Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data

Roslin, Alexandra; Esterle, Joan

doi:10.1016/j.cageo.2016.03.006

Cited by 25 publications

(12 citation statements)

References 5 publications

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“…The last step is to assign lithologies to clusters to provide them meaningful electrofacies. This procedure is usually performed by either applying the lithologies of centroid to the whole cluster universally (Doveton, 2014) or assigning lithologies based on their corresponding logs and log interpretations (Roslin and Esterle, 2016). However, those two methods rely on 1) a relatively large number of clusters; and 2) distinct log values among clusters and thorough understanding of these log values.…”

Section: Step 3: Classificationmentioning

confidence: 99%

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Zhang

2021

Adv. Geo-Energy Res.

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While the well log-based lithology classification has been extensively utilized in reservoir characterization, the classification of carbonate sub-facies remains challenging due to the subtle nuances in conventional well-logs. The nuclear magnetic resonance (NMR) log provides extra information of pore size and pore geometry features, improving differentiating carbonate sub-facies. Here we explore the feasibility of using the ratio between NMR longitudinal relaxation time and transverse relaxation time as a potential lithology indicator to determine carbonate sub-facies. We analyzed a series of logging data and corresponding core samples of Arbuckle Group carbonate containing mudstone, packstone, grainstone, incipient breccia, and breccia in northern Kansas for the characteristics of longitudinal relaxation times, transverse relaxation times, and longitudinal over transverse relaxation time ratios. The results show that mudstone, packstone, and grainstone exhibit high, intermediate, and low longitudinal over transverse relaxation time ratios, respectively, while incipient breccia and breccia have a wide range of longitudinal over transverse relaxation time ratios. Furthermore, we evaluated the potential of using longitudinal over transverse relaxation time ratios to classify carbonate sub-facies using multivariate analysis. By adding longitudinal over transverse relaxation time ratios to neutron porosity, total gamma-ray, and conductivity logs as inputs of automated facies classification, the prediction error decreased, especially for incipient breccia. On the contrary, when photoelectric log and computed gamma-ray are also available, adding longitudinal over transverse relaxation time ratios does not improve the accuracy of sub-facies classification. Our results suggest that longitudinal over transverse relaxation time ratio is an independent lithology indicator. However, it cannot replace other logs like gamma-ray and photoelectric logs in classifying carbonate sub-facies. Our study provided valuable evidence and credible elucidation of the importance and physicochemical mechanism of longitudinal over transverse relaxation time ratios, which is essential for deciphering NMR logging data in carbonate reservoirs.

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Section: Step 3: Classificationmentioning

confidence: 99%

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Zhang

2021

Adv. Geo-Energy Res.

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“…Metoda analisis log GR untuk mengetahui fasies pengendapan ini efektif digunakan untuk mengevaluasi lingkungan pengendapan serpihan yang kaya organik pada lingkungan transisi [6], serta terkait endapan sungai untuk mengetahui kontrol siklus pada fasies sedimentasi [7] [8]. Fasies sendiri diartikan sebagai aspek fisika, kimia atau biologi suatu endapan dalam kesamaan waktu [9].…”

Section: Pendahuluanunclassified

Interpretasi Lingkungan Pengendapan Formasi Batuan Menggunakan Analisis Elektrofasies di Lokasi Tapak Puspiptek Serpong

Syaeful¹,

Muhammad²

2017

EKSPLORIUM

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ABSTRAKKegiatan karakterisasi material bawah permukaan penyusun pondasi tapak merupakan bagian dari studi tapak instalasi nuklir. Karakterisasi dilakukan dengan berbagai metode, diantaranya pemahaman tentang sistem pengendapan formasi batuan. Sebagai bagian dari metode interpretasi lingkungan pengendapan, analisis pemodelan fasies berdasarkan elektrofasies memberikan informasi yang cepat mengenai sistem pengendapan suatu formasi batuan. Metodologi yang digunakan adalah dengan interpretrasi log sinar gamma (log GR) menggunakan korelasi relatif antara variasi bentuk log dan fasies sedimentasi. Berdasarkan analisis diketahui Formasi Bojongmanik terbentuk pada lingkungan marine-lagoonal dengan pengaruh gelombang sangat rendah. Log GR yang menunjukan bentuk funnel, bergerigi dan simetris, mengindikasikan fasies shoreface, lagoon, dan tidal point bar. Arah sedimentasi, cekungan, dan suplai pada pengendapan sedimen Formasi Bojongmanik diinterpretasikan relatif ke utara. Formasi Serpong diendapkan pada sistem sungai bermeander dan tersusun atas endapan point bar, crevasse splay dan floodplain. Hasil analisis ini diharapkan dapat menjadi panduan dalam analisis lanjutan terkait karakterisasi material pondasi.Kata kunci: studi tapak, lingkungan pengendapan, fasies, elektrofasies ABSTRACT The activity of subsurface material composing site foundation characterization is part of nuclear installation siting study. Characterization conducted by several methods, such as understanding the depositional environment of rock formations. As a segment of depositional environment interpretation method, facies model analysis based on electrofacies provides quicker information on depositional system of rock formation. Methodology applied is gamma ray log (log GR) interpretation using relative correlation between log

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“…Empirical methods and traditional modelling techniques such as multivariate regression fail to derive the cause of backbreak in cases where it is affected by numerous parameters nonlinearly. Artificial intelligence has been diversely applied in earth sciences recently, using fuzzy logic (Demicco & Klir, 2004;Muhammad & Glass, 2011), neural networks (Bonaventura et al, 2017;Chatterjee et al, 2010;Izadi et al, 2017;Rogiers et al, 2012;Roslin & Esterle, 2016;Muhammad et al, 2014), and neuro-fuzzy modelling techniques (Cherkassky et al, 2006;Kar et al, 2014;Valdés & Bonham-Carter, 2006;Yegireddi & Uday Bhaskar, 2009;Yurdakul et al, 2014;Zoveidavianpoor et al, 2013). Recently, several researchers have solved backbreak problems through applying neural networks (Jang & Topal, 2013;Monjezi & Dehghani, 2008;Monjezi et al, 2013;Saadat et al, 2014;Sayadi et al, 2013;Ebrahimi et al, 2016), neuro-fuzzy techniques (Ghasemi et al, 2016), stochastic optimisation (Sari et al, 2013), and machine learning techniques (Khandelwal & Monjezi, 2012;Mohammadnejad et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

Muhammad

Shah²

2017

Archives of Mining Sciences

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Backbreak, defined as excessive breakage behind the last row of blastholes in blasting operations at a quarry, causes destabilisation of rock slopes, improper fragmentation, minimises drilling efficiency. In this paper an artificial neural network (ANN) is applied to predict backbreak, using 12 input parameters representing various controllable factors, such as the characteristics of explosives and geometrical blast design, at the Dewan Cement limestone quarry in Hattar, Pakistan. This ANN was trained with several model architectures. The 12-2-1 ANN model was selected as the simplest model yielding the best result, with a reported correlation coefficient of 0.98 and 0.97 in the training and validation phases, respectively. Sensitivity analysis of the model suggested that backbreak can be reduced most effectively by reducing powder factor, blasthole inclination, and burden. Field tests were subsequently carried out in which these sensitive parameters were varied accordingly; as a result, backbreak was controlled and reduced from 8 m to less than a metre. The resulting reduction in powder factor (kg of explosives used per m 3 of blasted material) also reduced blasting costs.Keywords: Neural Network, backbreak, sensitivity analysis, modeling, blast design, quarry Kruszenie części złoża poza obszarem prowadzonych prac strzałowych oznacza nadmierne pęka-nie skał poza ostatnim rzędem otworów strzałowych w trakcie prac w kamieniołomach i prowadzi do destabilizacji górotworu poprzez zmianę nachylenia warstw skalnych, powoduje niepotrzebną fragmentację skał i obniża efektywność prac wiertniczych. W pracy tej wykorzystano sztuczną sieć neuronową (ANN) do przewidywania zasięgu kruszenia dalszej części złoża przy wykorzystaniu 12 parametrów wejściowych. Parametry te opisują różne zmienne czynniki, np. charakterystyka materiału wybuchowego czy przyjęty plan prac strzałowych w kamieniołomie Deewan w regionie Hattar w Pakistanie. Prowadzono proces uczenia sieci dla różnej architektury modelu, wybrano model 12-2-1 ANN, jako model najprostszy, zapewniający najlepszy wynik a współczynniki korelacji uzyskane dla fazy uczenia i walidacji wyniosły odpowiednio 0.98 i 0.97. Przeprowadzona analiza wrażliwości modelu wykazała że zasięg kruszenia dalszych części złoża obniżyć można poprzez zmianę parametrów ładunku strzelniczego, zmianę nachylenia otworów strzałowych oraz zmianę przybitki. Badania terenowe w czasie których ulegały zmianie wartości wyżej wymienionych wrażliwych parametrów wykazały, że zasięg kruszenia złoża poza obszarem prac strzałowych ograniczono z uprzednich 8 m do wielkości poniżej jednego metra. Obniżenie współczynnika charakteryzującego ładunek (kg zastosowanego materiału wybuchowego przypadający na 1 m 3 rozkruszonego materiału skalnego) pozwoliło także na obniżenie kosztów prac strzałowych.Słowa kluczowe: sieci neuronowe, zasięg kruszenia, analiza wrażliwości, modelowanie, projektowanie robót strzałowych, kamieniołom

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Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data

Cited by 25 publications

References 5 publications

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Interpretasi Lingkungan Pengendapan Formasi Batuan Menggunakan Analisis Elektrofasies di Lokasi Tapak Puspiptek Serpong

Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

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