Well reservoir model identification using translation and scale invariant higher order networks

Kumoluyi, A.O.; Daltaban, T S; Končar, Nenad; Jones, A.; Archer, J. S.

doi:10.1007/bf01414074

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Although AI (Artificial Intelligence) techniques have been used in many branches of the petroleum engineering including well log interpretation (Mohaghegh et al, 1994;Al-Bulushi et al, 2012), seismic interpretation (Boerner et al, 2003), well test model recognition (Allian and Houze, 1995;Al-Kaabi and Lee, 1990;Ershaghi et al, 1993;Athichanagorn and Horne, 1995;Kumoluyi et al, 1995;Alajmi and Ertekin, 2007;Kharrat and Razavi, 2008;Adibifard et al, 2014), and fluid properties estimation (Gharbi et al, 1999;Elsharkwy and Gharbi, 2001;Osman et al, 2001;Huang et al, 2003), there are a few studies regarding the applications of AI in well test nonlinear regression analysis. Güyagüler et al (2001) used GA (Genetic Algorithm) to recognize reservoir model using nonlinear regression analysis technique.…”

Section: Employing Artificial Intelligence Techniques In Well Test An...mentioning

confidence: 99%

Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

Adibifard

Bashiri

Roayaei

et al. 2016

International Journal of Applied Metaheuristic Computing

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Since two of the most important disadvantages of the classical nonlinear regression methods, such as Levenberg-Marquardt (LM), are to calculate error derivative function and use an initial point to get the results, PSO algorithm, which lies in the category of population based meta-heuristic algorithms, is used in this study to implement nonlinear regression in well test analysis. Root Mean Square Error (RMSE) over pressure and pressure derivative data are used in the cost function formulation and the multi-objective problem is reduced to single objective one by including the weight for each of the cost functions related to pressure and pressured derivative data. The superiority of the procedure developed in this study is verified through a simulated drawdown test and one field case. Error comparison over estimated reservoir parameters and analysis of 95% confidence interval reveal that implemented PSO algorithm can be used accurately to estimate reservoir properties.

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Section: Employing Artificial Intelligence Techniques In Well Test An...mentioning

confidence: 99%

Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

Adibifard

Bashiri

Roayaei

et al. 2016

International Journal of Applied Metaheuristic Computing

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A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter

Bazargan

Adibifard

2017

Neural Comput & Applic

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Estimation of subsurface petrophysical properties using different stochastic algorithms in nonlinear regression analysis of pressure transients

Aboosadi

Rooeentan

Adibifard

2018

Journal of Applied Geophysics

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Well reservoir model identification using translation and scale invariant higher order networks

Cited by 3 publications

References 13 publications

Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter

Estimation of subsurface petrophysical properties using different stochastic algorithms in nonlinear regression analysis of pressure transients

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