Multivariate Supervised Classification, Application to a New Zealand Offshore Field

Hami-Eddine, Kamal; Klein, Pascal; Richard, Loic; Elabed, Dorra; Chatila, Eric; Furniss, Andrew

doi:10.5724/gcs.11.31.0358

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ASNN explicitly corrects the bias of the neural network ensemble and leads to an improved prediction ability (Gao et al, 2010). This has been demonstrated (Hami-Eddine et al, 2011) in the case of lithological facies prediction based on prestack amplitudes.…”

Section: Associative Neural Networkmentioning

confidence: 93%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

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-

show abstract

Section: Associative Neural Networkmentioning

confidence: 93%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

Hussein

Stewart

Sacrey³

et al. 2021

Interpretation

View full text Add to dashboard Cite

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

show abstract

Simultaneous lithofacies inversion from prestack amplitudes

Richard

Klein

Hami-Eddine

et al. 2013

SEG Technical Program Expanded Abstracts 2013

View full text Add to dashboard Cite

Lithofacies prediction from well and seismic data is the challenge for the oil and gas industry to accurately describe the subsurface geology. This paper describes how lithofacies represented by probability vectors can be inverted using simultaneously all the amplitudes corresponding to various distances between source and receiver (offset) reflecting on the same interface. The inverse problem is solved using a maximum likelihood approach similar to the one described by Tarantola (1987). Prior information deduced from well is introduced to add high frequency and better conditioning.

show abstract

Multivariate Supervised Classification, Application to a New Zealand Offshore Field

Cited by 3 publications

References 0 publications

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

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

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

Simultaneous lithofacies inversion from prestack amplitudes

Contact Info

Product

Resources

About