Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm – A case study

Amiri, Morteza; Ghiasi-Freez, Javad; Golkar, Behnam; Hatampour, Amir

doi:10.1016/j.petrol.2015.01.013

Cited by 47 publications

(14 citation statements)

References 15 publications

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“…ICA: ICA is known as one of the most recently‐developed algorithms as a social counterpart method. This algorithm was developed by Atashpaz‐Gargari and Lucas, and shows efficient performance in many problems such as well placement optimization, water saturation in tight shale sandstone reservoirs, etc …”

Section: Optimization Techniquesmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Section: Optimization Techniquesmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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show abstract

“…Therefore, a large and comprehensive data bank including the petrophysical properties noted above is provided in this study in order to develop reliable models for accurate estimation of water saturation of shale and tight gas reservoirs. To this end, the core and well log data [8] from 12 wells of Mesaverde group tight reservoir located in the largest Western US has been applied for the model development. More than 4000 data points has been considered from one of the tight gas sand basins in this study.…”

Section: Databasementioning

confidence: 99%

“…Amiri et al [8] developed an ANN model to estimate water saturation in tight and shale gas reservoir. They also used the imperialist competitive algorithm as optimization methodology to couple with the ANN algorithm.…”

Section: Introductionmentioning

confidence: 99%

New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

Kamari¹,

Sheng²

2018

OJOGas

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The determination of water saturation is a key step for the reservoir characterization and prediction of future reservoir performance in terms of production. The importance of water saturation has been further identified when the reservoirs refer to rocks with low porosity and permeability such as shale and tight formations. In this communication, two advanced artificial intelligence strategies consisting of least square support vector machine (LSSVM) and gene expression programming (GEP) have been applied in order to develop reliable predictive models for the calculation of water saturation of shale and tight reservoirs. To this end, an extensive core and log data bank has been analysed from 12 wells of a Mesaverde group tight reservoir located in the largest Western US. The results indicate that the estimated water saturation data by the models developed in this study are in satisfactory agreement with the actual log data. Furthermore, new methods proposed in this study are useful for the characterization of shale and tight reservoirs and can be applied to the relevant software.

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“…Nonetheless, there is still, a certain extent of error in manual empirical calculations due to its non-linear nature and assumptions made for each model to account for uncertainties. [5,6,7] Water saturation is usually derived from resistivity log, however, most of the water saturation models evolved from the very first and basic Archie's equation with considerations of conductivity contributed by clay or shale presence. The core assumption of Archie's equation is that the formation is considered as clean sands with no shale or clay presence.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Porosity and Water Saturation Using Neural Networks in Shaly Sand Reservoirs, Western Deseret, Egypt

Hamada¹,

Sakka

Nyein

2020

Journal of Petroleum and Mining Engineering

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Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm – A case study

Cited by 47 publications

References 15 publications

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

Prediction of Porosity and Water Saturation Using Neural Networks in Shaly Sand Reservoirs, Western Deseret, Egypt

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