Improvement of lithology and saturation determined from well logging using statistical methods

Kaźmierczuk, M.; Jarzyna, J.

doi:10.2478/s11600-006-0030-y

Cited by 19 publications

(5 citation statements)

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“…for the processing of geochemical data acquired from water wells (Vriend et al 1988). Kazmierczuk and Jarzyna (2006) studied its possibilities in lithology and hydrocarbon saturation determination using well logging measurements in Poland. Paasche and Tronicke (2007) used the fuzzy K-means cluster analysis successfully for subsurface zonation and petrophysical modelling.…”

Section: Introductionmentioning

confidence: 99%

Improved well logs clustering algorithm for shale gas identification and formation evaluation

et al. 2021

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The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

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Section: Introductionmentioning

confidence: 99%

Improved well logs clustering algorithm for shale gas identification and formation evaluation

et al. 2021

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“…These techniques include evaluating trace element concentrations and ratios of diagenetically relevant elements (Brand and Veizer, ; Banner, ; Frank, Lohmann, and Meyers, ; Metzger and Fike, ) and have been applied to carbonate δ 13 C and δ 18 O values to eliminate diagenetically altered samples from datasets to improve interpretation of original biogeochemical changes. Other workers have applied statistical techniques such as parametric and non‐parametric statistical models (Oehlert et al , ; Krissansen‐Totton, Buick, and Catling, ), hierarchical cluster analysis (Coimbra, Marques, and Olóriz, ) and principal component analysis (Elek, ; Kazmerczuk and Jarzyna, ; Ma, ; Enikanselu and Ojo, ; Coimbra et al , ; among others) to datasets of elemental and carbon and oxygen isotope stratigraphy in ancient carbonates in order to quantify changes in sediment source, lithology, facies and diagenesis. In many cases, petrographic analysis of carbonate sediments and their cements, as well as core descriptions of sedimentologically relevant surfaces are often incorporated to prevent the interpretation of diagenetically altered samples as pristine records of original changes in the global carbon cycle.…”

Section: Introductionmentioning

confidence: 99%

Rolling window regression of δ¹³C and δ¹⁸O values in carbonate sediments: Implications for source and diagenesis

Oehlert

Swart

2019

The Depositional Record

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Diagenetic alteration can produce, modify or erase significant biogeochemical information recorded in carbon and oxygen (δ13C and δ18O) values of marine carbonates throughout geological time. Understanding the type and extent of alteration experienced by a carbonate deposit can improve sedimentological and geochemical interpretations of events in Earth history. In this study, we present a new application of a statistical approach to aid in the identification and interpretation of sedimentological surfaces in a shallow marine carbonate sequence using a rolling window regression (RWR) analysis. RWR analysis evaluates the degree of covariation between two records and how it changes through time. Geological application of this statistical technique permits a new perspective on the fine scale variability in carbon and oxygen isotope records and the processes that generate them and provides a complementary tool for sedimentological interpretations. In this study, we apply RWR to δ13C and δ18O values from the Clino drilled into the western margin of Great Bahama Bank, a core that has been extensively altered by diagenetic processes, within the vadose, phreatic and marine burial zones. This core penetrates ~676 m of shallow marine to deeper slope deposits and contains a variety of both sedimentological and diagenetic events, including facies transitions, subaerial exposure surfaces, marine hardgrounds, firmgrounds and periods of reduced sedimentation rate. Using more than 1,200 analyses of paired δ13C and δ18O values, we have applied a RWR analysis to evaluate how the correlation between these two proxies changes at scales of 10, 30 and 100 m. The results of this study highlight the dynamic evolution of correlations between δ13C and δ18O values within diagenetic zones and in association with diagenetic surfaces and provide context for interpreting covariance in δ13C and δ18O values from the geological record.

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“…Multivariate statistics have been shown in sedimentology and hydrocarbon exploration to objectively classify the data into lithological units or lithofacies (e.g., Rice et al, 1976;Delfiner et al, 1987;Bosch et al, 2002;Reátegui et al, 2005;Kaźmierczuk and Jarzyna, 2006;Tang and White, 2008;Rowe et al, 2012). Since the early 1960s, several multivariate statistical techniques have been developed for automatic segmentation of log data (e.g., Testerman, 1962;Gill, 1970;Hawkins, 1972;Hawkins and Ten Krooden, 1979).…”

Section: Introductionmentioning

confidence: 99%

Lithological discrimination based on statistical analysis of multi-sensor drill core logging data in the Matagami VMS district, Quebec, Canada

Fresia

Ross

Gloaguen

et al. 2017

Ore Geology Reviews

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In mineral exploration, traditional core logging is largely based on the visual inspection of drilled cores, a process which is often subjective and non-reproducible. However, a number of physical, chemical and mineralogical properties of rocks can now be measured at high spatial resolution on drill cores. The resulting large multiparameter databases can help geologists to quantitatively discriminate between lithologies, study hydrothermal alteration, and potentially vector towards mineralization. Multivariate statistical methods are important tools to assist geologists in interpreting such large databases. We present an application of model-based cluster analysis in the Matagami base metal mining district, more specifically to improve lithological discrimination at the zinc-rich Bracemac and McLeod volcanogenic massive sulfide (VMS) deposits. The model-based cluster analysis method is able to efficiently discriminate different geological units encountered, even in cases where two units are visually similar or in the presence of strong hydrothermal alteration.

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Improvement of lithology and saturation determined from well logging using statistical methods

Cited by 19 publications

References 1 publication

Improved well logs clustering algorithm for shale gas identification and formation evaluation

Improved well logs clustering algorithm for shale gas identification and formation evaluation

Rolling window regression of δ¹³C and δ¹⁸O values in carbonate sediments: Implications for source and diagenesis

Lithological discrimination based on statistical analysis of multi-sensor drill core logging data in the Matagami VMS district, Quebec, Canada

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Improvement of lithology and saturation determined from well logging using statistical methods

Cited by 19 publications

References 1 publication

Improved well logs clustering algorithm for shale gas identification and formation evaluation

Improved well logs clustering algorithm for shale gas identification and formation evaluation

Rolling window regression of δ13C and δ18O values in carbonate sediments: Implications for source and diagenesis

Lithological discrimination based on statistical analysis of multi-sensor drill core logging data in the Matagami VMS district, Quebec, Canada

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Rolling window regression of δ¹³C and δ¹⁸O values in carbonate sediments: Implications for source and diagenesis