Prediction of the aqueous solubility of BaSO4 using pitzer ion interaction model and LSSVM algorithm

Safari, Hossein; Shokrollahi, Amin; Jamialahmadi, M.; Ghazanfari, Mohammad Hossein; Bahadori, Alireza; Zendehboudi, Sohrab

doi:10.1016/j.fluid.2014.04.010

Cited by 59 publications

(12 citation statements)

References 56 publications

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“…One of the robust sensitivity analysis strategies is through the use of the analysis of variance (i.e. ANOVA) method . Using this analysis, the dependency of the dependent variable (i.e.…”

Section: Review Of the Methodology Results And Discussionmentioning

confidence: 99%

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

2017

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One of the detrimental issues in upstream oil industry is permeability impairment in oil reservoirs. The permeability may be reduced because of scale deposition originated from injected water incompatibility with the in‐situ water. To tackle this issue and monitor permeability impairment owing to scale deposition, an inventive evolutionary approach of Artificial Neural Network (ANN) is utilized which is based on the bio‐inspired science. This approach is optimized by various optimization algorithms which provide a high‐precision decision‐making process with low uncertainty associated with the interconnected weights of the developed neural network model. The constructed intelligent approaches are evaluated using extensive experimental data from the open literature. Moreover, two regression models are developed to highlight the robustness and precision of the addressed techniques. For model validation, the predictions obtained from the smart technique as well as the regression method are compared with the formation damage experimental data. Based on various performance criteria, it was obtained that the predictions from the optimized genetic algorithm have infinitesimal average relative and absolute deviation from the experimental data (< 0.1 %). The results obtained in the current research prove that implication of HGAPSO‐ANN in monitoring of formation damage associated with scale deposition in porous media results in reliable estimation of permeability impairment. This can result in designing a more reliable reservoir simulation model that captures the permeability impairment due to scale deposition, hence providing thorough action plans for developing EOR technologies that may cause scale deposition‐related formation damage.

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“…One of the robust sensitivity analysis strategies is through the use of the analysis of variance (i.e. ANOVA) method . Using this analysis, the dependency of the dependent variable (i.e.…”

Section: Review Of the Methodology Results And Discussionmentioning

confidence: 99%

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

2017

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“…Outliers are observations that deviate from the bulk of data, which are achieved entirely under the same conditions . Normally, in all projects concerned with data collection, there is a possibility of outliers or doubtful data creeping into recorded measurements.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Intelligent Computational Scheme for Determination of Refractive Index of Crude Oil Using SARA Fraction Analysis

et al. 2015

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Asphaltene precipitation and consequent deposition may result in several operational problems ranging from the wellbore to transmission lines. Despite several studies, stability conditions of the asphaltene in crude oil are still a challenging issue and a potential area of investigation. Refractive Index (RI) is a parameter indicative of the region at which asphaltene becomes stable. In this study, a Committee Machine Intelligent System (CMIS) is incorporated to predict the RI of different crude oils through the existing SARA fractions experimental data. The CMIS itself utilizes different artificial neural networks: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Least Squares Support Vector Machine (LSSVM). By comparing the results of each artificial neural network with the final output, it was demonstrated that the CMIS increases the generalization capability of the utilized artificial network. The results were compared with two well‐known classical correlations. It was proven that the proposed intelligent system outperforms the classical correlations. At the end, outlier detection was performed to identify data which deviate from the bulk of the data points and obtain the applicability domain of the CMIS model.

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“…and exp. stand for the predicted, and experimental or actual data, respectively, and n s is the initial population number [16] [17].…”

Section: Databasementioning

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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Prediction of the aqueous solubility of BaSO4 using pitzer ion interaction model and LSSVM algorithm

Cited by 59 publications

References 56 publications

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

A Hybrid Intelligent Computational Scheme for Determination of Refractive Index of Crude Oil Using SARA Fraction Analysis

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

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