Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Abstract

Quantitative lithofacies modeling is important to understand the depositional and diagenetic history, and hydrocarbon potential of unconventional resources at a regional scale. The complex heterogeneous nature and large data dimensionality of unconventional mudstone reservoirs increase the challenge of lithofacies interpretation by conventional qualitative methods. Quantitative shale lithofacies, which are meaningful, mappable, and predictable at core, well log, and regional scales, can be defined based on mineralogy and Total Organic Carbon (TOC) derived from core analysis and advanced geochemical spectroscopy logs (e.g. Pulsed Neutron Spectroscopy, PNS). However, access to numerous and widespread core samples and geochemical log responses is typically limited by cost and time. We apply different mathematical techniques to ubiquitous conventional well log suites calibrated to rock types, defined by the limited number of wells with high-quality core and geochemical logs. The documented interrelationships between lithofacies and conventional logs are propagated with a quantified degree of accuracy in wells without advanced log or core data. Our study addresses issues of different approaches of quantitative lithofacies classification and

Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: A case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran

“…In the following step, we performed our comparative study by applying SVM classifier as a class-membership predictive model. In this paper, the RBF kernel has been adopted in SVM implementation which is performing quite well in comparison to other kernels (Bhattacharya et al, 2016;Camps-Valls and Bruzzone, 2009;Hsu et al, 2003). The other important parameters for the SVM classifier include the coefficient of RBF kernel γ and σ 2 .…”

A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies

“…Conventionally, qualitative analysis is performed to recognize subsurface mudstone facies using core analysis, geomechanical spectroscopy logs, Rock-Eval pyrolysis, etc. (Bhattacharya et al 2016). The conventional methodology is found to be inconvenient, tiresome, expensive in nature and requires high domain expertise.…”

“…Recognition of subsurface lithofacies is much researched topic and still a thought-provoking problem due to the uncertainty associated with reservoir measurements (Chaki et al 2015;Bhattacharya et al 2016). Quantitative modeling of lithofacies is essential to assess the potential of unconventional hydrocarbon reservoirs lying in mudstone formations.…”

“…Several logging techniques such as wireline, logging-while-drilling, measurement-while-logging, etc., are utilized to generate wide varieties of well logs for measuring petrophysical properties of reservoir rock (Anifowose et al 2015(Anifowose et al , 2017. Various researchers have reported that logging data are found to be nonlinear, high dimensional, complex and noisy in nature due to the heterogeneity and spatial distribution of reservoir properties (Kocberber and Collins 1990;Chaki et al 2015;Bhattacharya et al 2016;Tewari and Dwivedi 2018a). Therefore, manual identification of geological lithofacies from sensory well logs is an impractical and tedious job even for expert field engineers.…”

Classificação de sismofáceis carbonáticas a partir da técnica Self-Organizing Maps (SOM).

“…As redes neurais artificiais (RNA's) são ferramentas capazes de capturar estas relações entre os atributos inclusive relações não lineares(HAGAN et al, 2002). As comparações entre a aplicação das RNA's supervisionadas e não supervisionadas nas geociências mostram que a escolha é dependente da análise em cada caso, não existe uma regra a ser aplicada a todos os casos (BHATTACHARYA; CARR;PAL, 2016;SAYAGO et al, 2012). No caso específico deste trabalho, optamos por utilizar a técnica SOM.…”