Behind the Log: A Characterization Workflow Designed for Improved Learning Curves
Consistency and repeatability drive predictability. While each shale is unique, a standard interpretation approach regarding data quality and data analysis will be shown to provide production correlated results. First, a petrophysics approach which incorporates XRD and XRF data combined with MICP to estimate mineralogy and clay types. The clay volume comes from triple combo data. TOC and fluid properties come from known equations for logs. Both of these data types are combined into a solution for clay volume, TOC, porosity, water, and hydrocarbon. Then organic components are added to yield the total hydrocarbon. It will be shown that petrophysics alone do not dictate production; maturity is required as well. To be discussed is an example from the Midland Basin where logs indicate significant hydrocarbon, but the wells do not flow according to the in-place values. RTA and 3D simulation suggest the wells should be economic, but they do not produce as expected. Thermal maturity is the missing piece in the puzzle Another example will be provided which highlights the dangers of simple single phase volumetrics. GORs have been shown to increase with time in a number of plays and the corresponding production profiles are impacted. Lastly, mature hydrocarbon in-place seems to be the primary driver behind plays in the US today. However, in-place volumes alone do not correlate to economics. A density of hydrocarbons or hydrocarbon concentration is actually a large driver as the wells access vertical volumes which do not connect in the way a conventional reservoir might. They are only connected via hydraulic fractures so in-place volumes are never drained without a flow path to the well.
AAPG Datapages/Search and Discovery Article #90333©2018 AAPG Middle East Region, Shale Gas Evolution Symposium, Manama, Bahrain, December 11-13, 2018