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Predictive Modeling of Hydrodynamic Dispersion for High-Pressure Atomized Crude Oil Plumes During Tidal Phase-Lag Loops in Santa Barbara Estuaries, Nigeria

Predictive Modeling of Hydrodynamic Dispersion for High-Pressure Atomized Crude Oil Plumes During Tidal Phase-Lag Loops in Santa Barbara Estuaries, Nigeria

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

Okuroghoboye Diepreye Itugha

Abstract

This study models the multi-phase hydrodynamic atomization, near-surface jet shattering, and transient longitudinal advection-diffusion transport of high-pressure crude oil plumes within a tidally driven tropical estuary. It maps the 38-day uncontained wellhead failure at the Santa Barbara South Well-1 (OML 29) in Opu-Nembe, Bayelsa State, Nigeria, to quantify sub-surface contaminant migration paths. Hydrodynamic and chemical concentration data were compiled across a 10 km × 1 km estuarine corridor enveloping 47 remote communities. Ambient water samples (N = 120) were extracted from the water column using decontaminated Niskin sampler grids, stabilized, and processed via Gas Chromatography-Mass Spectrometry (GC-MS) to quantify Total Petroleum Hydrocarbons (TPH). Estuarine water surface elevation and multi-directional flow profiles were captured via a bottom-mounted Acoustic Doppler Current Profiler (ADCP). A two-dimensional depth-averaged finite-difference hydrodynamic model (x = y = 50 m) was coupled to an isentropic choked-jet discharge algorithm to calculate transient plume dispersion across a 12.4-hour Semi-diurnal tidal cycle. Thermodynamic derivations show that the reservoir pressure (4.2 MPa) drove a choked multi-phase discharge velocity (vjet  145.24 m/s), producing aerodynamic Weber numbers far exceeding critical breakup thresholds (We  100). This jet velocity shattered the liquid stream into ultra-fine droplets, driving immediate emulsification and chemical dissolution into the sub-surface water column. The cross-validated model isolated a strong phase-lag dynamic controlling transport. Flood-tide vectors drove the dissolved plume upstream, while ebb-tide drawdown compressed the mixing column, funneling persistent hydrocarbon matrices directly into low-velocity intertidal mangrove channels. This mechanism matched a peak measured near-source TPH concentration of 52.40 ± 2.62 mg/L at station AQ-01, representing a five-fold exceedance of national regulatory acute aquatic hazard boundaries. Model cross-validation yielded a Mean Absolute Error (MAE = 0.024 ± 0.003 mg/L), a low Fractional Bias (+0.012), a FAC2 index of 0.945, and a Nash-Sutcliffe Efficiency (NSE = 0.912). Conventional spill models that assume simple surface-slick transport underestimate the spatial extent and velocity of sub-surface estuarine plume migration. Upstream maritime contingency frameworks must shift from surface booming loops to implement real-time hydro-telemetry tracking grids. Environmental impact criteria should mandate continuous downhole automated isolation systems to prevent sub-surface resource fouling within vulnerable coastal delta networks.

DOI

https://doi.org/10.31223/X5VF79

Subjects

Engineering

Keywords

Hydrodynamic modeling, Advection-diffusion equations, Isentropic choked flow, Estuarine transport, Total petroleum hydrocarbons, Finite-difference methods

Dates

Published: 2026-09-09 14:48

Last Updated: 2026-09-09 14:48

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
The author declares no competing financial or personal interests that could influence the impartiality of the modeling data presented in this research.

Data Availability:
All raw micro-meteorological variables, Acoustic Doppler Current Profiler (ADCP) hydrodynamic velocity tracking vectors, and gas chromatography-mass spectrometry (GC-MS) spectrometry core water digests are fully contained within the appendices of this document (Embedded as In-Line tables (Tables 1 to 4)). The baseline mathematical models can be reproduced entirely utilizing the raw data matrices and the explicit partial differential objective functions detailed in Section 2.2. This implies that all raw data and validation matrices are fully embedded as in-line tables and standalone appendices within this preprint file.

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