Hierarchical Multiscale Methods for Life-Cycle-Production Optimization: A Field Case Study

SPE Journal ◽  
2015 ◽  
Vol 20 (05) ◽  
pp. 896-907 ◽  
Author(s):  
D. F. Oliveira ◽  
A. C. Reynolds
Keyword(s):  
Net Present Value ◽  
Simple Procedure ◽  
Optimization Techniques ◽  
Multiscale Methods ◽  
Production Optimization ◽  
Real Field ◽  
Historical Period ◽  
Reference Case ◽  
Multiscale Optimization ◽  
Hierarchical Multiscale

Summary We apply hierarchical multiscale techniques previously developed by the authors to estimate the well controls that maximize the net present value of the long-term production from a real field offshore Brazil. This field has been in production for several years, and it represents a significant share of the overall oil production for the country. The production-optimization step is preceded by a 10-year historical period, where seismic and production data were history matched by use of ensemble-based approaches. The well controls on a sequence of control steps (time intervals) are optimized for the next 10 years of production by use of the hierarchical-multiscale-optimization and the refinement-indicator-based hierarchical-multiscale-optimization techniques, which refine the control steps as the optimization proceeds. The performance of our approaches is compared with that of a reference case, which applies the well rates used to forecast the production of the real field, as well as with the performance of a standard optimization procedure that uses a fixed set of well controls and a simple procedure to refine control steps.

scholarly journals Stochastic Simplex Approximate Gradient for Robust Life-Cycle Production Optimization: Applied to Brugge Field

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Bailian Chen ◽  
Jianchun Xu
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Operating Conditions ◽  
Optimization Under Uncertainty ◽  
Theoretical Argument ◽  
Gas Industry

In oil and gas industry, production optimization is a viable technique to maximize the recovery or the net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates settings of inflow control valves and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for the robust life-cycle production optimization. Recently, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Fonseca and co-workers (2016, A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization Under Uncertainty, Int. J. Numer. Methods Eng., 109(13), pp. 1756–1776) provided a theoretical argument on the superiority of StoSAG over EnOpt. However, it has not drawn significant attention in the reservoir optimization community. The purpose of this study is to provide a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAG generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.

Adaptation of the CPR Preconditioner for Efficient Solution of the Adjoint Equation

SPE Journal ◽  
2013 ◽  
Vol 18 (02) ◽  
pp. 207-213 ◽  
Author(s):  
Choongyong Han ◽  
John Wallis ◽  
Pallav Sarma ◽  
Gary Li ◽  
Mark L. Schrader ◽  
...  
Keyword(s):  
Efficient Solution ◽  
Jacobian Matrix ◽  
Adjoint Equation ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Real Field ◽  
Matching Problem ◽  
Linear Solver ◽  
Reservoir Models ◽  
Reservoir Simulator

Summary It is well known that the adjoint approach is the most efficient approach for gradient calculation, and it can be used with gradient-based optimization techniques to solve various optimization problems, such as the production-optimization problem and the history-matching problem. The adjoint equation to be solved in the approach is a linear equation formed with the “transpose” of the Jacobian matrix from a fully implicit reservoir simulator. For a large and/or complex reservoir model, generalized preconditioners often prove impractical for solving the adjoint equation. Preconditioners specialized for reservoir simulation, such as constrained pressure residual (CPR), exploit properties of the Jacobian matrix to accelerate convergence, so they cannot be applied directly to the adjoint equation. To overcome this challenge, we have developed a new two-stage preconditioner for efficient solution of the adjoint equation by adaptation of the CPR preconditioner (named CPRA: CPR preconditioner for adjoint equation). The CPRA preconditioner has been coupled with an algebraic multigrid (AMG) linear solver and implemented in Chevron's extended applications reservoir simulator (CHEARS®). The AMG solver is well known for its outstanding capability to solve the pressure equation of complex reservoir models; solving the linear system with the “transpose” of the pressure matrix is one of the two stages of construction of the CPRA preconditioner. Through test cases, we have confirmed that the CPRA/AMG solver with generalized minimal residual (GMRES) acceleration solves the adjoint equation very efficiently with a reasonable number of linear-solver iterations. Adjoint simulations to calculate the gradients with the CPRA/AMG solver take approximately the same amount of time (at most) as do the corresponding CPR/AMG forward simulations. Accuracy of the solutions has also been confirmed by verifying the gradients against solutions with a direct solver. A production-optimization case study for a real field using the CPRA/AMG solver has further validated its accuracy, efficiency, and the capability to perform long-term optimization for large, complex reservoir models at low computational cost.

scholarly journals Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

2015 ◽  
Vol 18 (04) ◽  
pp. 554-563 ◽  
Author(s):  
R.-M.. -M. Fonseca ◽  
O.. Leeuwenburgh ◽  
E.. Della Rossa ◽  
P. M. Van den Hof ◽  
J.-D.. -D. Jansen
Keyword(s):  
Control Problem ◽  
Net Present Value ◽  
Water Saturation ◽  
Computational Cost ◽  
Production Optimization ◽  
Discrete Control ◽  
Real Field ◽  
Continuous Control ◽  
Initial Water ◽  
Control Devices

Summary We consider robust ensemble-based (EnOpt) multiobjective production optimization of on/off inflow-control devices (ICDs) for a sector model inspired by a real-field case. The use of on/off valves as optimization variables leads to a discrete control problem. We propose a reparameterization of such discrete controls in terms of switching times (i.e., we optimize the time at which a particular valve is either open or closed). This transforms the discrete control problem into a continuous control problem that can be efficiently handled with the EnOpt method. In addition, this leads to a significant reduction in the number of controls that is expected to be beneficial for gradient quality when using approximate gradients. We consider an ensemble of sector models where the uncertainty is described by different permeability, porosity, net/gross ratios, and initial water-saturation fields. The controls are the ICD settings over time in the three horizontal injection wells, with approximately 15 ICDs per well. Different optimized strategies resulting from different initial strategies were compared. We achieved a mean 4.2% increase in expected net present value (NPV) at a 10% discount rate compared with a traditional pressure-maintenance strategy. Next, we performed a sequential biobjective optimization and achieved an increase of 9.2% in the secondary objective (25% discounted NPV to emphasize short-term production gains) for a minimal decrease of 1% in the primary objective (0% discounted NPV to emphasize long-term recovery gains), as averaged over the 100 geological realizations. The work flow was repeated for alternative numbers of ICDs, showing that having fewer control options lowers the expected value for this particular case. The results demonstrate that ensemble-based optimization work flows are able to produce improved robust recovery strategies for realistic field-sector models against acceptable computational cost.

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 Analysis of Enhanced Geothermal System Development Scenarios for District Heating and Cooling of the Göttingen University Campus

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 349
Author(s):  
Dmitry Romanov ◽  
Bernd Leiss
Keyword(s):  
Net Present Value ◽  
District Heating ◽  
Reservoir Modeling ◽  
Geothermal System ◽  
Heat Output ◽  
Enhanced Geothermal Systems ◽  
University Campus ◽  
Energy Potential ◽  
Reference Case ◽  
Heating And Cooling

The huge energy potential of Enhanced Geothermal Systems (EGS) makes them perspective sources of non-intermittent renewable energy for the future. This paper focuses on potential scenarios of EGS development in a locally and in regard to geothermal exploration, poorly known geological setting—the Variscan fold-and-thrust belt —for district heating and cooling of the Göttingen University campus. On average, the considered single EGS doublet might cover about 20% of the heat demand and 6% of the cooling demand of the campus. The levelized cost of heat (LCOH), net present value (NPV) and CO2 abatement cost were evaluated with the help of a spreadsheet-based model. As a result, the majority of scenarios of the reference case are currently not profitable. Based on the analysis, EGS heat output should be at least 11 MWth (with the brine flow rate being 40 l/s and wellhead temperature being 140 °C) for a potentially profitable project. These parameters can be a target for subsurface investigation, reservoir modeling and hydraulic stimulation at a later stage. However, sensitivity analysis presented some conditions that yield better results. Among the most influential parameters on the outcome are subsidies for research wells, proximity to the campus, temperature drawdown and drilling costs. If realized, the EGS project in Göttingen might save up to 18,100 t CO2 (34%) annually.

Review of Hierarchical Multiscale Modeling to Describe the Mechanical Behavior of Amorphous Polymers

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
J. L. Bouvard ◽  
D. K. Ward ◽  
D. Hossain ◽  
S. Nouranian ◽  
E. B. Marin ◽  
...  
Keyword(s):  
Mechanical Behavior ◽  
Multiscale Modeling ◽  
Coarse Graining ◽  
Amorphous Polymers ◽  
Multiscale Methods ◽  
Structure Property ◽  
Lightweight Materials ◽  
Structure Property Relationships ◽  
Modeling Strategy ◽  
Hierarchical Multiscale

Modern computational methods have proved invaluable for the design and analysis of structural components using lightweight materials. The challenge of optimizing lightweight materials in the design of industrial components relates to incorporating structure-property relationships within the computational strategy to incur robust designs. One effective methodology of incorporating structure-property relationships within a simulation-based design framework is to employ a hierarchical multiscale modeling strategy. This paper reviews techniques of multiscale modeling to predict the mechanical behavior of amorphous polymers. Hierarchical multiscale methods bridge nanoscale mechanisms to the macroscale/continuum by introducing a set of structure-property relationships. This review discusses the current state of the art and challenges for three distinct scales: quantum, atomistic/coarse graining, and continuum mechanics. For each scale, we review the modeling techniques and tools, as well as discuss important recent contributions. To help focus the review, we have mainly considered research devoted to amorphous polymers.

scholarly journals Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 816 ◽  
Author(s):  
Daigang Wang ◽  
Yong Li ◽  
Jing Zhang ◽  
Chenji Wei ◽  
Yuwei Jiao ◽  
...  
Keyword(s):  
Time Delay ◽  
Net Present Value ◽  
Water Injection ◽  
Injection Rate ◽  
Production Optimization ◽  
Carbonate Reservoirs ◽  
Karst Reservoir ◽  
Control Step ◽  
Multi Phase Flow

Due to the coexistence of multiple types of reservoir bodies and widely distributed aquifer support in karst carbonate reservoirs, it remains a great challenge to understand the reservoir flow dynamics based on traditional capacitance–resistance (CRM) models and Darcy’s percolation theory. To solve this issue, an improved injector–producer-pair-based CRM model coupling the effect of active aquifer support was first developed and combined with the newly-developed Stochastic Simplex Approximate Gradient (StoSAG) optimization algorithm for accurate inter-well connectivity estimation in a waterflood operation. The improved CRM–StoSAG workflow was further applied for real-time production optimization to find the optimal water injection rate at each control step by maximizing the net present value of production. The case study conducted for a typical karst reservoir indicated that the proposed workflow can provide good insight into complex multi-phase flow behaviors in karst carbonate reservoirs. Low connectivity coefficient and time delay constant most likely refer to active aquifer support through a high-permeable flow channel. Moreover, the injector–producer pair may be interconnected by complex fissure zones when both the connectivity coefficient and time delay constant are relatively large.

Production optimization using a modified net present value

2020 ◽  
Vol 24 (3) ◽  
pp. 1087-1100
Author(s):  
Eugênio Libório Feitosa Fortaleza ◽  
Emanuel Pereira Barroso Neto ◽  
Marco Emílio Rodrigues Miranda
Keyword(s):  
Net Present Value ◽  
Production Optimization ◽  
Present Value

Optimization of Well Placement Under Time-Dependent Uncertainty

2006 ◽  
Vol 9 (02) ◽  
pp. 135-145 ◽  
Author(s):  
Umut Ozdogan ◽  
Roland N. Horne
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Optimization Technique ◽  
Production Optimization ◽  
Time Dependent ◽  
Hill Climbing ◽  
Process Data ◽  
Well Placement ◽  
Placement Decisions ◽  
Exploration And Development

Summary Well-placement decisions made during the early stages of exploration and development activities have the capability to improve later placement decisions by providing more information (greater certainty). Therefore, recovery and efficient use of information may add value beyond the amount of oil recovered. This study proposes an approach that emphasizes the value of time-dependent information to achieve better decisions in terms of reduced uncertainty and increased probable net present value (NPV). Unlike previous approaches, well-placement optimization is coupled with recursive probabilistic history-matching steps through the use of the pseudohistory concept. The pseudohistory is defined as the probable (future) response of the reservoir that is generated by a probabilistic forecasting model. To test the results of the proposed approach, an example reservoir was investigated with multiple realizations, all of which match the same production history. The results of this study showed that subsequent well-placement decisions can be improved when probabilistic production profiles obtained from the wells, as they are drilled, are incorporated in the optimization scheme.. Introduction Well placement is one of the important decisions made during the exploration and development phase of projects. Most of the time, the large number of possibilities, constraints on computational resources, and the size of the simulation models limit the number of possible scenarios that may be considered. In these cases, optimization algorithms become extremely valuable in searching for the optimum development scenario. Various approaches have been proposed for production optimization. Bittencourt (1994) optimized the scheduling of a field using the polytope algorithm. Beckner and Song (1995) applied the traveling salesman framework on a well-placement problem, using simulated annealing (SA) to find the optimum locations of the wells. Bittencourt and Horne (1997) hybridized genetic algorithms (GA) with the polytope algorithm and tabu search and referred to this hybrid optimization technique as HGA. HGA was observed to improve the economic forecasts and CPU effort during optimization. Pan and Horne (1998) used kriging as a proxy to the reservoir simulator to decrease the number of simulations. Guyaguler et al. (2000) showed that the number of simulations required to optimize the injector well locations decrease when an HGA was coupled with a kriging proxy. Yeten et al. (2002) coupled GA with hill-climbing methods and an artificial neural network (ANN) proxy to optimize the type, location, and trajectory of nonconventional wells. Guyaguler and Horne (2001) assessed the uncertainty of the well-placement results using utility theory together with multiple realizations of the reservoir. All these approaches considered only the information that was available at the beginning of the optimization process. Data that would become available as the reservoir developed in time was not taken into account.

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