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• W3211550223 abstract "It is helpful to consider the variations in a shale formation's brittleness and total organic carbon (TOC) when considering which zones to target for drilling and fracture stimulation. Zones with relatively high brittleness combined with relatively high TOC are likely to yield more gas at higher flow rates. Brittleness is a complex characteristic which is not readily calculated formulaically as a rock property from petrophysical log variables. Likewise, although empirical relationships between well logs, specifically bulk density, resistivity and gamma ray and TOC are established, they tend not to perform consistently in all shale formations. Measuring brittleness and TOC in the laboratory is costly and time consuming, making machine learning (ML) methods to predict these variables from basic well log information appealing. A mineralogical brittleness index and TOC values measured from rock samples in control wells can be used to calibrate supervised ML. This enables these metrics to then be predicted from well log data in wells where no measured mineralogical data is available. Transparent optimized data-matching tools can be usefully applied for this purpose to provide substantial insight to variations existing across shale formations. A case study demonstrating this is presented for two wells drilled in the Lower Barnett Shale (Texas, USA). The transparent open box (TOB) algorithm generates highly accurate supervised-learning predictions of brittleness index and TOC using data from five well log curves and stratigraphic height. Its prediction accuracy improves with a higher sampling density of the well-log curves. Banner headline Combining information from a mineralogical brittleness index and total organic carbon provides useful guidance on the most favorable zones of an organic-rich shale to drill and fracture stimulate. Both variables require costly laboratory measurements and are not easily and consistently estimated from well-log data using available empirical formulas. Transparent optimized data matching machine learning offers an effective and accurate tool for predicting these metrics from well-log curves particularly when they are sampled at high densities." @default.
• W3211550223 created "2021-11-22" @default.
• W3211550223 creator A5028777364 @default.
• W3211550223 date "2022-01-01" @default.
• W3211550223 modified "2023-10-02" @default.
• W3211550223 title "Assessing the brittleness and total organic carbon of shale formations and their role in identifying optimum zones to fracture stimulate" @default.
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• W3211550223 doi "https://doi.org/10.1016/b978-0-323-85465-8.00014-5" @default.
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