Simulating Gas–Liquid−Water Partitioning and Fluid Properties of Petroleum under Pressure: Implications for Deep-Sea Blowouts

Abstract

With the expansion of offshore petroleum extraction, validated models are needed to simulate the behaviors of petroleum compounds released in deep (>100 m) waters. We present a thermodynamic model of the densities, viscosities, and gas-liquid-water partitioning of petroleum mixtures with varying pressure, temperature, and composition based on the Peng-Robinson equation-of-state and the modified Henry's law (Krychevsky-Kasarnovsky equation). The model is applied to Macondo reservoir fluid released during the Deepwater Horizon disaster, represented with 279-280 pseudocomponents, including 131-132 individual compounds. We define >n-C8 pseudocomponents based on comprehensive two-dimensional gas chromatography (GC × GC) measurements, which enable the modeling of aqueous partitioning for n-C8 to n-C26 fractions not quantified individually. Thermodynamic model predictions are tested against available laboratory data on petroleum liquid densities, gas/liquid volume fractions, and liquid viscosities. We find that the emitted petroleum mixture was ∼29-44% gas and ∼56-71% liquid, after cooling to local conditions near the broken Macondo riser stub (∼153 atm and 4.3 °C). High pressure conditions dramatically favor the aqueous dissolution of C1-C4 hydrocarbons and also influence the buoyancies of bubbles and droplets. Additionally, the simulated densities of emitted petroleum fluids affect previous estimates of the volumetric flow rate of dead oil from the emission source.

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

“…Simulations are built by utilizing the various methods in each of the main Python modules of TAMOC; the ./ bin/ directory on the Github repository provides example scripts for typical types of model simulation. The model was first introduced in [33,34] and has been applied to simulate the thermodynamic behavior of Deepwater Horizon oil [29,35] and to hindcast a short period of the Deepwater Horizon oil spill [30]. This section presents the technical details of the modeling suite needed to appreciate the validation and application sections that follow.…”

“…The accuracy of the methods in the DPM and their implementation within TAMOC have been validated by comparison to measured thermodynamic properties of different gases and petroleum fluids. A rigorous description of the methods in the DPM and a validation to properties of Deepwater Horizon oil is given in Gros et al [29]. Physical properties of the dispersed phase particles include the equivalent spherical diameter, surface area, particle shape, and slip velocity.…”

“…For simple compounds (e.g., oxygen, methane, benzene), these properties are readily known [45]. For more complex components of petroleum, these properties can be estimated using group contribution methods, as detailed for our model in Gros et al [29].…”

“…We have tested the model implementation in TAMOC to ensure that the SPM reproduces the results in Socolofsky et al [8], and this is not repeated here. The equations of state in the DPM are validated comparing to data for the Deepwater Horizon oil in Gros et al [29], and the LPM has been validated to analytical solutions for particle advection. The BPM remains to be validated, and we present the results of the validation in this section with a focus on the entrainment models, particle tracking, calibration of the buoyancy parameter j, and the numerical solution using an adaptive time step, stiff equation solver.…”

“…Models differ in their complexity, with oil-well blowout models generally requiring non-ideal equations-of-state and solubility models [28]. Here, we utilize a discrete particle model developed for the Deepwater Horizon reservoir fluids [29,30] and focus our discussion on the dynamics of multiphase plumes in the environment.…”