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What besides redox conditions? Impact of sea-level fluctuations on redox-sensitive trace-element enrichment patterns in marine sediments

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Science China Earth Sciences 2022年第10期65卷 1985-2004页

作者： Junwen PENG Bureau of Economic Geology Jackson School of GeosciencesThe University of Texas at AustinAustinTexas 78713USA

Concentrations of redox-sensitive trace-element(RSTE) in marine shales have long been interpreted simply as redox proxies. However, the impact of other non-redox factors(e.g., sea-level fluctuation and seawater chemistry) on the enrichment of RSTE, especially molybdenum(Mo) and uranium(U), in sediments has been rarely reported. This study presents newly obtained RSTE datasets from the Upper Pennsylvanian organic-rich Cline Shale in the silled midland basin, U.S., to illustrate the influence of sea-level fluctuation on the authigenic accumulation of RSTE in marine sediments. A previously established transgressive-regressive sequence of the Cline Shale, a well-constrained high-amplitude glacio-eustatic fluctuation curve, and an accompanying episodic resupply of aqueous RSTE from the Panthalassic Ocean provide an ideal stratigraphic framework for determining the spatial and temporal variations of sediment RSTE enrichment patterns that responded to the episodic variations of seawater chemistry in this marginal silled paleomarine basin. Results suggest that although slightly higher median RSTE concentrations were observed in sediments from more reducing environments, the overall variation ranges of RSTE concentrations largely overlap among sediments deposited from a wide redox spectrum(from oxic to euxinic conditions) or different sea-level statuses in the Cline Shale. In contrast to the sediment RSTE enrichment patterns, the variations of sediment Mo/TOC and U/TOC ratios are coupled with glacio-eustatic fluctuation. The highest Mo/TOC and U/TOC ratios are commonly observed in sediments deposited during the highest relative sea-level(RSTE resupply), whereas the lowest Mo/TOC and U/TOC ratios usually appear in sediments deposited during the lowest relative sea-level(RSTE depletion). Our findings suggest that the benthic redox conditions recorded in sediment Mo and U concentrations can be greatly obscured and weakened by depleted aqueous Mo and U concentrations in highly restricted basins. Thus, the use of sediment Mo a

关键词： Molybdenum Uranium Glacio-eustatic fluctuation Pennsylvanian Cline Shale midland basin

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Machine learning classification approach for formation delineation at the basin-scale

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Petroleum Research 2022年第2期7卷 165-176页

作者： Derek Vikara Vikas Khanna Department of Civil and Environmental Engineering University of PittsburghPittsburghPA15261United States KeyLogic Systems LLCMorgantownWV26505United States Department of Chemical and Petroleum Engineering University of PittsburghPittsburghPA15261United States

Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications.They are seen as novel methods that may enhance existing capabilities,providing for improved efficiency in exploration and production operations.Furthermore,their inte-gration into reservoir management workflows may shape the future landscape of the energy industry.This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units(i.e.,formations)as a function of total vertical depth and spatial positioning.The framework is applied in a case study to 13 specific formations of interest(Upper Spraberry through Atoka/Morrow reservoirs)in the midland basin,West Texas,United States;a prominent hydrocarbon producing sub-basin of the larger Permian basin.The study dataset consists of over 275,000 records and includes data fields like formation iden-tifier,true vertical depth(in feet)of formations observed,and latitude and longitude coordinates(in decimal degrees).A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training,validation,and testing.Four super-vised learning approaches including random forest(RF),gradient boosting(GB),support vector machine(SVM),and multilayer perceptron neural network(MLP)were evaluated and their prediction accuracy compared.The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data.The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres,Upper Spraberry,Lower Spraberry,Clearfork,and Wolfcamp at 98,94,89,94,and 94 percent respectively.Overall,the resulting data-driven model provides a robust,cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy

关键词： Permian basin midland basin K-means clustering Random forest Classification machine learning

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