Leaching of lanthanide and yttrium from a Central Appalachian coal and the ashes obtained at 550-950℃

In this work,we investigated leaching of lanthanide and yttrium(REY) from a Central Appalachian coal and its ashes obtained at 550-950℃ with the main purpose of understanding the impact of ashing temperature on REY leachability in water,ammonium sulfate,and hydrochloric acid.It is found that the coal contains a negligible amount of water-soluble REY,less than 1% ion-exchangeable REY,and about 28% of HCl-soluble REY.Ashing leads to dramatic changes in REY leachability in both ammonium sulfate and hydrochloric acid solutions,which is believed to be related to transformation and redistribution of organically-associated REY in coal during the ashing process.Ashing temperature significantly affects REY leaching from coal ashes;higher ashing temperature results in lower REY leachability in both solutions.Clay minerals may play a significant role in changing the leachability of REY after ashing.In addition,the results also suggest that the organic matter in the coal is relatively enriched in heavy REY.

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

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 applications.