Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and expectation–maximization method and application in petrophysical inversion

Grana, Dario; Lang, Xiaozheng; Wu, Wenting

doi:10.1111/1365-2478.12428

Cited by 28 publications

(5 citation statements)

References 35 publications

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“…These models were classified into distinct water masses. Bayesian classification (e.g., Avseth et al, 2005;Grana et al, 2017) was trained based on the existing direct measurements of temperature and salinity profiles of spatially located nearby Argo floats taken during the acquisition of the seismic oceanography data. N w different types of water were identified including the Central Atlantic Water, the Mediterranean Outflow and the Subarctic Intermediate Waters, as described in Comas-Rodríguez et al (2011).…”

Section: Number Of Channels 636mentioning

confidence: 99%

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

et al. 2021

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A two-dimensional multichannel seismic reflection profile acquired in the Madeira Abyssal Plain during June 2016 was used in a modeling workflow comprising seismic oceanography processing, geostatistical inversion and Bayesian classification to predict the probability of occurrence of distinct water masses. The seismic section was processed to image in detail the fine scale structure of the water column using seismic oceanography. The processing sequence was developed to preserve, as much as possible, the relative seismic amplitudes of the data and enhance the shallow structure of the water column by effectively suppressing the direct arrival. The migrated seismic oceanography section shows an eddy at the expected Mediterranean Outflow Water depths, steeply dipping reflectors, which indicate the possible presence of frontal activity or secondary dipping eddy structures, and strong horizontal reflections between intermediate water masses suggestive of double diffuse processes. We then developed and applied an iterative geostatistical seismic oceanography inversion methodology to predict the spatial distribution of temperature and salinity. Due to the lack of contemporaneous direct measurements of temperature and salinity we used a global ocean model as spatial constraint during the inversion and nearby contemporaneous ARGO data to infer the expected statistical properties of both model parameters. After the inversion, Bayesian classification was applied to all temperature and salinity models inverted during the last iteration to predict the spatial distribution of three distinct water masses. A preliminary interpretation of these probabilistic models agrees with the expected ocean dynamics of the region.

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Section: Number Of Channels 636mentioning

confidence: 99%

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

et al. 2021

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“…lithology, petrophysics, geomechanics, i.e) based on measurements from wells and seismic data (Bonnell & Hurich 2008, Heidsiek et al 2020, Jones et al 2008, Yu et al 2008, Zhang et al 2006. The distribution of properties in an industry-standard 3D model draws on data from wellbores (lithology succession, petrophysics, fluid types) with a spatial resolution of centimeters to meters, and seismic reflection data (2D and 3D volumes) with a spatial resolution of tens to hundreds of meters (Burchette 2012, Grana et al 2016, Ozkan et al 2011, Raeesi et al 2012, Worden et al 2018. Heterogeneities formed by depositional and diagenetic processes present a challenge for reservoir modeling, especially for carbonate rocks (Burchette 2012, Worden et al 2018.…”

Section: Introductionmentioning

confidence: 99%

“…Static reservoir modeling depends on the power to predict intrinsic properties in regions far from the wellbore (Grana et al 2016, Zhao et al 2014, and well observations are not always sufficient to inform the predictions desired (Burchette 2012, Laubach et al 2019), such as in thin reservoir units with great horizontal extensions. Furthermore, the manual labeling of properties of interest, either lithofacies (groups of rocks that share similar lithologic or physical characteristics), or petrofacies (groups of rocks that share similar petrographic or mineralogical characteristics) regarding variations found from site to site over the borehole data can also be ambiguous, expensive, and time-consuming (Edwards et al 2017, Lineman et al 1987.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based classification of petrofacies in fine laminated limestones

GENESIS,

GOMES,

BARBOSA

et al. 2024

An. Acad. Bras. Ciênc.

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Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a test case of petrofacies classification using machine learning models in a thin interval of finely laminated limestones using pseudo-well data created over outcrops (radiometric and unconfined compressive strength logs). We tested Gaussian naïve Bayes (GNB) and support vector machine (SVM) techniques to classify eight petrofacies types, divided into two groups. The objective was to observe the capacity of some well-known models to classify petrofacies with a high-frequency vertical variation of diagenetic heterogeneities in an extreme scenario within a thin sedimentary interval. The GNB was less effective (F 1 score of 0.29), and the SVM achieved the best results in classifying the main facies patterns (F 1 = 0.47). However, the GNB performed better when the analysis was focused on distinguishing the two main groups of petrofacies. The results demonstrate that high-frequency facies variations present a challenge to the automatic identification of lithofacies, mainly due to local variations in horizontal heterogeneities (on the mm-to cm-scale) created by depositional and diagenetic processes, which impact the flow in porous media.

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“…Some studies use the aid of seismic attributes (e.g. P wave impedance, S wave impedance and density) from inversion to generate the facies model in the reservoir through classification (Roncarolo & Grana, 2010;Grana, 2016;Tellez et al, 2017). Since elastic inversions provide only information about the seismic bandwidth, some studies avoid this problem by combining the inversion with the low frequency model, in addition to petrophysical information, for the construction of the facies model (Sams & Saussus, 2013;Zabihi Naeini & Exley, 2017).…”

Section: Introductionmentioning

confidence: 99%

3D lithofluid facies classification by machine learning algorithms

Aguiar¹,

Oliveira²,

Matos³

et al. 2021

Proceeding of the 17th International Congress of the Brazilian Geophysical

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There must be an adequate integration of well and seismic data to create a reliable facies model. However, heterogeneous rocks, lack of data and inaccurate information accumulate uncertainties for model characterization. Here we seek to simulate a scenario where there are few wells and only one seismic data to create the facies model. The main goal is to estimate the predominant lithology distribution with fluid information. This strategy can help in new wells' locations and reducing the risk of false discoveries. The Norne field located on the Norwegian Sea will be used as an example for this methodology. Machine learning techniques have been commonly used for automation and task optimization processes. We show that these methods can achieve high performance when receiving proper processed data. The summarized workflow can be described in two stages: electrofacies classification on eight different wells and use of these facies as labels for classification on the field's seismic volume. The results indicate accuracy close to 90% both in well and seismic scenarios. The facies model with lithofluid information is still compared with the reservoir simulator from the same period to show the high similarity.

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Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and expectation–maximization method and application in petrophysical inversion

Cited by 28 publications

References 35 publications

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

Geostatistical Seismic Inversion for Temperature and Salinity in the Madeira Abyssal Plain

Machine learning-based classification of petrofacies in fine laminated limestones

3D lithofluid facies classification by machine learning algorithms

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