| dc.description.abstract | This thesis introduces a rock classification technique for organic-rich shale that takes into account well-log-based estimates of compositional, petrophysical, and elastic properties.
Well logs and laboratory core measurements were used to calculate depth-by-depth petrophysical and compositional properties of three wells in two organic-rich formations. Then, either acoustic well logs or effective medium theories helped estimate formation elastic properties. Estimates of total porosity, Total Organic Content (TOC), fluid saturation, volumetric concentrations of mineral constituents, and elastic properties facilitated identification of different rock classes, using an unsupervised artificial neural network. A good rock classification technique improves (a) petrophysical evaluation of organic-rich shale reservoirs, (b) fluid flow characterization, (c) detection of productive zones for fracturing jobs, and (d) prediction of hydraulic fracturing and stimulation effectiveness.
Then, a rock classification method was then applied to the field examples from the Haynesville shale and Woodford shales for rock classification. The estimates of porosity, TOC, bulk modulus, shear modulus, and volumetric concentrations of minerals were obtained and then validated by comparing them to laboratory measurements. These calculated properties and well logs served as inputs to an artificial neural network to identify the different rock classes in both formations. Finally, the rock classes enabled identification of good candidate zones for fracture stimulation. | en |
