The main objective of hydraulic well stimulation is creating fractures in a tight rock that enhance its natural permeability and make hydrocarbon production economic. Because the geometry of fractures and the permeability of treated formation influence the subsequent production, their assessment is important for the development of tight-gas fields. The fracture shapes and orientations are inferred conventionally from microseismic data acquired in the process of well stimulation, but the same data also can be used to estimate the formation permeability. We have compared two techniques for permeability estimation that utilize different aspects of information contained in the observed microseismicity. We applied those techniques to data recorded in the course of the hydraulic fracturing of four wells drilled in the Pinedale Field, Wyoming, U.S.A. Then we used the obtained permeabilities to predict the gas rates from 20 treatment stages at which the number of identified microseismic events was sufficient to perform our analysis. The predictions of both techniques correlate with production and allow us to establish the characteristics of rocks and hydraulic fractures that make good producers at Pinedale.

Reservoir discrimination and characterization for proper lithology and fluid-content distribution is a must for proper reservoir management. Seismic inversion methods using variation of amplitude with offset (AVO) are regularly used in differentiating lithology along with its fluid content. Acoustic impedance (AI), shear impedance (SI), and density (ρ) are the fundamental rock properties often derived using AVO equations. The AI and SI estimates are more stable as compared to the density estimate that remains unstable because of its dependence on far-angle amplitude information. Various attributes, e.g., lambda-rho, mu-rho, elastic impedance, Poisson's ratio etc., derived from these inversion results are routinely used to discriminate lithology along with its fluid content. A new approach using Poisson dampening factor (PDF) attribute, derived using AI and SI values and making density estimate intrinsic within it, is applied and found to improve reservoir delineation and description.

No abstract