... permeabilities, and small grid size. This paper combines DCA and machine learning to forecast crude oil production, analyzing US shale data to capture intricate relationships between reservoir parameters and production decline. The dataset has over 1000 wells from a US shale gas play, with monthly production...

... with sufficiently high trace density can provide adequate subsurface resolution, the focus shifted into the pre-stack space. The quantitative interpretations, using pre-stack analysis and inversion, were conducted on both conventional and EcoSeis datasets. The results from each stage of the analyses were utilized...

... on new datasets. Such a digital system can account for ML models drift recalibration and regional localization ensuring the solution remains operational and reliable over time. Reliable data products through DataOps pipelines feeding contextualized information to ML models deployed and operationalized...

.... Using NOVI Labs’ data analytics platform, Shell developed over 250 machine learning models based on close to 600 wells to scale oil production for a wide range of conditions. Datasets were filtered based on operator, target zone, normalized production, porosity, and completion vintage year. Around 120...

... that are often unclear from analysis of a single dataset. The most common datasets employed include completion diagnostics, mud log and cuttings analysis, fracturing and production results, and geosteering interpretations. Case histories will demonstrate multiple ways that drilling data can be used...

... and their associated facies distribution, in order to apply them in geocellular modeling. The key technology is provided by Neural Networks. geologist upstream oil & gas complex reservoir facies dataset heterogenous environment shale oil geology rock type reservoir characterization information la...