Petroelastic and geomechanical classification of lithologic facies in the Marcellus Shale

Abstract

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.

Bayesian facies classification in a CO2 sequestration study using statistical rock physics modeling of elastic and electrical properties

“…The Expectation-Maximization (EM) algorithm (Hastie et al, 2009) was used for comparison ( Figure 4b) with consistent results. The EM algorithm is a statistical clustering method often used in facies classification (Grana et al, 2015): the method is based on an iterative algorithm under the assumption that the input properties are Gaussian within each component (facies, in our application). The cutoff method is not suitable when the number of input variables increases.…”

Method of Geomechanical Parameter Determination and Volumetric Fracturing Factor Simulation under Highly Stochastic Geologic Conditions

“…Sequence stratigraphy and geomechanics also have some correlations, as many geological properties affect geomechanical properties and, ultimately, reservoir operations and performance [19]. Petroelastic and geomechanical classification of lithologic facies also have correlations to some extent, representing a new research frontier [20]; through the analysis of rock facies and rock properties, the correlations between petroelastic and geomechanical properties can be determined.…”

Facies analysis by integrating 3D seismic attributes and well logs for prospect identification and evaluation — A case study from Northwest China

“…Commonly used petrophysical logs for facies classification include gamma ray (GR), spontaneous potential (SP), neutron logs, and other formation evaluation parameters such as volumetric fractions (porosity and mineralogical percent) (Ransom, 1995). Facies classification on well logs allows us to determine the number of facies, lithology, depositional setting, and the correlation between facies along the well profile (Grana et al, 2015). Cores and outcrops description further add detailed information of geologic facies to the facies obtained from well profiles.…”