Oilfield Data Mining Workflows for Robust Reservoir Characterization

Abstract

Abstract "There are more things in heaven and earth, Horatio, than are thought of in your philosophy" "Hamlet" William Shakespeare To fully appreciate the plethora of disparate data sets across the upstream geoscientific silos, it is essential to establish a sound and consistent suite of workflows that embrace data management facets as well as diverse data mining modules established under the banner of soft computing or artificial intelligence. When does one implement a neural network, a decision tree or non-linear regression techniques? Will Genetic Algorithms or Fuzzy Logic be appropriate for my objective function? This paper sets out to answer some important questions around data mining workflows underpinned by exploratory data analysis, confirmatory data analysis, descriptive and predictive modeling to establish sound and important reservoir characterization decision-cycles. Case studies are presented to illustrate effective and successful studies based on advanced statistical analysis and AI workflows in sandstone and carbonate reservoirs. Can such workflows be adopted in unconventional reservoirs? Determining accurate and effective hydraulic fracturing packages are keys in tight gas plays, and this paper explicates how data mining workflows can be successfully applied in such unconventional plays.