Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

Silva, Adrielle A.; Neto, Irineu A. Lima; Misságia, Roseane M.; Ceia, Marco; Carrasquilla, Abel; Archilha, Nathaly L.

doi:10.1016/j.jappgeo.2015.03.027

Cited by 47 publications

(20 citation statements)

References 10 publications

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“…The results showed that CMIS performed better than NN models acting alone. The newest results in the same area can be found in the paper (Silva et al, 2015). This paper demonstrates petrographic classification of carbonate-siliciclastic rocks using a neural network.…”

Section: Introductionmentioning

confidence: 67%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article -which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for.Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice.The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication

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Section: Introductionmentioning

confidence: 67%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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“…The database employed here, shown in Table 10, is a subset of samples of the database found in [25]. This subset was selected for purposes of comparison with [26]. The dataset was generated through core plugs extracted from land well La Ciotat-1 drilled down to 150 m from the surface.…”

Section: Experimental Datasetmentioning

confidence: 99%

“…In addition, a recently proposed activation function called Swish [22] is implemented and its performance is compared with other well established activation functions in the literature. We have performed computational experiments and we have consistently achieved better results than those achieved by [26]. The remainder Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification of this paper is organized as below.…”

Section: Introductionmentioning

confidence: 96%

“…Swish activation function has recently proposed by [22]. This paper explores an Extreme Learning Machine (ELM) [23] associated with a Differential Evolution (DE) [24] to classify data from the South Provence Basin, taken from [25], using the paper by [26] as a baseline reference. The output of the classifier is created from input data composed by the combination of mineralogy and textural information and divided into seven classes.…”

Section: Introductionmentioning

confidence: 99%

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Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Saporetti

Duarte

Fonseca

et al. 2018

RITA

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Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an activation function recently proposed in the literature is implemented and tested. The computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.

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“…The multilayer perceptrons (MLPs) is a biologically inspired computational tool for solving pattern recognition problems and is efficient in recognizing previously trained patterns. The capability of neural networks with multiple inputs and multiple outputs realizes data parallel processing and self-learning [13,18]. The parameters, as well as neurons, perform math functions intended to interweave them to a net, divided into carcinogens and non-carcinogens.…”

Section: Introductionmentioning

confidence: 99%

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

Song

Zhang

Liang

et al. 2016

IJERPH

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A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.

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Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

Cited by 47 publications

References 10 publications

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

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