scholarly journals Optimization of Well Sidetrack under Uncertainty using Response Surface and Decision Support. I. Primary Recovery Mechanism

2019 ◽  
Vol 8 (4) ◽  
pp. 3294-3302
Keyword(s):  
Net Present Value ◽  
Analytical Models ◽  
Computational Time ◽  
Reservoir Properties ◽  
Multi Objective ◽  
Recovery Mechanism ◽  
Cumulative Production ◽  
Early Production ◽  
Proxy Models ◽  
Predicted Values

The Optimal sidetrack time (tR-OPT) has been estimated for uncertainty of the probability of success (POS) of the sidetrack operation, reservoir properties and economics for a reservoir under primary recovery mechanism. The case studies worked on in literature considered in this study are for those for primary recovery in which production profiles were represented by empirical and analytical models. However, not all recovery can be adequately replicated by these analytical models. Hence, the need to apply proxy models not just to predict cumulative production but net-present-value (NPV). In this study the analysis of a decision tree with several branches is carried out to maximize NPV that is evaluated under the influence of production stoppage due to the sidetrack into another non-communicating upper zone with uncertainty of reservoir properties. The optimal sidetrack time adds a severe non-linearity in the response of the resulting proxy model and expected monetary value (EMV), the objective function. Multi -objective functions of proxy models over time-intervals for highly time impacted terminal branches, known as split design was applied to evaluate when to conduct a well sidetrack operation under risk and uncertainty in order to resolve severe non-linearity of the NPV solved by a standard optimization algorithm in a spreadsheet. The Predicted values of optimal sidetrack time by the developed workflow was relatively reasonable and highly satisfactory in comparison with simulation results and that of empirical and analytical models. Though, further performance improvement is possible, the constraint on computational time for multi-objective optimization must be weighed against the desired result. Monte Carlo implementation on EMV based on uncertainty of reservoir properties and varying POS acknowledges the fact that for favourable POS, that is values approaching 1.0, tR-OPT clustered at early production life with a spike and the later for unfavourable values.

scholarly journals Integration of Response Surface and Decision Support to optimize a Well Sidetrack under Uncertainty II. Secondary Recovery Mechanism

2019 ◽  
Vol 8 (4) ◽  
pp. 6571-6583
Keyword(s):  
Net Present Value ◽  
Simulation Analysis ◽  
Perfect Match ◽  
Analytical Models ◽  
Optimal Time ◽  
Reservoir Properties ◽  
Multi Objective ◽  
Recovery Mechanism ◽  
Secondary Recovery ◽  
Proxy Models

The optimal time to sidetrack into a different layer from an already producing horizon with secondary recovery mechanism of waterflooding is evaluated with the uncertainties embedded in Probabilities of success (POS) including economic, operational, technical and reservoir properties. Previous literatures are majorly primary recovery and secondary recovery by waterflooding in which production profiles were represented by empirical and analytical models. However, not all recovery mechanisms can be sufficiently reproduced by these models and this introduces and explains the need for the use of proxy models to predict cumulative production and net-present-value (NPV). The peculiarity of this study is the application of decision analysis/tree with multiple terminal branches to both production and injection sidetrack, where NPV is estimated under the influence of change of recovery mechanism due to sidetrack (recompletion) to another possibly non-communicating zone or layer with uncertainty of reservoir properties and production discontinuity from the already producing horizon. By and large, sidetrack time adds in acute non-linearity on the NPV. Multi–objective functions of proxy models over time-intervals for the impacted terminal branches, known as split design was applied to evaluate when to carry out a well sidetrack operation under risk and uncertainty. This was adopted to resolve severe non-linearity of the NPV and the multi-objective function of EMV by a standard optimization algorithm in a spreadsheet. The final results gave a satisfactory match to the simulation results. In order to get a perfect match through more improvements on performance there is a need for large computation times and the decision must be made depending on the required result. Monte- Carlo simulation analysis shows that optimal sidetrack time is at the early production life

Multi-Objective Hydro-Thermal Scheduling Problem Using Two Novel Optimization Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 1-36
Author(s):  
Provas Kumar Roy ◽  
Moumita Pradhan ◽  
Tandra Pal
Keyword(s):  
Power Systems ◽  
Test System ◽  
Optimization Techniques ◽  
Computational Time ◽  
Environmental Aspects ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Loading Effects ◽  
Solution Accuracy

This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cascade of 4 hydro and 3 thermal units whose valve-point loading effects are also taken into account. Furthermore, statistical outcomes of the conventional heuristic approaches available in the literature are compared with the proposed GWO and OGWO approaches, and these methods give moderately better operational fuel cost and emission in less computational time.

scholarly journals Multi-Objective Optimization of Wire Electrical Discharge Machining of AL 2124/SIC Composite

2019 ◽  
Vol 9 (1) ◽  
pp. 2155-2163
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Strength ◽  
Wire Electrical Discharge Machining ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Predicted Values ◽  
Pulse On Time ◽  
Pulse Off Time

The growing demand for the use of high strength to weight alloys in industries for manufacturing complex structures challenges the machinability of such advanced materials. In the present investigation, the machinability of SiC particle reinforced Al 2124 composite was studied on Wire electrical discharge machining (WEDM). The process parameters namely pulse on-time (Ton), pulse off time (Toff), peak current (IP), and servo voltage (SV) were optimized by utilizing the central composite design layout. The output responses such as kerf and material removal rate (MRR) were studied in detail. The single and multi-objective optimization was studied for a combination effect using Derringer’s desirability approach and Genetic Algorithm (GA). The experimental and predicted values for each response were validated at the optimized condition. The experimental results were found in line with the predicted values. Multi objective optimization of kerf and MRR by GA showing better result compared to RSM.

Robust Well Placement Optimization Through Universal Trace Kriging with Adaptive Sampling

2021 ◽  
Author(s):  
Carlo Cristiano Stabile ◽  
Marco Barbiero ◽  
Giorgio Fighera ◽  
Laura Dovera
Keyword(s):  
Adaptive Sampling ◽  
Pareto Front ◽  
Computing Time ◽  
Computational Cost ◽  
Oil Field ◽  
Computational Time ◽  
Decision Space ◽  
Adaptive Approach ◽  
Reservoir Simulations ◽  
Proxy Models

Abstract Optimizing well locations for a green field is critical to mitigate development risks. Performing such workflows with reservoir simulations is very challenging due to the huge computational cost. Proxy models can instead provide accurate estimates at a fraction of the computing time. This study presents an application of new generation functional proxies to optimize the well locations in a real oil field with respect to the actualized oil production on all the different geological realizations. Proxies are built with the Universal Trace Kriging and are functional in time allowing to actualize oil flows over the asset lifetime. Proxies are trained on the reservoir simulations using randomly sampled well locations. Two proxies are created for a pessimistic model (P10) and a mid-case model (P50) to capture the geological uncertainties. The optimization step uses the Non-dominated Sorting Genetic Algorithm, with discounted oil productions of the two proxies, as objective functions. An adaptive approach was employed: optimized points found from a first optimization were used to re-train the proxy models and a second run of optimization was performed. The methodology was applied on a real oil reservoir to optimize the location of four vertical production wells and compared against reference locations. 111 geological realizations were available, in which one relevant uncertainty is the presence of possible compartments. The decision space represented by the horizontal translation vectors for each well was sampled using Plackett-Burman and Latin-Hypercube designs. A first application produced a proxy with poor predictive quality. Redrawing the areas to avoid overlaps and to confine the decision space of each well in one compartment, improved the quality. This suggests that the proxy predictive ability deteriorates in presence of highly non-linear responses caused by sealing faults or by well interchanging positions. We then followed a 2-step adaptive approach: a first optimization was performed and the resulting Pareto front was validated with reservoir simulations; to further improve the proxy quality in this region of the decision space, the validated Pareto front points were added to the initial dataset to retrain the proxy and consequently rerun the optimization. The final well locations were validated on all 111 realizations with reservoir simulations and resulted in an overall increase of the discounted production of about 5% compared to the reference development strategy. The adaptive approach, combined with functional proxy, proved to be successful in improving the workflow by purposefully increasing the training set samples with data points able to enhance the optimization step effectiveness. Each optimization run performed relies on about 1 million proxy evaluations which required negligible computational time. The same workflow carried out with standard reservoir simulations would have been practically unfeasible.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

MICROSTRUCTURE BASED FINITE ELEMENT ANALYSIS OF PARTICLE FILLED POLYMER COMPOSITE

2017 ◽  
Vol 41 (5) ◽  
pp. 681-690
Author(s):  
S. Supriya ◽  
J. Selwinrajadurai ◽  
P. Anshul
Keyword(s):  
Effective Properties ◽  
Epoxy Composite ◽  
Heterogeneous Material ◽  
Detection Algorithm ◽  
Analytical Models ◽  
Element Analysis ◽  
Filled Polymer ◽  
Sem Image ◽  
Mechanical Models ◽  
Predicted Values

Particle filled polymer composites are widely used because of its tailor-made properties and ease of manufacturability. Existing micro mechanical models to characterize heterogeneous material are based on the Representative Volume Element (RVE). The assumptions made in the RVE model, play a crucial role in the exact prediction of effective properties of the composites. In this work, microstructure based RVE is utilized to predict the effective properties of Solid Glass Microsphere (SGM) filled epoxy composite. The Scanning Electron Microscope (SEM) image obtained from the specimens fabricated at different loading fractions is processed in MATLAB. Canny edge detection algorithm is utilized for processing the images. The random dispersion of the particle is exactly modeled in ANSYS from the MATLAB output. The effective Young’s modulus of the SGM filled epoxy composite is determined. The numerically predicted values are compared with the experimental value and analytical models.

Multi-objective forestry increases the production of ecosystem services

2020 ◽  
Author(s):  
Olalla Díaz-Yáñez ◽  
Timo Pukkala ◽  
Petteri Packalen ◽  
Manfred J Lexer ◽  
Heli Peltola
Keyword(s):  
Ecosystem Services ◽  
Boreal Forest ◽  
Boreal Forests ◽  
Carbon Balance ◽  
Net Present Value ◽  
Simulation And Optimization ◽  
Multi Objective ◽  
Clear Cut ◽  
Trade Offs ◽  
Continuous Cover Forestry

Abstract Boreal forests produce multiple ecosystem services for the society. Their trade-offs determine whether they should be produced simultaneously or whether it is preferable to assign separate areas to different ecosystem services. We use simulation and optimization to analyse the correlations, trade-offs and production levels of several ecosystem services in single- and multi-objective forestry over 100 years in a boreal forest landscape. The case study area covers 3600 ha of boreal forest, consisting of 3365 stands. The ecosystem services and their indicators (in parentheses) considered are carbon sequestration (forestry carbon balance), biodiversity (amount of deadwood and broadleaf volume), economic profitability of forestry (net present value of timber production) and timber supply to forest industry (volume of harvested timber). The treatment alternatives simulated for each of the stands include both even-aged rotation forestry (thinning from above with clear cut) and continuous cover forestry regimes (thinning from above with no clear cut). First, we develop 200 Pareto optimal plans by maximizing multi-attribute utility functions using random weights for the ecosystem service indicators. Second, we compare the average level of ecosystem services in single- and multi-objective forestry. Based on our findings, forestry carbon balance and the amount of deadwood correlate positively with each other, and both of them correlate negatively with harvested timber volume and economic profitability of forestry. Despite this, the simultaneous maximization of multiple objectives increased the overall production levels of several ecosystem services, which suggests that the management of boreal forests should be multi-objective to sustain the simultaneous provision of timber and other ecosystem services.

Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

2013 ◽  
Vol 3 (2) ◽  
pp. 94-106 ◽  
Author(s):  
M. Akbarizadeh ◽  
A. Daghbandan ◽  
M. Yaghoobi
Keyword(s):  
Genetic Algorithms ◽  
Water Treatment ◽  
Treatment Process ◽  
Data Sets ◽  
Raw Water ◽  
Multi Objective ◽  
Water Treatment Process ◽  
Coagulant Dosage ◽  
Predicted Values ◽  
Experimental Values

Coagulation-flocculation is the most important parts of water treatment process. Traditionally, optimum pre coagulant dosage is determined by used jar tests in laboratory. However; jar tests are time-consuming, expensive, and less adaptive to changes in raw water quality in real time. Soft computing can be used to overcome these limitations. In this paper, multi-objective evolutionary Pareto optimal design of GMDH Type-Neural Network has been used for modeling and predicting of optimum poly electrolyte dosage in Rasht WTP, Guilan, Iran, using Input - output data sets. In this way, multi-objective uniform-diversity genetic algorithms (MUGA) are then used for Pareto optimization of GMDH networks. In order to achieve this modeling, the experimental data were divided into train and test sections. The predicted values were compared with those of experimental values in order to estimate the performance of the GMDH network. Also, Multi Objective Genetic Algorithms (MOGA) are then used for optimization of influence parameters in pre coagulant (Poly electrolyte) dosage.

The Relationship between Metaheuristics Stopping Criteria and Performances

2014 ◽  
Vol 5 (3) ◽  
pp. 44-70 ◽  
Author(s):  
Mohamed-Mahmoud Ould Sidi ◽  
Bénédicte Quilot-Turion ◽  
Abdeslam Kadrani ◽  
Michel Génard ◽  
Françoise Lescourret
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Statistical Tests ◽  
Particle Swarm ◽  
Computational Time ◽  
Plant Management ◽  
Stopping Criterion ◽  
Nsga Ii ◽  
Multi Objective ◽  
Crowding Distance

A major difficulty in the use of metaheuristics (i.e. evolutionary and particle swarm algorithms) to deal with multi-objective optimization problems is the choice of a convenient point at which to stop computation. Indeed, it is difficult to find the best compromise between the stopping criterion and the algorithm performance. This paper addresses this issue using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-Objective Particle Swarm Optimization with Crowding Distance (MOPSO-CD) for the model-based design of sustainable peach fruits. The optimization problem of interest contains three objectives: maximize fruit fresh mass, maximize fruit sugar content, and minimize the crack density on the fruit skin. This last objective targets a reduction in the use of fungicides and can thus enhance preservation of the environment and human health. Two versions of the NSGA-II and two of the MOPSO-CD were applied to tackle this difficult optimization problem: the original versions with a maximum number of generations used as stopping criterion and modified versions using the stopping criterion proposed by the authors of (Roudenko & Schoenauer, 2004). This second stopping criterion is based on the stabilization of the maximal crowding distance and aims to stop computation when many generations are performed without further improvement in the quality of the solutions. A benchmark consisting of four plant management scenarios has been used to compare the performances of the original versions (OV) and the modified versions (MV) of the NSGA-II and the MOPSO-CD. Twelve independent simulations were performed for each version and for each scenario, and a high number of generations were generated for the OV (e.g., 1500 for the NSGA-II and 200 for the MOPSO-CD). This paper compares the performances of the two versions of both algorithms using four standard metrics and statistical tests. For both algorithms, the MV obtained solutions similar in quality to those of the OV but after significantly fewer generations. The resulting reduction in computational time for the optimization step will provide opportunities for further studies on the sustainability of peach ideotypes.

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