Upscaling of wireline log-derived reservoir properties with minimum data loss
High fidelity upscaling of critical reservoir-seal-source rock properties (TOC, thickness, porosity, composition, geomechanical behavior) influences forward decision making with the assistance of reservoir and basin models. At the field scale, these properties control (a) hydrocarbon migration, (b) degree of production compartmentalization, (c) connectivity, and (d) fracture aperture and length. This presentation introduces a new big data tool, specifically designed to upscale very large (100,000+) well datasets for reservoir and basin modeling purposes. This new tool works in four steps: (I) identification of property boundaries and break down of any wireline log into a facies column, (II) automated calculation of facies thicknesses and subsequent quantification of important descriptors and factors, including thickness histograms, ratios and noise, (III) a user guided quality control (UQC) that refines and enhances the results of the automatic recognition of beds and noise, and (IV) an export that can be easily implemented into Petrel, GoCAD, ArcGIS, Petra, Geoscout and other reservoir modeling software packages. Once all configurations have been set rock attribute trends are being computed within minutes, beyond computed property statistics, new factors describing the reservoir heterogeneity are being introduced. The user can, for example, estimate how likely two sand beds are vertically connected or how heterogeneous reservoir or seal units are developed across an oil and gas field. It therefore lends itself to support predictions on sediment transport fairways as well as determination of the weakest links across a hydrocarbon trap - all in a fully quantitative format. The entire workflow has the ability to reproduce with more than 80% fidelity against calibrated core and reduces the time spent for static reservoir model building by ~ 35-45%.
AAPG Datapages/Search and Discovery Article #90350 © 2019 AAPG Annual Convention and Exhibition, San Antonio, Texas, May 19-22, 2019