Application of Neural Networks and Cokriging for the Mapping of a Reservoir Property

Yoshioka, Katsuhei; Shimada, Nobusuke

doi:10.3997/2214-4609.201406834

Cited by 4 publications

(2 citation statements)

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“…Some of the important articles with special emphasis on RC using 3D seismic data include Boadu, (1997) ;Nikravesh, (1998a,b);Nikravesh et al, (1998); Chawathe et al, (1997); Yoshioka et al, (1996); Schuelke et al, (1997); Monson and Pita, (1997); Aminzadeh and Chatterjee, (1985); Finol and Jing (2002); Hou et al, (2008); Kadkhodaie-Ilkhchi et al, (2009); Al-Dousari and Garrouch (2013); Na 'imi, et al, (2014); Iturrarán-Viveros and Parra (2014).…”

Section: Introductionmentioning

confidence: 99%

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

Verma

Chaki

Routray

et al. 2014

Journal of Applied Geophysics

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In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.

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Section: Introductionmentioning

confidence: 99%

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

Verma

Chaki

Routray

et al. 2014

Journal of Applied Geophysics

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“…of the problem(112) we can apply finite element method. Local stiffness matrix (in local coordinate system) has the following formmatrix in global coordinate system has the following form:Ke = Jcce)T (Be)T DeBeCe dx vewhere rotation matrix has the following form sin(ae) cos( a e)Local load vector in global coordinate system: Qe = Jcce/ (Ne/ ndx ve The structure is shown in the Fig.…”

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confidence: 99%