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.

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 SEQUENTIAL CAPITAL INVESTMENT DECISION MAKING UNDER EXTREME CASH FL OW SITUATIONS: EVIDENCE USING MONTE CARLO SIMULATION

2015 ◽  
Vol 16 (5) ◽  
pp. 877-900 ◽  
Author(s):  
Wenqing Zhang ◽  
Prasad Padmanabhan ◽  
Chia-Hsing Huang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Capital Investment ◽  
Net Present Value ◽  
Capital Budgeting ◽  
Cash Flows ◽  
Present Value ◽  
Short Term ◽  
Value Analysis

Uncertainty influences a decision maker's choices when making sequential capital investment decisions. With the possibility of extremely negative cash inflows, firms may need to curtail operations significantly. Traditional Net Present Value analysis does not allow for efficient management of these problems. In addition, firm managers may behave irrationally by accepting negative Net Present Value projects in the short term. This paper presents a Monte Carlo simulation based model to provide policy insights on how to incorporate extreme cash flows and manager irrationality scenarios into the capital budgeting process. This paper presents evidence that firms with irrational managers and experiencing extremely negative cash flows may, under certain conditions, reap long term rewards associated with the acceptance of negative Net Present Value projects in the short term. These benefits are largest if cost ratios (discount rates) are small, or investment horizons are high. We argue that acceptance of short term negative Net Present Value projects implies the purchase of a long term real option which can generate positive long term cash flows under certain conditions.

Robust Multiobjective Nonlinear Constrained Optimization with Ensemble Stochastic Gradient Sequential Quadratic Programming-Filter Algorithm

SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Zhe Liu ◽  
Albert Reynolds
Keyword(s):  
Life Cycle ◽  
Sequential Quadratic Programming ◽  
State Constraints ◽  
Production Optimization ◽  
Pareto Optimal ◽  
Nonlinear Constraints ◽  
Water Production ◽  
Production Rates ◽  
Short Term ◽  
Lexicographic Method

Summary As the crucial step in closed-loop reservoir management, robust life-cycle production optimization is defined as maximizing/minimizing the expected value of a predefined objective (cost) function over geological uncertainties (i.e., uncertainties in the reservoir permeability, porosity, endpoint relative permeability, etc.). However, with robust optimization, there is no control over downside risk defined as the minimum net present value (NPV) among the individual NPVs of the different reservoir models. Yet, field operators generally wish to keep this minimum NPV reasonably large to try to ensure that the reservoir is commercially viable. In addition, the field operator may desire to maximize the NPV of production over a much shorter time period than the life of the reservoir under the limitation of surface facilities (e.g., field liquid and water production rates). Thus, it is important to consider multiobjective robust production optimization with nonlinear constraints and when geological uncertainties are incorporated. The three objectives considered in this paper are; to maximize the average life-cycle NPV, to maximize the average short-term NPV, and to maximize the minimum NPV of the set of realizations. Generally, these objectives are in conflict; for example, the well controls that give a global maximum for robust life-cycle production optimization do not usually correspond to the controls that maximize the short-term average NPV of production. Moreover, handling the nonlinear state constraints (e.g., field liquid production rates and field water production rates for the bottom-hole pressure controlled producers in the robust production optimization) is also a challenge because those nonlinear constraints should be satisfied at each control steps for each geological realization. To provide potential solutions to the multiobjective robust optimization problem with state constraints, we developed a modified lexicographic method with a minimizing-maximum scheme to attempt to obtain a set of Pareto optimal solutions and to satisfy all nonlinear constraints. We apply the sequential quadratic programming filter with modified stochastic gradients to solve a sequence of optimization problems, where each solution is designed to generate a single point on the Pareto front. In the modified lexicographic method, the objective is always considered to be the primary objective, and the other objectives are considered by specifying bounds on them to convert them to state constraints. The temporal damping and truncation schemes are applied to improve the quality of the stochastic gradient on nonlinear constraints, and the minimizing–maximum procedure is applied to enforce constraints on the normal state constraints. The main advantage that the modified lexicographic method has over the standard lexicographic method is that it allows for the generation of potential Pareto optimal points, which are uniformly spaced in the values of the second and/or third objective that one wishes to improve by multiobjective optimization.

scholarly journals Short-term stochastic movements of electricity prices and long-term investments in power generating technologies

2021 ◽  
Author(s):  
Carlo Mari
Keyword(s):  
Electricity Market ◽  
Nuclear Power ◽  
Net Present Value ◽  
Probability Distributions ◽  
Present Value ◽  
Electricity Prices ◽  
Short Term ◽  
Market Prices ◽  
Fuel Prices

AbstractModeling probability distributions for the long-term dynamics of electricity prices is of key importance to value long-term investments under uncertainty in the power sector, such as investments in new generating technologies. Starting from accurate modeling of the short-term behavior of electricity prices, we derive long-term stationary probability distributions. Then, investments in new baseload generating technologies, namely gas, coal and nuclear power, are discussed. In order to compute the stochastic Net Present Value of investments in new generating technologies, the revenues from selling electricity in power markets as well as the costs which come from buying fuels at uncertain market prices must be evaluated over very long time horizons, i.e., over the whole lifetime of the plants. Starting from accurate short-term stochastic models of fuel prices in addition to electricity prices, we provide long-run probability distributions which are used to compute revenues and costs incurring during the whole lifetime of the plants. Five sources of uncertainty are taken into account, namely electricity market prices, fossil fuel prices (natural gas and coal prices), nuclear fuel prices and $$\hbox {CO}_{\text{2 }}$$ CO 2 prices. Our evaluation model is calibrated on empirical data to account for both historical market prices and macroeconomic views about future trends of electricity and fuel prices. The full probability density of the stochastic Net Present Value is thus determined for each generation technology considered in this study.

Streamline Tracing and Applications in Dual Porosity Dual Permeability Models

2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Time Of Flight ◽  
Rate Allocation ◽  
Production Optimization ◽  
Dual Porosity ◽  
Computationally Efficient ◽  
Present Value ◽  
Sweep Efficiency ◽  
Streamline Tracing

Abstract Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.

scholarly journals LIFE-CYCLE HYPOTHESIS OF HYBRID SUKUK

JURISDICTIE ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 137
Author(s):  
Luthvia Moonda
Keyword(s):  
Economic Growth ◽  
Life Cycle ◽  
Financial Institutions ◽  
Financial Services ◽  
Net Present Value ◽  
Vital Role ◽  
Muslim World ◽  
Present Value ◽  
The Public ◽  
Life Cycle Hypothesis

The development of financial system recently has been contributing to the economic growth of the nation. Its vital role helps many financial institutions to advance their financial services, particularly in investment systems such as Sukuk. As seen in recent years, the involvement of Sukuk in many industries ranging from sovereigns to corporates for both Muslim and non-Muslim world companies. Although its popularity increases, it seems to be widely accepted that the insufficient structures of Sukuk become a big challenge to serve the public needs to be in compliance with Shari’ah principles. In an attempt to fulfil many financial companies in issuing Sukuk and the needs of the Muslim world, this study aims to provide the new structure of Sukuk. The study proposes a design of securitization by combining two contracts of Sukuk into one Sukuk structure. The constructed idea will use a model of life cycle hypothesis to support the Sukuk issuing companies in maintaining their incomes. It also explains the cycle cash flow and asset movement as well as the calculation of Net Present Value (NPV) of the project.

Creating continuous areas of old forest in long-term forest planning

2000 ◽  
Vol 30 (11) ◽  
pp. 1817-1823 ◽  
Author(s):  
Karin Öhman
Keyword(s):  
Net Present Value ◽  
Problem Formulation ◽  
Core Area ◽  
Northern Sweden ◽  
Present Value ◽  
The Core ◽  
Long Range Planning ◽  
The Cost

Harvest activities tend often to create landscapes where the old forest is fragmented into isolated patches that provide marginal conditions for species that inhabit forest interiors. This paper presents a long-range planning model designed to maximize the net present value and to create continuous patches of old forest. In this model, the spatial structure of old forest is controlled by core area and edge habitats. Core area is defined as the area of old forest that is free of edge effects from surrounding habitats. The core area requirement is set to a fixed value for each of a number of time periods, whereas the area of edge habitats, which should be as small as possible, is weighted against the net present value. The model is applied in a case study to an actual landscape consisting of 755 stands of forest in northern Sweden and solved using simulated annealing. The results show that distinct continuous patches of old forest are created when both a core area requirement and consideration of the amount of edge habitats are included in the problem formulation. The cost of creating continuous areas of old forest was found to be significant.

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