Abstract
The effective optimization of fracture stimulation treatments in horizontal wells requires the integration of a wide range of engineering and geological data to be successful. This process begins with a thorough understanding of not only the reservoir rock properties, but also the properties of the confining non-reservoir rock. This geological information is used in the design of non-damaging completions fluids, to predict the fracture height growth and fracture half length, and to interpret the results from diagnostics, post job stimulation data and production performance.
This paper documents the work conducted to design and implement multi-stage horizontal fracture stimulations in the Cleveland and Tonkawa sandstones located in Dewey County, Oklahoma. Methods used in this process included: 1) review of historical treatment and production data available in the area to help identify current best practices, 2) SEM, thin section and X-ray diffraction petrography to define the mineralogy, 3) core floods to determine formation damage mechanisms, 4) laboratory proppant pack floods to screen for the effectiveness of chemical additives (such as surfactants), 5) core floods using reservoir rock to determine regain permeability and flow back performance, 6) determination of mechanical rock properties, such as Young's modulus, Poisson's ratio, Brinell hardness and triaxial stress, from multiwave sonic and laboratory testing for use in fine-tuning the fracture stimulation model parameters, 7) design, acquisition and analysis of initial injection and fall-off tests, 8) fracture stimulation modeling to predict the fracture geometry created by the fracture design, and 9) analysis of hourly flow back and tracer data to determine the effectiveness of the treatments in accessing the maximum amount of reservoir rock.
The objectives of this work were to engineer an optimized treatment design (that would result in significant gains in initial well productivity and long term ultimate hydrocarbon recovery) and also to develop and refine new potential best practices.
