considered as set points (reference values) for a tracking control algorithm, also known as a model predictive controller (MPC), to optimize the production variables over a short moving horizon on the basis of a simple data-driven model. In the process industry such a two-level approach is a well-known strategy to correct for small local disturbances that may have a negative (cumulative) effect on the long-term production strategy. We used a conventional reservoir simulator with gradient-based optimization functionality to perform the life-cycle optimization. Next, we applied this long-term strategy to a reservoir model, representing the truth, with somewhat different geological characteristics and near-wellbore characteristics not captured in the reservoir model used for the longterm optimization. We compared the performance (oil recovery) of this truth model when applying the life-cycle strategy with and without the corrections provided by the data-driven algorithm and the tracking controller. In this theoretical study we observed that the use of the lower-level controller enabled successful tracking of the reference values provided by the upper-level optimizer. In our example, a performance drop of 6.4% in net present value (NPV), caused by differences between the reservoir model used for life-cycle optimization and the true reservoir, was successfully reduced to only 0.5% when applying the two-level strategy. Several studies have demonstrated that model-based life-cycle production optimization has a large scope to improve long-term economic performance of waterflooding projects. However, because of uncertainties in geology, economics, and operational decisions, such life-cycle strategies cannot simply be applied in reality. Our two-level approach offers a potential solution to realize life-cycle optimization in an operational setting.

Hierarchical Long-Term and Short-Term Production Optimization

SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 191-199 ◽  
Author(s):  
G.M.. M. van Essen ◽  
P.M.J.. M.J. Van den Hof ◽  
J.D.. D. Jansen
Keyword(s):  
Life Cycle ◽  
Optimization Problem ◽  
Primary Objective ◽  
Production Optimization ◽  
Cycle Performance ◽  
Short Term ◽  
Secondary Objective ◽  
Life Cycle Optimization ◽  
Term Performance

Summary Model-based dynamic optimization of oil production has a significant potential to improve economic life-cycle performance, as has been shown in various studies. However, within these studies, short-term operational objectives are generally neglected. As a result, the optimized injection and production rates often result in a considerable decrease in short-term production performance. In reality, however, it is often these short-term objectives that dictate the course of the operational strategy. Incorporating short-term goals into the life-cycle optimization problem, therefore, is an essential step in model-based life-cycle optimization. We propose a hierarchical optimization structure with multiple objectives. Within this framework, the life-cycle performance in terms of net present value (NPV) serves as the primary objective and shortterm operational performance is the secondary objective, such that optimality of the primary objective constrains the secondary optimization problem. This requires that optimality of the primary objective does not fix all degrees of freedom (DOF) of the decision variable space. Fortunately, the life-cycle optimization problem is generally ill-posed and contains many more decision variables than necessary. We present a method that identifies the redundant DOF in the life-cycle optimization problem, which can subsequently be used in the secondary optimization problem. In our study, we used a 3D reservoir in a fluvial depositional environment with a production life of 7 years. The primary objective is undiscounted NPV, while the secondary objective is aimed at maximizing shortterm production. The optimal life-cycle waterflooding strategy that includes short-term performance is compared to the optimal strategy that disregards short-term performance. The experiment shows a very large increase in short-term production, boosting first-year production by a factor of 2, without significantly compromising optimality of the primary objective, showing a slight drop in NPV of only -0.3%. Our method to determine the redundant DOF in the primary objective function relies on the computation of the Hessian matrix of the objective function with respect to the control variables. Although theoretically rigorous, this method is computationally infeasible for realistically sized problems. Therefore, we also developed a second, more pragmatic, method relying on an alternating sequence of optimizing the primary-and secondary-objective functions. Subsequently, we demonstrated that both methods lead to nearly identical results, which offers scope for application of hierarchical long-term and short-term production optimization to realistically sized flooding-optimization problems.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

Robust Constrained Optimization of Short- and Long-Term Net Present Value for Closed-Loop Reservoir Management

SPE Journal ◽  
2012 ◽  
Vol 17 (03) ◽  
pp. 849-864 ◽  
Author(s):  
C.. Chen ◽  
G.. Li ◽  
A.C.. C. Reynolds
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Production Optimization ◽  
Nonlinear Constraints ◽  
Present Value ◽  
Short Term ◽  
Linear And Nonlinear Constraints ◽  
Linear And Nonlinear ◽  
Reservoir Description

Summary In this paper, we develop an efficient algorithm for production optimization under linear and nonlinear constraints and an uncertain reservoir description. The linear and nonlinear constraints are incorporated into the objective function using the augmented Lagrangian method, and the bound constraints are enforced using a gradient-projection trust-region method. Robust long-term optimization maximizes the expected life-cycle net present value (NPV) over a set of geological models, which represent the uncertainty in reservoir description. Because the life-cycle optimal controls may be in conflict with the operator's objective of maximizing short-time production, the method is adapted to maximize the expectation of short-term NPV over the next 1 or 2 years subject to the constraint that the life-cycle NPV will not be substantially decreased. The technique is applied to synthetic reservoir problems to demonstrate its efficiency and robustness. Experiments show that the field cannot always achieve the optimal NPV using the optimal well controls obtained on the basis of a single but uncertain reservoir model, whereas the application of robust optimization reduces this risk significantly. Experimental results also show that robust sequential optimization on each short-term period is not able to achieve an expected life-cycle NPV as high as that obtained with robust long-term optimization.

scholarly journals Review of Wind Models at a Local Scale: Advantages and Disadvantages

2021 ◽  
Vol 9 (3) ◽  
pp. 318
Author(s):  
Felix P. Martinez-García ◽  
Antonio Contreras-de-Villar ◽  
Juan J. Muñoz-Perez
Keyword(s):  
Large Scale ◽  
Near Field ◽  
Wind Farms ◽  
Local Scale ◽  
Data Driven ◽  
Future Scenarios ◽  
Global Circulation ◽  
Advantages And Disadvantages ◽  
Global Circulation Models

Wind forecasts are widely spread because of the growth in wind power, but also because there are other applications to consider, such as the long-term scenario forecasts regarding the effects of global warming. Overall, there have been big developments in global circulation models (GCM) that inform future scenarios at the large scale, but wind forecast at a local scale is a problem that has not totally been solved. It should be possible to estimate the winds in the near field with a certain accuracy, which is interesting for aspects such as the blowing of incident wind at wind farms, the wind on a dune in movement, or the wind blowing in a harbour. Therefore, a data-driven wind transference equation at a local scale is needed. Among the conclusions, it is worthy to state that the statistical downscaling techniques are suitable for application as a statistical inference at small scales. The aim of this paper is therefore to review the current methods and techniques used and show the different methodologies and their applications in the field. Additional targets will be to identify the advantages, disadvantages, or limitations of the current models at the local scale and propose research to find possible improvements.

Multiple optimization and segmentation technique (MOST) for large-scale bilevel life cycle optimization

2011 ◽  
Vol 38 (3) ◽  
pp. 263-271 ◽  
Author(s):  
Tarek Hegazy ◽  
Ahmed Elhakeem
Keyword(s):  
Life Cycle ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bilevel Optimization ◽  
Scale Problem ◽  
Segmentation Technique ◽  
Building Components ◽  
Life Cycle Optimization ◽  
New Formulation

This paper introduces a new formulation for large-scale combinatorial bilevel optimization problems that involve integer, discrete, two-level decisions. The most vivid example where the new technique most applies is the life cycle optimization needed to allocate repair types and repair timings to a number of infrastructure assets (e.g., building components). Combining these decisions into a single optimization for hundreds of assets simultaneously makes the optimization problem complex and prohibitive. For such a large-scale problem, a multiple optimization and segmentation technique (MOST) is proposed to handle the optimization one level at a time through a series of small-size optimizations that can be solved easily. The performance of MOST has been validated on various problem sizes and proved to be innovative and can handle thousands of variables simultaneously.

scholarly journals Life-cycle production optimization of hydrocarbon fields: thermoeconomics perspective

2019 ◽  
Vol 3 (11) ◽  
pp. 3050-3060 ◽  
Author(s):  
R. Farajzadeh ◽  
S. S. Kahrobaei ◽  
A. H. de Zwart ◽  
D. M. Boersma
Keyword(s):  
Life Cycle ◽  
Production Optimization ◽  
Hydrocarbon Fields ◽  
Life Cycle Optimization

Life-cycle optimization of waterfloods leads to reduced CO2 emission and increased profit.

Assessing the Applicability of the Tropical Convective–Stratiform Paradigm in the Extratropics Using Radar Divergence Profiles

2014 ◽  
Vol 27 (17) ◽  
pp. 6673-6686 ◽  
Author(s):  
Cameron R. Homeyer ◽  
Courtney Schumacher ◽  
Larry J. Hopper
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Horizontal Wind ◽  
Stratiform Rain ◽  
Vertical Divergence ◽  
Upper Level ◽  
The Tropics ◽  
The Impact ◽  
Level Disturbance

Abstract Long-term radar observations from a subtropical location in southeastern Texas are used to examine the impact of storm systems with tropical or extratropical characteristics on the large-scale circulation. Climatological vertical profiles of the horizontal wind divergence are analyzed for four distinct storm classifications: cold frontal (CF), warm frontal (WF), deep convective upper-level disturbance (DC-ULD), and nondeep convective upper-level disturbances (NC-ULD). DC-ULD systems are characterized by weakly baroclinic or equivalent barotropic environments that are more tropical in nature, while the remaining classifications are representative of common midlatitude systems with varying degrees of baroclinicity. DC-ULD systems are shown to have the highest levels of nondivergence (LND) and implied diabatic heating maxima near 6 km, whereas the remaining baroclinic storm classifications have LND altitudes that are about 0.5–1 km lower. Analyses of climatological mean divergence profiles are also separated by rain regions that are primarily convective, stratiform, or indeterminate. Convective–stratiform separations reveal similar divergence characteristics to those observed in the tropics in previous studies, with higher altitudes of implied heating in stratiform rain regions, suggesting that the convective–stratiform paradigm outlined in previous studies is applicable in the midlatitudes. Divergence profiles that cannot be classified as primarily convective or stratiform are typically characterized by large regions of stratiform rain with areas of embedded convection of shallow to moderate extent (i.e., echo tops <10 km). These indeterminate profiles illustrate that, despite not being very deep and accounting for a relatively small fraction of a given storm system, convection dominates the vertical divergence profile and implied heating in these cases.

Development of a Visualized Data Management System for Life-Cycle Health Monitoring of Civil Structures

2012 ◽  
Vol 83 ◽  
pp. 188-197
Author(s):  
Ke Chang Lin ◽  
Yi Qing Ni ◽  
Xiao Wei Ye ◽  
Kai Yuan Wong
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Health Monitoring ◽  
Spatial Data ◽  
Management System ◽  
Large Scale ◽  
Monitoring Data ◽  
Data Management System ◽  
Civil Structures

The data management system (DMS) is an essential part for long-term structural health monitoring (SHM) systems, which stores a pool of monitoring data for various applications. A robust database within a DMS is generally used to archive, manage and update life-cycle information of civil structures. However, many applications especially those to large-scale structures provide little support for visualizing the long-term monitoring data. This paper presents the development of an efficient visualized DMS by integrating 4-dimension (4D) model technology, nested relational database, and virtual reality (VR) technology. Spatial data of the 4D model are organized in nested tables, while real-time (temporal) monitoring data are linked to the 4D model. The model is then reconstructed by use of an OpenSceneGraph 3D engine. A user interface is developed to query the database and display the data via the 4D model. To demonstrate its efficiency, the proposed method has been applied to the Canton Tower, a supertall tower-like structure instrumented with a long-term SHM system

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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