A Genetic Algorithm Integrated with Monte Carlo Simulation for the Field Layout Design Problem

Sales, Leonardo; Pitombeira‐Neto, Anselmo Ramalho; Prata, Bruno de Athayde

doi:10.2516/ogst/2018017

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…Genetic based calculations have been commonly used in petroleum industry as a promising approach in estimating several parameters [34][35][36]. In recent years, Gene Expression Programming (GEP) [37] as an evolutionary algorithm, has been increasingly applied in different disciplines of petroleum and chemical engineering.…”

Section: Introductionmentioning

confidence: 99%

Application of a new approach for modeling the oil field formation damage due to mineral scaling

Rostami

Shokrollahi

Shahbazi

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Mineral scaling has been considered a great concern for developing the oil production from the underground petroleum reservoirs. One of the main causes of this phenomenon is known as the chemical incompatibility of injected brine, frequently sea water, with the reservoir brine leading to the deposition of various supersaturated salts such as calcium carbonate, calcium sulfate and barium sulfate. In present communication, an evolutionary approach namely, Gene Expression Programming (GEP), was employed for rigorous modeling of formation damage by mineral scaling of mixed sulfate salt deposition. At first, a large databank of damaged permeability datapoints as a function of injected volume, injection flowrate, temperature, differential pressure and ionic concentrations of the existing chemical species in the porous media was employed. In this regard, a user-friendly correlation was extended for the first time by the aforementioned technique in the literature. Professional evaluation of the suggested GEP-based model was implemented by different statistical parameters and appealing visualization tools. Having proposed the GEP-based correlation, statistical parameters of the Average Absolute Relative Deviation Percent (AARD%) of 0.640% and determination coefficient (R2) of 0.984 was calculated. Accordingly, it is demonstrated that the proposed model has a superior performance and great potential for efficient prediction of damaged permeability due to the mixed sulfate salt scaling. Moreover, the implemented outlier diagnosis technique verified the validity of the databank used for modeling, as well as the high robustness of the suggested model was confirmed. In conclusion, the developed correlation in this work can be of enormous practical value for skillful engineers and scientists in any academic study and industrial applications dealing with mixed salt deposition.

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Section: Introductionmentioning

confidence: 99%

Application of a new approach for modeling the oil field formation damage due to mineral scaling

Rostami

Shokrollahi

Shahbazi

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…Later research (Garcia-Diaz et al 1996) was conducted to overcome this and other problems. Rosa and Ferreira Filho (2013), Rodrigues et al (2016) andSales et al (2018) proposed methods of optimizing subsea layout by locating and allocating topsides and manifolds. Rosa and Ferreira Filho (2013) developed an exhaustive search model that maximizes NPV, while Rodrigues et al (2016) proposed an integer linear programming model for the same problem.…”

Section: Previous Workmentioning

confidence: 99%

“…Rosa and Ferreira Filho (2013) developed an exhaustive search model that maximizes NPV, while Rodrigues et al (2016) proposed an integer linear programming model for the same problem. Sales et al (2018) proposed a Monte Carlo simulation approach combined with a genetic algorithm to address uncertainties in the placement of topsides and manifolds. In these studies, no other subsea equipment was considered besides flow-lines and manifolds.…”

Section: Previous Workmentioning

confidence: 99%

Superstructure optimization of subsea processing layouts

Sales

Stanko

Jäschke

2023

J Petrol Explor Prod Technol

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As the oil and gas industry expands the use of subsea processing, the complexity of subsea layouts increases, making manual design processes cumbersome and suboptimal. Here we propose a method to support subsea field design where optimization is performed on a model of the subsea system, to maximize the net present value of the project. The proposed mixed-integer nonlinear (MINLP) model is solved to compute a global optimum design considering constraints in production, equipment duties and cost, and reliability and maintenance aspects. The subsea layout, equipment capacity, oil and gas production rates, and system pressures are optimized. The method was applied on a synthetic field based on the Goliat field in the Barents Sea. The method successfully finds the best designs, while the second and third-best layouts give general insights for subsea processing layout optimization.

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“…At last, in the production area, Sales et al (2018) use a Monte Carlo simulation approach to deal with uncertainties for the field design layout problem. The sampling method objective is to generate many production scenarios of an oil field in order to assign the wells flow rates a probability distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

Using data analytics to quantify the impact of production test uncertainty on oil flow rate forecast

Monteiro

Duque

Chaves

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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In general, flow measurement systems in production units only report the daily total production rates. As there is no precise control of individual production of each well, the current well flow rates and their parameters are determined when production tests are conducted. Because production tests are performed periodically (e.g., once a month), information about the wells is limited and operational decisions are made using data that are not updated. Meanwhile, well properties and parameters from the production test are typically used in multiphase flow models to forecast the expected production. However, this is done deterministically without considering the different sources of uncertainties in the production tests. This study aims to introduce uncertainties in oil flow rate forecast. To do this, it is necessary to identify and quantify uncertainties from the data obtained in the production tests, consider them in production modeling, and propagate them by using multiphase flow simulation. This study comprises two main areas: data analytics and multiphase flow simulation. In data analytics, an algorithm is developed using R to analyze and treat the data from production tests. The most significant stochastic variables are identified and data deviation is adjusted to probability distributions with their respective parameters. Random values of the selected variables are then generated using Monte Carlo and Latin Hypercube Sampling (LHS) methods. In multiphase flow simulation, these possible values are used as input. By nodal analysis, the simulator output is a set of oil flow rate values, with their interval of occurrence probabilities. The methodology is applied, using a representative Brazilian offshore field as a case study. The results show the significance of the inclusion of uncertainties to achieve greater accuracy in the multiphase flow analysis of oil production.

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A Genetic Algorithm Integrated with Monte Carlo Simulation for the Field Layout Design Problem

Cited by 9 publications

References 40 publications

Application of a new approach for modeling the oil field formation damage due to mineral scaling

Application of a new approach for modeling the oil field formation damage due to mineral scaling

Superstructure optimization of subsea processing layouts

Using data analytics to quantify the impact of production test uncertainty on oil flow rate forecast

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