The use of an artificial neural network to estimate natural gas/water interfacial tension

Zhang, Jiyuan; Feng, Qihong; Zhang, Xianmin; Zhang, Xueli; Yuan, Nuo; Wen, Shengjun; Wang, Shoulei; Zhang, Angang

doi:10.1016/j.fuel.2015.04.057

Cited by 30 publications

(17 citation statements)

References 30 publications

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“…In the past decades, ANN techniques have been extensively implemented for solving problems and for prediction tasks in the petroleum industry [56] . For instance, Jiyuan et al [57] studied the natural gas/water interfacial tension (IFT) using ANN. They acquired experimental data of pure methane and synthetic natural gas from the literature to develop their model.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

116

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Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

116

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“…From laboratory side, trapping of gas by liquids is weakly dependent on fluid pressure, temperature and displacement rate, ([ [1], [5], [6], [7]]). Trapped gas saturation can be measured experimentally using different techniques.…”

Section: Measuring This Correlationmentioning

confidence: 99%

A fast method for trapped gas determination

et al. 2019

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Gas reservoirs are mainly produced by depletion with an aquifer rise; reservoir simulation requires two main SCAL inputs: the amount of trapped gas by the aquifer (residual gas saturation: Sgr) and the relative permeability to water due to aquifer flooding. As it is quasi impossible to predict aquifer strength, the primary SCAL input for reservoir simulation is the Sgr. The recovery factor is directly defined by initial and residual gas saturations. In fact, the residual gas saturation Sgr highly depends on the initial gas saturation Sgi and there is no universal petrophysical parameter governing the shape of this curve. This relationship can be described by several different models (Land, Aissaoui…). While Land’s model is widely used, the Aissaoui model better fits the experimental results (Suzanne et al. 2003), at least for homogeneous sandstones. For a given threshold of initial gas saturation Sg0, this relationship typically exhibits a plateau at high Sgi>Sg0 and an increasing linear trend at low Sgi<Sg0. The challenge here is to properly estimate the value of the Sg0 threshold. Classical laboratory method would require one experiment per point in the Sgr/Sgi plot, and therefore can be achieved in a matter of months. Here we propose a laboratory method allowing the acquisition of the Sgr/Sgi curve in a few days. The proposed method combines centrifugation and capillary rise under imaging. First, the centrifuge allows creating a saturation profile along a sample; measured by NMR. Then, capillary rise is used to capture Sgr under NMR monitoring. By adding NMR imaging, this technique allows combining the benefits of centrifugation to explore a wide range of Sgi; and the ease and cost effectiveness of capillary rise to measure the resulting Sgr. Therefore, at a timescale close to a traditional capillary rise, the proposed technique avoids Land extrapolation and provides a direct measurement of Sgr in a wide range of Sgi. As an additional benefit, the combination of NMR and centrifuge can provide at the same time a direct measurement of capillary pressure, providing information on the gas in place and potential imbibition process in the reservoir.

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“…We use T-test to analyze the normality and F-test to assess the homoscedasticity. The null hypothesis is evaluated by T-test where the data are coming from an unspecified normal distribution (Zhang et al 2015). If the test result is zero, the null hypothesis cannot be rejected at the 5% significance level, in which case the data are normal distribution.…”

Section: Normality and Homoscedasticity Testsmentioning

confidence: 99%

A prediction formula for ratio of injection–production control area in triangle well pattern

Chen

Feng

Zhang

et al. 2017

J Petrol Explor Prod Technol

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The ratio of injection-production control area is an important aspect in evaluating the development effect and calculating oilfield development index. Conventional approaches to determine the ratio of injection-production control area neglect the heterogeneity of the reservoir and require long time simulating and a lot of complex iterations. This paper establishes a prediction formula to quickly determine the ratio of injection-production control area in triangle well pattern. A total of 410 sets of streamline models are built to acquire the database. Permeability, oil saturation, injection-production pressure drop and injector-producer spacing are selected as independent variables to establish the prediction formula by multivariate parametric regression. Based on error analysis and application, the accuracy of this prediction formula is approved. Results indicate that the prediction formula has a correlation coefficient R 2 of 0.96, representing a satisfactory performance. Normality and homoscedasticity tests and standardized residual diagnostics demonstrate the statistical significance of the results. The application of this prediction formula shows an excellent match between the predicted and actual injection-production area, which further confirms the accuracy of this prediction formula. The established prediction formula can effectively and accurately decide the ratio of injection-production control area for waterflooding reservoir.

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The use of an artificial neural network to estimate natural gas/water interfacial tension

Cited by 30 publications

References 30 publications

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

A fast method for trapped gas determination

A prediction formula for ratio of injection–production control area in triangle well pattern

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