Chemostratigraphic Variability of the Eagle Ford Shale, South Texas

Tinnin, Beau; Darmaoen, Shariva T. R.

doi:10.1306/13541965m1103664

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…Actually, quartz in Eagle Ford Shale is known as mixture of detrital and authigenic (mainly biogenic and volcanic) quartz (e.g. Tinnin and Darmaoen, 2016;Milliken et al, 2016;Lee et al, 2018) . Although this irregular behaviour of quartz and plagioclase is troublesome, estimation of terrestrial input from Zr and Ti seems to be effective way to understand mineral composition roughly because both main brittle component (calcite) and ductile component (clay) were strongly controlled by the supply from landscape.…”

Section: Well Datamentioning

confidence: 99%

“…have also been utilized to estimate sediment-water interface oxygenation (paleo-redox) conditions of Eagle Ford sediments and abundances of Ni, Cu, etc. are thought to represent organic C sinking flux (frequently referred to as paleo-productivity) (Tribovillard et al, 2006;Driskill et al, 2012;Moran, 2012;Rowe et al, 2013;Denne et al, 2014;Eldrett et al, 2014;Boling, 2015;Fairbanks et al, 2016;Tinnin et al, 2016) . Therefore, total organic carbon is predictable in use of these elements and Hart et al (2019) calculate TOC with good accuracy to measured TOC (r 2 ＝ 0.747) .…”

Section: Introductionmentioning

confidence: 99%

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Reservoir characterization by X-ray fluorescence（XRF）analysis and machine learning in tight reservoir

et al. 2021

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： Development of tight reser voir is represented by multi-stage hydraulic fracturing and horizontal drilling. Optimized hydraulic fracturing design based on the geomechanical character of horizontal section is required in order to achieve ef cient oil and gas recovery from tight reservoir. Generally, geomechanical analysis using core sample and sonic logging are helpful to capture a geomechanical feature, however, application of these technique is too costly way to obtain an enormous quantity of data from many producing wells. The use of X-ray uorescence (XRF) -related techniques in shale plays has been standard method to evaluate rock character of ne-grained rocks due to the dif culties in visual characterization. For example, in the Eagle Ford play, there are many XRF-related studies, such as estimation of depositional environments, mineral composition, total organic carbon (TOC) , etc. In this study, we constructed the prediction model of mineral composition, TOC and geomechanical features using machine learning methods based on XRF data obtained from conventional core of a vertical pilot well in Eagle Ford. The constructed prediction model was then applied to the XRF data of horizontal wellʼs cuttings samples. Multi-regression, random forest and auto machine learning (Auto ML) were utilized to estimate mineral composition, TOC and geomechanical character in this study. Modified multi-regression model for estimation of mineral composition and TOC achieved similar quality with recent researches, and random forest and Auto ML model (tree algorism) were able to estimate Leeb hardness, Youngʼs modulus and Poissonʼs ratio with high accuracy. As the work ow constructed with Eagle Ford sample worked well in Onnagawa Formation, this prediction techniques using machine learning methods is expected to be applicable for various tight reservoir plays.

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Section: Well Datamentioning

confidence: 99%