Application of artificial neural networks for lithofacies determination based on limited well data

Brcković, Ana; Kovačević, Monika; Cvetković, Marko; Močilac, Iva Kolenković; Rukavina, David; Saftić, Bruno

doi:10.1556/24.60.2017.012

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…In developing behaviour model, particularly in ANN model, the first step was to divide consumer green behaviour into three groups as the target or output of this model. This output will be used to train the ANN model (Brcković et al, ).…”

Section: Methodsmentioning

confidence: 99%

How does perception on green environment across generations affect consumer behaviour? A neural network process

Deliana

Rum

2019

Int J Consumer Studies

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This paper examines how the perception on green environment can affect consumer behaviour. It tests whether consumer behaviour responds to environmental change. This study captures the interaction between green environment perception and behaviour. A neural network analysis is used to address this challenge to model such a behaviour. A total of 462 interviews were conducted and analysed using neural network process. A Scaled Conjugate Gradient with random data division was used in training and the performance was calculated using mean square error. The result reveals that perception–behaviour gap exists across generations on green environment. It indicates that perception on green producing, green living, green transport and green product is an important factor in determining the behaviour on green consumption. The neural network accurately recognized green behaviour up to nearly 82% with MSE = 0.09 at epoch 31, which is a high level of recognition accuracy. This study underlines the existence of perception–behaviour gap on green environment. However, these are only identified to people with low‐to‐medium level of green behaviours (level of correct only 44.5% and 30%). People with high level of green behaviour in fact show consistency between their perceptions and behaviours (level of correct 96.4%). This result shows that people tend to have higher perceptions on green environment, but their behaviour reflects otherwise.

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Section: Methodsmentioning

confidence: 99%

How does perception on green environment across generations affect consumer behaviour? A neural network process

Deliana

Rum

2019

Int J Consumer Studies

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“…Besides single-attribute analyses, the application of multiple seismic attributes in a combined plot is a key element of our study. They are generally usable in various clustering techniques, like self-organized maps (Roden et al, 2015;Zhao et al, 2015), geostatistics (Janson & Madriz, 2012;Ba et al, 2019), and neural networks (Brcković et al, 2017;Gogoi & Chatterjee, 2019;Abdel-Fattah et al, 2020) that have gained a lot of attention in recent years, because they enable parameter-based classifications, e.g., to obtain a 3D lithology or facies reservoir model. The quality of the results strongly depends on the input data such as seismic attributes and, in case of availability, the desired output data (e.g.…”

Section: Methodical Approachmentioning

confidence: 99%

“…Various studies have therefore attempted to show the benefits of a combined approach using both seismic and well data, in order to reduce the uncertainties of reservoir characterization (Toublanc et al, 2005;Fang et al, 2017;Albesher et al, 2020;Boersma et al, 2020;Méndez et al, 2020). Another aspect to consider is that manual interpretation of seismic data can be a very time-consuming task due to the high amount of data, which is why computational solutions, such as supervised and unsupervised neural networks have been increasingly used for seismic interpretation, pattern recognition, and lithology classification in recent years (Saggaf et al, 2003;Baaske et al, 2007;Bagheri & Riahi, 2015;Roden et al, 2015;Brcković et al, 2017;Zahmatkesh et al, 2021). Besides the long-time use for hydrocarbon reservoir investigation, seismic attribute analysis has also been increasingly used in geothermal exploration in recent years, especially for complex structured reservoirs (Pendrel, 2001;Chopra & Marfurt, 2007;Doyen, 2007;Abdel-Fattah et al, 2020), e.g., in Poland (Pussak et al, 2014) and Denmark (Bredesen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Advanced seismic characterization of a geothermal carbonate reservoir – Insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Wadas

Bauer

Shipilin

et al. 2023

Preprint

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Abstract. The quality of geothermal carbonate reservoirs is controlled by numerous factors and processes, such as the depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution are therefore difficult to predict. For the example of a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate, how an advanced analysis can deliver an improved reservoir model concept and help to identify possible exploitation targets within the Upper Jurassic carbonates. To identify possible reservoir sections and to understand their above-mentioned controlling factors, we analyse different seismic single- and multi-attributes. Some of the seismic attributes, together with lithology logs from wells, are then used to identify parameter correlations between the seismic attributes and the different carbonate lithologies to obtain a supervised neural network based 3D lithology model of the geothermal reservoir. Furthermore, we compare the fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis), to address scalability. Our results show that, for example, acoustic impedance is well suited to identify reefs and karst-related dolines. Areas with strong karstification or fault- and fracture-related deformation, which are both associated with high permeabilities, are also indicated by e.g. strong frequency attenuation, variance anomalies, and/or morphological features like bowl-shaped structures derived from the shape index. Furthermore, by using sweetness we can reconstruct the reef development of two exemplary reefs, and regarding the lithology distribution, we show that the upper part of the reservoir is dominated by limestone and dolomitic limestone rather than dolomite. In addition, we observe spatial trends in the degree of dolomitization. With respect to the fracture orientations on seismic- and well scales, we point out that a general scalability is not possible due to a combination of methodological limitations and geological reasons. Nonetheless, we argue that the combination of both methods provides an improved overall impression of the fracture systems, and therefore possible fluid pathways. By taking all the results into account, we are able to improve and adapt recent reservoir concepts to outline the different phases of its structural and diagenetic evolution. Furthermore, our results help to identify high quality reservoir zones in the Munich area.

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“…Various studies have therefore attempted to show the benefits of a combined approach using both seismic and well data in order to reduce the uncertainties of reservoir characterization (Toublanc et al, 2005;Fang et al, 2017;Albesher et al, 2020;Boersma et al, 2020;Méndez et al, 2020). Another aspect to consider is that manual interpretation of seismic data can be a very time-consuming task due to the high amount of data, which is why computational solutions, such as supervised and unsupervised neural networks, have been increasingly used for seismic interpretation, pattern recognition, and lithology classification in recent years (Saggaf et al, 2003;Baaske et al, 2007;Bagheri and Riahi, 2015;Roden et al, 2015;Brcković et al, 2017;Zahmatkesh et al, 2021). Besides the long-time use for hydrocarbon reservoir investigation, seismic attribute analysis has also been increasingly used in geothermal exploration in recent years, especially for complex structured reservoirs (Pendrel, 2001;Chopra and Marfurt, 2007;Doyen, 2007;Abdel-Fattah et al, 2020), e.g.…”

Section: Introductionmentioning

confidence: 99%

Advanced seismic characterization of a geothermal carbonate reservoir – insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Wadas,

Krumbholz,

Shipilin

et al. 2023

Solid Earth

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Abstract. The quality of geothermal carbonate reservoirs is controlled by, for instance, depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution difficult to predict. Using a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate how a comprehensive seismic attribute analysis can significantly improve the understanding of a complex carbonate reservoir. We deliver an improved reservoir model concept and identify possible exploitation targets within the Upper Jurassic carbonates. We use seismic attributes and different carbonate lithologies from well logs to identify parameter correlations. From this, we obtain a supervised neural-network-based 3D lithology model of the geothermal reservoir. Furthermore, we compare fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis) to address scalability. Our results show that, for example, acoustic impedance is suitable to identify reefs and karst-related dolines, and sweetness proves useful to analyse the internal reef architecture, whereas frequency- and phase-related attributes allow the detection of karst. In addition, reef edges, dolines, and fractures, associated with high permeabilities, are characterized by strong phase changes. Fractures are also identified using variance and ant tracking. Morphological characteristics, like dolines, are captured using the shape index. Regarding the diagenetic evolution of the reservoir and the corresponding lithology distribution, we show that the Upper Jurassic carbonate reservoir experienced a complex evolution, consisting of at least three dolomitization phases, two karstification phases, and a phase of tectonic deformation. We observe spatial trends in the degree of dolomitization and show that it is mainly facies-controlled and that karstification is facies- and fault-controlled. Karstification improves porosity and permeability, whereas dolomitization can either increase or decrease porosity. Therefore, reservoir zones should be exploited that experienced only weak diagenetic alteration, i.e. the dolomitic limestone in the upper part of the Upper Jurassic carbonates. Regarding the fracture scalability across seismic and well scales, we note that a general scalability is, due to a combination of methodological limitations and geological reasons, not possible. Nevertheless, both methods provide an improved understanding of the fracture system and possible fluid pathways. By integrating all the results, we are able to improve and adapt recent reservoir concepts, to outline the different phases of the reservoir's structural and diagenetic evolution, and to identify high-quality reservoir zones in the Munich area. These are located southeast at the Ottobrunn Fault and north of the Munich Fault close to the Nymphenburg Fault.

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Application of artificial neural networks for lithofacies determination based on limited well data

Cited by 12 publications

References 20 publications

How does perception on green environment across generations affect consumer behaviour? A neural network process

How does perception on green environment across generations affect consumer behaviour? A neural network process

Advanced seismic characterization of a geothermal carbonate reservoir – Insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Advanced seismic characterization of a geothermal carbonate reservoir – insight into the structure and diagenesis of a reservoir in the German Molasse Basin

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