Reservoir characterization from downhole mineralogy

Lofts, Jeremy; Harvey, P. K.; Lovell, M. A.

doi:10.1016/0264-8172(95)98377-h

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Thus, the GLT continuously records down a borehole and provides an in‐situ method for estimating mineralogical composition (e.g., Hertzog & Plasek, 1979; Hertzog, 1980; Herron, 1986; Hertzog et al., 1989; Pratson et al., 1992). Various approaches have been attempted to improve the accuracy of converting elemental measurements into quantitative estimates of mineralogy (e.g., Freedman et al., 2015; Harvey et al., 1990, 1992; Herron & Herron, 1996; Lofts et al., 1995; Zhao et al., 2017). To date, some modern GLTs newly developed provide accurate in‐situ mineralogical characterization for conventional and unconventional reservoirs (e.g., Pemper et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

Hu,

Liu,

Chen

et al. 2023

Earth and Space Science

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Accurate estimation of mineral composition is essential for refined reservoir characterization, thermal conductivity and mechanical determinations of sedimentary rocks, but is extremely challenging in shale units due to the mineralogical complexity, low porosity and ultra‐low permeability. Direct mineral measurements derived from laboratory X‐ray diffraction (XRD) analysis on core samples and borehole geochemical logging tool (GLT), and conventional geophysical logs from vertical wells penetrating sediments are widely available in some basins, which enables detailed mineralogical characterization of a well. A hybrid machine learning (ML) architecture that improves model training and validation by combining convolutional neural network (CNN) with XGBoost allows accurate description of the mineralogical compositions across a basin. We applied this ML approach to predict the mineral compositions using conventional well logs from the Horn River Basin, northeast British Columbia, Canada, where extensive drilling for shale‐gas and conventional hydrocarbon resources, complemented by high temperature geothermal energy potential is ideal for case testing. The predicted mineral compositions from the ML approach are consistent with the mineralogical readings from the GLT and are confirmed by the XRD mineral measurements. This allows basin‐wide mineral compositions mapping that reveals spatial trends of major mineral compositions and their relationship with the previously recognized geomechanical and geological features, which have important implications for thermal conductivity modeling, reservoir evaluation and extensive geological studies.

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Section: Introductionmentioning

confidence: 99%

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

Hu,

Liu,

Chen

et al. 2023

Earth and Space Science

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“…The reconstruction of a lithological profile based on such measurements allows to continuously analyse the variability of mineral composition in a borehole as well as to use this information while interpreting the remaining geophysical logs [3,4]. Logging of this type was introduced in the 1980s [5][6][7][8] and because of its usefulness it gained widespread popularity despite its high costs.…”

Section: Introductionmentioning

confidence: 99%

Downhole Lithological Profile Reconstruction Based on Chemical Composition of Core Samples and Drill Cuttings Measured with Portable X-ray Fluorescence Spectrometer

et al. 2020

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The reconstruction of a lithological profile based on geophysical logs of chemical composition provided by geochemical gamma-gamma well logging probes has been increasingly used for geophysical interpretation. A chemical profile, analogous to the measurements mentioned above, can be determined based on measurements made with a portable X-ray fluorescence spectrometer (pXRF). This paper presents a methodology for determining the mineral composition of drilled, clastic, as well as clay-rich rocks on the basis of both inexpensive and timesaving pXRF measurements as well as models combining the results of chemical composition analysis with results of mineral composition analysis (XRD). The results of chemical composition analysis obtained with a portable XRF spectrometer were calibrated based on a detailed analysis produced with ICP-OES and ICP-MS methods. A significant advantage of the proposed method is the possibility to apply it with regard to drill cuttings as well as archival cores. However, considerable discrepancies in the results obtained were identified while comparing the results of chemical composition analysed directly on the core and milled material. The analysed material comprised Carboniferous rocks derived from three boreholes located in Poland: Kobylin-1 as well as Biesiekierz-1 and -2. It was possible to directly compare the lithological profile obtained based on measurements taken on drill cuttings with the results of the lithological interpretation of a geochemical probe log.

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Non-classical applications of chemical analysis based on nuclear activation

Grdeń

2019

J Radioanal Nucl Chem

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Reservoir characterization from downhole mineralogy

Cited by 6 publications

References 9 publications

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

Downhole Lithological Profile Reconstruction Based on Chemical Composition of Core Samples and Drill Cuttings Measured with Portable X-ray Fluorescence Spectrometer

Non-classical applications of chemical analysis based on nuclear activation

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