Optimization of Well Placement

1999 ◽  
Vol 122 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Baris Guyaguler ◽  
Roland Horne
Keyword(s):  
Hybrid Algorithm ◽  
Single Point ◽  
Optimization Technique ◽  
Search Space ◽  
Complex Problem ◽  
Production Optimization ◽  
Optimal Placement ◽  
Optimization Approach ◽  
Injection Wells ◽  
Well Placement

Optimal placement of oil, gas or water wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, as well as economic parameters. An optimization approach that enables the evaluation of all these information is presented. A hybrid of the genetic algorithm (GA) forms the basis of the optimization technique. GA operators such as uniform, single-point, two-point crossover, uniform mutation, elitism, tournament and fitness scaling were used. An additional operator that employs kriging is proposed. The GA was hybridized with the polytope algorithm, which makes use of the trends in the search space. The hybrid algorithm was tested on a set of mathematical functions with different characteristics in order to determine the performance sensitivity to GA operators and hybridization. Simple test cases of oil production optimization on 16×16 simulation grids with known optimum well locations were carried out to verify the hybrid GA results. Next, runs were carried out for a 32×32 problem. The locations of a production and injection well were optimized in the case of three existing producers. Exhaustive runs were carried out for these cases to determine the effects of the operators, hybridization and the population size on the performance of the algorithm for well placement problems. Subsequently, the optimum configuration of two injection wells were determined with two existing producers in the field. It was observed that the hybrid algorithm is able to reduce the required number of simulations substantially over simple GA. [S0195-0738(00)00502-1]

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

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.

scholarly journals A HYBRID OPTIMIZATION APPROACH FOR COMPLEX NONLINEAR OBJECTIVE FUNCTIONS

2018 ◽  
pp. 102-112
Author(s):  
Samuel O. Obadan ◽  
Zenghui Wang
Keyword(s):  
Objective Function ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Search Space ◽  
Bench Mark ◽  
Optimization Approach ◽  
Stochastic Algorithm ◽  
Objective Functions ◽  
Function Calls

With respect to the ‘no free launch’ theorem, no single algorithm has a better performance when tested against a completely stochastic algorithm on all objective functions. Consequently, choosing the best algorithm for a particular problem is often more of an art than science. The complexity of an objective function can be determined by certain features such as the modality, the basins, the valleys, the separability, and the dimensionality of the objective function. While the separability and modality contribute to the complexity of the function, the dimensionality and domain range increases the function’s search space exponentially. In this paper, the authors analyze the algorithmic constructs of Simulated Annealing (SA), Cuckoo-search (CK), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) along with two hybrid paradigms. In addition, an extensive comparative study was conducted using 30 standard bench mark functions to demonstrate how an ingenious hybrid algorithm could significantly shorten the amount of function calls (generations) needed to attain the optimal or rather near optimal solution for almost any complex objective function. Results from empirical analysis unveil the precision, robustness and success of the hybrid algorithm (without compromising run-time complexity) over its counterparts.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

2013 ◽  
Vol 860-863 ◽  
pp. 2040-2045 ◽  
Author(s):  
Xiao Hua Feng ◽  
Yu Yao He ◽  
Juan Yu
Keyword(s):  
Search Space ◽  
Complex Problem ◽  
Evolution Strategies ◽  
Power Losses ◽  
Bacterial Foraging Optimization ◽  
Thermal Generation ◽  
Bacterial Foraging ◽  
Global And Local ◽  
Generation Test ◽  
Bacterial Foraging Optimization Algorithm

This paper presents a novel modified bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

Production Optimization for Second Stage Field Development Using ICD and Advanced Well Placement Technology

2008 ◽  
Author(s):  
D. Maggs ◽  
A.G. Raffn ◽  
Francisco Porturas ◽  
J. Murison ◽  
F. Tay ◽  
...  
Keyword(s):  
Production Optimization ◽  
Well Placement ◽  
Field Development ◽  
Second Stage

Advanced Multi-Disciplinary Simulation Workflow for Well Placement, Hydraulic Fracturing and Production Optimization Applied to the Diyab Unconventional Play in Onshore UAE

2021 ◽  
Author(s):  
Hamid Pourpak ◽  
Samuel Taubert ◽  
Marios Theodorakopoulos ◽  
Arnaud Lefebvre-Prudencio ◽  
Chay Pointer ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
United Arab Emirates ◽  
Reservoir Characterization ◽  
Horizontal Wells ◽  
Production Optimization ◽  
Production Performance ◽  
Well Placement ◽  
Fully Integrated ◽  
Sector Model ◽  
The Impact

Abstract The Diyab play is an emerging unconventional play in the Middle East. Up to date, reservoir characterization assessments have proved adequate productivity of the play in the United Arab Emirates (UAE). In this paper, an advanced simulation and modeling workflow is presented, which was applied on selected wells located on an appraisal area, by integrating geological, geomechanical, and hydraulic fracturing data. Results will be used to optimize future well landing points, well spacing and completion designs, allowing to enhance the Stimulated Rock Volume (SRV) and its consequent production. A 3D static model was built, by propagating across the appraisal area, all subsurface static properties from core-calibrated petrophysical and geomechanical logs which originate from vertical pilot wells. In addition, a Discrete Fracture Network (DFN) derived from numerous image logs was imported in the model. Afterwards, completion data from one multi-stage hydraulically fracked horizontal well was integrated into the sector model. Simulations of hydraulic fracturing were performed and the sector model was calibrated to the real hydraulic fracturing data. Different scenarios for the fracture height were tested considering uncertainties related to the fracture barriers. This has allowed for a better understanding of the fracture propagation and SRV creation in the reservoir at the main target. In the last step, production resulting from the SRV was simulated and calibrated to the field data. In the end, the calibrated parameters were applied to the newly drilled nearby horizontal wells in the same area, while they were hydraulically fractured with different completion designs and the simulated SRVs of the new wells were then compared with the one calculated on the previous well. Applying a fully-integrated geology, geomechanics, completion and production workflow has helped us to understand the impact of geology, natural fractures, rock mechanical properties and stress regimes in the SRV geometry for the unconventional Diyab play. This work also highlights the importance of data acquisition, reservoir characterization and of SRV simulation calibration processes. This fully integrated workflow will allow for an optimized completion strategy, well landing and spacing for the future horizontal wells. A fully multi-disciplinary simulation workflow was applied to the Diyab unconventional play in onshore UAE. This workflow illustrated the most important parameters impacting the SRV creation and production in the Diyab formation for he studied area. Multiple simulation scenarios and calibration runs showed how sensitive the SRV can be to different parameters and how well placement and fracture jobs can be possibly improved to enhance the SRV creation and ultimately the production performance.

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