... Abstract In this paper, a new workflow for data wrangling, cleansing, processing, automated identification and classification of drilling dysfunctions (e.g. bit balling, drillstring vibrations, etc.) for any number of wells is presented. Statistically-derived baseline rock strengths were used...

... influential parameters that impact deliverability of wells. Results from single point statistical analysis were further extended to unsupervised discrete classification using neural-network to bin the reservoir. Binning classification was then used to design and optimize hydraulic fracture parameters...

... modeling can be obtained with unsupervised multivariate classification procedures applied to suites of log curves. Data analytics applied to log data can provide lithologies and flow units across the reservoir. Important rock properties that are critical inputs for a static earth model are included as part...

... geologic modeling log analysis classification hra rock class seismic data facies variability calcite rock class Pellet composition speck facies geological modeling well logging geologic facies shell fragment reservoir quality hra model geologic model texture organic material sabine...

... classifications, which contrasts and compares them with reporting requirements from the petroleum and mining industries. Present day techniques of computing geothermal resources provide only ballpark estimates at best. The classification and quantification of different geothermal occurrences will differ...