Interpreting shale lithofacies is an important step in identifying productive zones in the Marcellus Shale gas play; the target reservoir can be less than 3 m (10 ft) thick with overlying shales that appear similar on certain petrophysical well logs and in the core. However, these nonreservoir-quality shales contain widely varying organic and mechanical properties. To distinguish between reservoir- and nonreservoir-quality shale facies, this classification method applies a pattern-recognition algorithm, expectation maximization, to a set of commonly available petrophysical and elastic well logs to create 1D shale facies models along the wellbore. The statistical algorithm classifies the petrophysical and elastic well-log data into defined facies using the theory that each well log contains a distribution of Gaussian curves for each facies. The method is applied to 12 wells across Pennsylvania and northern West Virginia, and it is able to discriminate between shale facies based on organic content and brittleness, characteristics which are not always evident in core. To verify the geologic accuracy of the facies models, the results are compared with the core data, well logs, mudlogs, and regional stratigraphic studies when available. In addition, we were able to designate a lithofacies-defined top to the Marcellus Formation in parts of the Appalachian Basin where the gradational contact with the overlying Mahantango Formation is poorly defined on petrophysical logs.

Log-facies classification at the well location allows determination of the number of facies, the facies definition, and the correlation between facies and rock properties along the well profile. In unconventional reservoirs, because of the necessity for hydraulic fracturing in shale gas and shale oil reservoirs, facies classification should account for petroelastic and geomechanical properties. We developed a facies classification methodology based on the expectation-maximization algorithm, a statistical method that allows finding the most likely facies classification and the associated probability distribution, given the set of geophysical measurements in the borehole. We applied the proposed workflow to a complete set of well logs from the Marcellus shale and developed the corresponding facies classification from log properties measured and computed in three different domains: petrophysics, rock physics, and geomechanics. In thne preliminary well-log and rock-physics analysis, we identify three main lithofacies: limestone, shale, and sandstone. The application of the classification method provided the vertical sequence of the three lithofacies and their pointwise probability of occurrence. A sensitivity analysis was finally evaluated to investigate the impact of the number of input variables on the classification and the effects of cementation and kerogen.

A new methodology for a log-based facies classification using a statistical algorithm, Expectation Maximization, is proposed to classify lithologic-facies utilizing commonly available wireline logs. This method was tested in the Marcellus Shale, an unconventional reservoir located in the Appalachian Basin of eastern North America. The method relies on Gaussian mixture models with the assumption that each facies has a unique Gaussian distribution of petroelastic properties. The technique was checked against mud logs, well log interpretations, and regional stratigraphy for accuracy and produced promising results.