Optimization of surface condensate production from natural gases using artificial intelligence

Al-Farhan, Farhan A.; Ayala, Luis F.

doi:10.1016/j.petrol.2006.05.001

Cited by 15 publications

(24 citation statements)

References 4 publications

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“…The relevance, S, is the term used to quantify the influence of each input parameter on the output of the network (Belue and Bauer, 1995;Redondo and Espinosa, 1999;Al-Farhan, 2005). The relationship between the relevance term and the weight relevances, S ij , is given by the following equation:…”

Section: Sensitivity Analysismentioning

confidence: 99%

A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

Basbug¹,

Karpyn²

2008

IJOGCT

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An artificial neural network (ANN) was designed and tested in the present study to examine the correlation between permeability estimations and porous medium properties, such as porosity, specific surface area, and irreducible water saturation. The network developed in this work uses soft computing techniques to investigate the absolute permeability dependence on pore-scale characteristics of carbonate reservoirs. The present study indicates that ANN generated permeability values are consistent with those obtained from core analysis. Results from this study confirm the complex relationship among permeability, porosity, specific surface area and irreducible water saturation of carbonate reservoirs, and suggest that variations in specific surface area affect the magnitude of irreducible water saturations, thus creating an apparent dependence of permeability on irreducible water saturation. Additional observations support a direct relationship between porosity and permeability, and an inverse relationship between specific surface area and permeability. [

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Section: Sensitivity Analysismentioning

confidence: 99%

A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

Basbug¹,

Karpyn²

2008

IJOGCT

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“…The relevance, , is the term used to quantify the influence of each input parameter on the output of the network S 25,26,27 . The relationship between the relevance term and the weight relevances, , is given by the following where represents the weight of each connection between inputs and the neurons in the hidden layer.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Estimation of Permeability from Porosity, Specific Surface Area, and Irreducible Water Saturation using an Artificial Neural Network

Basbug¹,

Karpyn²

2007

Proceedings of Latin American &Amp; Caribbean Petroleum Engineering Conference

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fax 01-972-952-9435. AbstractAn Artificial Neural Network (ANN) was designed and tested in the present study to examine the correlation between permeability estimations and porous medium properties, such as porosity, specific surface area, and irreducible water saturation. The network developed in this work is a predictive tool that uses soft computing techniques to estimate absolute permeability of carbonate reservoirs. The Artificial Neural Network toolbox of MATLAB ® R2006b and the Feed Forward Error Back Propagation methodology were used in the construction of the network. Carbonate reservoir field data presented in the literature were utilized in the training, testing, and validation of the proposed model. The present study indicates that ANN generated permeability values are consistent with those obtained from core analysis. Results from this study confirm the complex relationship among permeability, porosity, specific surface area and irreducible water saturation of carbonate reservoirs, and suggest that variations in specific surface area affect the magnitude of irreducible water saturations, thus creating an apparent dependence of permeability on irreducible water saturation. Additional observations support a direct relationship between porosity and permeability, and an inverse relationship between specific surface area and permeability.

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“…Unfortunately, details on the optimization procedure was not provided. Al-Farhan and Ayala [8] trained an artificial neural network (ANN) for a 3 stage separation train in order to predict optimal second stage separator pressure. First stage pressure as well as fluid composition was varied, providing a exhaustive number of data sets.…”

Section: Introductionmentioning

confidence: 99%

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

Andreasen

2019

Preprint

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In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.

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Optimization of surface condensate production from natural gases using artificial intelligence

Cited by 15 publications

References 4 publications

A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence

Estimation of Permeability from Porosity, Specific Surface Area, and Irreducible Water Saturation using an Artificial Neural Network

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

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