Well facies based supervised classification of prestack seismic: Application to a turbidite field

Hami-Eddine, Kamal; Klein, Pascal; Richard, Loic

doi:10.1190/1.3255223

Cited by 8 publications

(3 citation statements)

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“…As the linearity between the Z-factor and X is not particularly high, another model is needed to better estimate the Z-factor based on machine learning. Recently, neural networks have been widely used to solve non-linear problems due to their advantages over regression models [39,40]. Neural networks are among the supervised-learning tools that provide a function based on a network of input(s) and output(s) [41].…”

Section: Model Selectionmentioning

confidence: 99%

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Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Ghanem

Gouda²,

Alharthy

et al. 2022

Energies

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Simulating the phase behavior of a reservoir fluid requires the determination of many parameters, such as gas–oil ratio and formation volume factor. The determination of such parameters requires knowledge of the critical properties and compressibility factor (Z factor). There are many techniques to determine the compressibility factor, such as experimental pressure, volume, and temperature (PVT) tests, empirical correlations, and artificial intelligence approaches. In this work, two different models based on statistical regression and multi-layer-feedforward neural network (MLFN) were developed to predict the Z factor of natural gas by utilizing the experimental data of 1079 samples with a wide range of pseudo-reduced pressure (0.12–25.8) and pseudo reduced temperature (1.3–2.4). The statistical regression model was proposed and trained in R using the “rjags” package and Markov chain Monte Carlo simulation, while the multi-layer-feedforward neural network model was postulated and trained using the “neural net” package. The neural network consists of one input layer with two anodes, three hidden layers, and one output layer. The input parameters are the ratio of pseudo-reduced pressure and the pseudo-reduced temperature of the natural hydrocarbon gas, while the output is the Z factor. The proposed statistical and MLFN models showed a positive correlation between the actual and predicted values of the Z factor, with a correlation coefficient of 0.967 and 0.979, respectively. The results from the present study show that the MLFN can lead to accurate and reliable prediction of the natural gas compressibility factor.

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Section: Model Selectionmentioning

confidence: 99%

“…Recently, neural networks have been widely used to solve non-linear problems due to their advantages over regression models [39,40]. One of the common neural networks is the multi-layer feedforward (MLFN) neural network, which consists of three main types of layers: an input layer, one or more hidden layers, and an output layer [49].…”

Section: Mlfn Modelmentioning

confidence: 99%

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Ghanem

Gouda²,

Alharthy

et al. 2022

Energies

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“…It allows for the generation of lithology probabilities from a combination of quantitative rock typing analysis at wells and seismic data at the well location (Hami-Eddine et al, 2009). The validity of the method has been demonstrated through the direct use of angle gathers (representing the seismic data) combined with well facies analysis, to predict the lithology and fluid content.…”

Section: Introductionmentioning

confidence: 99%

A new technique for lithology and fluid content prediction from prestack data: An application to a carbonate reservoir

et al. 2015

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One of the leading challenges in hydrocarbon recovery is predicting rock types/fluid content distribution throughout the reservoir away from the boreholes because rock property determination is a major source of uncertainty in reservoir modeling studies. Spatial determination of the lateral and vertical heterogeneities has a direct impact on a reservoir model because it will affect the property distributions. An inappropriate determination of the facies distribution will lead to unrealistic reservoir behavior. Because these data can take different forms (lithologs, cuttings, and for seismic, poststack, and prestack attributes) and have different resolutions, the manual integration of all the information can be tedious and is sometimes impractical. We developed a new neural network-based methodology called democratic neural network association (DNNA). The DNNA method was trained using lithology logs from wells simultaneously with prestack seismic data. This technique, using a probabilistic approach, aims to find patterns in seismic that will predict lithology distribution and uncertainty. IntroductionThe economic viability of a field is dependent on the quality and accuracy of lithology distribution prediction, as well as by the heterogeneity of a potential reservoir. These components are the keys to successful hydrocarbon exploration and production. The rise in unconventional resource prospecting and the increasing complexity of conventional plays have made accurate lithology prediction even more critical. All relevant data must be used optimally to determine lithology at the prospect scale with the highest degree of resolution, resulting in the most geologically meaningful lithology distribution. Risk increases with complexity, however, and the probability of success and the integration of uncertainty into the nature and distribution of lithology must be taken into account in any approach that tries to predict lithology.Conventional approaches are mainly based on 2D or 3D analyses of inverted data to describe the elastic properties of the reservoir. However, precise lithology description in such attribute spaces often overlaps. This makes it difficult to clearly differentiate, for example, intermediate-type facies such as thin interbedded layers. The result is nonunique and highly sensitive to facies interpretation. It, therefore, becomes critical to estimate reservoir connectivity, as some lower qual-

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Delineation of thin and discrete sand reservoir facies from shale-dominated Kopili Formation (Middle to Late Eocene) using the post-stack seismic inversion and neural network algorithm: A case study from Assam Basin, India

et al. 2023

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Well facies based supervised classification of prestack seismic: Application to a turbidite field

Abstract: This article proposes a new approach to build a 3D geological model calibrated to well data using an adaptive neural network taking into account pre stack or post stack seismic behavior.

Cited by 8 publications

References 1 publication

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

A new technique for lithology and fluid content prediction from prestack data: An application to a carbonate reservoir

Delineation of thin and discrete sand reservoir facies from shale-dominated Kopili Formation (Middle to Late Eocene) using the post-stack seismic inversion and neural network algorithm: A case study from Assam Basin, India

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