Methodology of organic-rich shale lithofacies identification and prediction: A case study from Marcellus Shale in the Appalachian basin

• Published in: Computers & geosciences 2012-12, Vol.49, p.151-163
• Main Authors: Wang, Guochang, Carr, Timothy R.
• Format: Article
• Language: English
• Subjects: basins; carbon; case studies; computers; data analysis; discriminant analysis; fuzzy logic; hydrology; Lithofacies; Marcellus Shale; Mineral composition; mineral content; mineralogy; natural gas; neural networks; organic matter; Organic matter richness; prediction; shale; spectroscopy; stratigraphy; support vector machines
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2012.07.011

The success of shale gas in North America has attracted increased interest in “unconventional” reservoirs. Two critical factors for shale-gas reservoirs are units amenable to hydrologic fracture stimulation and sufficient natural gas content. The effectiveness of hydrologic fracture stimulation is influenced by rock geomechanical properties, which are related to rock mineralogy. The natural gas content in shale reservoirs has a strong relationship with organic matter, which is measured by total organic carbon (TOC). A 3D shale lithofacies model constructed using mineral composition, rock geomechanical properties and TOC content can be applied to optimize the design of horizontal well trajectories and stimulation strategies. Core analysis data, log data and seismic data were used to build a 3D shale lithofacies from core to well and finally to regional scale. Core, advanced and common logs were utilized as inputs to petrophysical analysis, and various pattern recognition methods, such as discriminant analysis, fuzzy logic, neural network and support vector machine. A limited set of eight derived parameters from common logs were determined as critical inputs for pattern recognition methods. Advanced logs, such as pulsed neutron spectroscopy, are used to determine mineral composition and TOC data improve and confirm the quantitative relationship between conventional logs and lithofacies. Seismic data, interpreted sequence stratigraphy and depositional environments were used as constraints to build deterministic and stochastic 3D lithofacies models and to extrapolate lithofacies from well scale to regional scale. ► An integrated method is present to predict shale lithofacies in three scales. ► Core-scale lithofacies is defined by mineral composition and TOC content. ► Well-scale lithofacies can be predicted based on common logs and core data. ► The 3D lithofacies model is helpful for horizontal drilling and stimulation. ► Marcellus Shale is used as an example.