Experimental and computational analysis of oil field operations

Abstract

This dissertation presents results of an experimental study that was done using a novel apparatus from the Halliburton Advanced Perforation Laboratory and a statistical analysis derived from those experimental studies. We examine both the subsurface component of unconventional reservoirs to improve fluid flow, and also the surface component of oilfield operations. Regarding the latter, we propose to optimize operations by reducing non-productive time and minimizing infrastructure and operational costs associated with drilling and fracturing. Historically, Darcy’s equation had been used to predict hydrocarbon production (Darcy 1856); however, it is inaccurate in unconventional reservoirs. This study was conducted in order to validate the Barree - Conway model (2004), which yields accurate flow predictions for all flow regimes in porous media, and examines output data such as pressures, temperature, and mass flow rate given a proppant pack design and specific fluid flow rate. Data were collected from various experimental studies conducted at high-flow rates through various proppant packs and using different experimental procedures. This study identifies the most important parameters associated with non-Darcy flow behavior. Statistical analysis of non-Darcy flow reveals that the most important predictors of mass flow rate are the particular material used, fluid velocity, fluid viscosity, and apparent permeability. We compare the results of the statistical analysis to previous work done in unconventional reservoir studies. These results show that statistical analysis can recommend which parameters to prioritize during the hydraulic fracturing design process. Although this work was conducted under experimentally controlled conditions, it is hypothesized that the results will hold for unconventional reservoirs. Following this experimental and predictive work, we introduce an optimization, or a prescriptive, model that improves efficiency and reduces costs for surface operations. The model considers drilling, fracturing, and production of 71 pads and a total of 729 wells, and identifies a schedule of operations to minimize idle time while adhering to precedence and resource constraints. Using a specialized algorithm, we solve a large-scale model to inform not only a schedule that minimizes makespan but also one that can identify bottleneck resources.