Porosity from artificial neural network inversion for Bermejo Field, Ecuador

Sergio, Dario

doi:10.1190/1.1816735

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Singh et al (2016) created back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field, they used as input sonic, density and resistivity log data; they found that porosity generated by their ANNs has a high degree of the correlation with the one generated from combined density and neutron data. Sun et al (2001) used ANN inversion to populate the porosity of a reservoir model for a producing zone in Bermejo Field. Al-Bulushi et al (2007) developed a methodology based on ANNs to predict water saturation using wire-line logs and Dean-Stark core data.…”

Section: Introductionmentioning

confidence: 99%

Porosity prediction from X-ray computed tomography logs (RHOB and PEF) using Artificial Neural Networks (ANN)

Ortiz¹,

Herrera²,

Santos³

2020

Bol. Geol.

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This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are trained so they can predict porosity from CT logs at a high resolution (0.625 mm). The ANNs validation and regression plots show that porosity predictions are good. High-resolution porosity models linked to CT images could contribute to enhancing the petrophysics model as they allow a more refined identification of intervals of interest due to the detailed measurement.

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Section: Introductionmentioning

confidence: 99%

Porosity prediction from X-ray computed tomography logs (RHOB and PEF) using Artificial Neural Networks (ANN)

Ortiz¹,

Herrera²,

Santos³

2020

Bol. Geol.

View full text Add to dashboard Cite

show abstract

“…The neuronal networks as a particular case of intelligent systems [Hertz et al, 1991;Rich & Knight, 1991;Setiono & Liu, 1996;Yao & Liu, 1998], have given promising results in fields like: modeling, analysis of time series, patterns recognition among others [Dow & Sietsma, 1991;Gallant, 1993;Back et al, 1998]. In the field of the geosciences this type of systems has contributed with conventional and no conventional developments of interpretation and processing [Heggland et al, 1999a;1999b;An & Moon, 1993;Johnston, 1993;Wang & Huang, 1993;Ping, 1994;Cai, 1994;Huang & Williamson, 1994;Zhang et al, 1995a;1995b, Sun et al, 2001Deker et al, 2001;Chengdang, 1993]. One open issue in high resolution inversion is that there is no way to obtain from seismic data the top and the base of a geologic formation with a thickness under 15 meters (approximately).…”

Section: Introductionmentioning

confidence: 99%