A comparative evaluation of global search algorithms in black box optimization of oil production: A case study on Brugge field

Foroud, Toomaj; Baradaran, Ali; Seifi, Abbas

doi:10.1016/j.petrol.2018.03.028

Cited by 40 publications

(8 citation statements)

References 35 publications

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“…In this case study, a 3D simulation of a black oil reservoir is used to develop the SPE-1 model. [65,66] describes detailed properties and specifications of the reservoir model, as shown in Figure 6(a). The SPE-1 model has a 10x10x3 grid block.…”

Section: A Case Studymentioning

confidence: 99%

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

et al. 2022

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The oil and gas industry faces difficulties in optimizing well placement problems. These problems are multimodal, non-convex, and discontinuous in nature. Various traditional and non-traditional optimization algorithms have been developed to resolve these difficulties. Nevertheless, these techniques remain trapped in local optima and provide inconsistent performance for different reservoirs. This study thereby presents a Surrogate Assisted Quantum-behaved Algorithm to obtain a better solution for the well placement optimization problem. The proposed approach utilizes different metaheuristic optimization techniques such as the Quantum-inspired Particle Swarm Optimization and the Quantum-behaved Bat Algorithm in different implementation phases. Two complex reservoirs are used to investigate the performance of the proposed approach. A comparative study is carried out to verify the performance of the proposed approach. The result indicates that the proposed approach provides a better net present value for both complex reservoirs. Furthermore, it solves the problem of inconsistency exhibited in other methods for well placement optimization.

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Section: A Case Studymentioning

confidence: 99%

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

et al. 2022

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“…Hence, this type of synthetic reservoir model is exploited in this study. The detailed characteristics and specification of this reservoir model are discussed in the literature [59] and shown in Figure 5. There are 10 × 10 × 3 grid blocks in the First SPE model.…”

Section: Description Of Case Studiesmentioning

confidence: 99%

Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization

et al. 2020

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The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir.

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“…Detailed information on the workflow and the mathematical formulations used in this algorithm have been obtained by many researchers [54,55,57]. SADE has been successfully implemented in many application in the petroleum industry, such as oil production optimization [58] and prediction of spud mud rheology [59].…”

Section: Differential Evolution (De) Is a Population-based Search Tecmentioning

confidence: 99%

A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

et al. 2019

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Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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A comparative evaluation of global search algorithms in black box optimization of oil production: A case study on Brugge field

Cited by 40 publications

References 35 publications

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization

A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

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