The overall objective of this research is to develop a mechanistic model for dissolution of hydrate-coated methane bubbles from natural seeps that fully explains fundamental laboratory and field observations of methane bubbles within the gas hydrate stability zone of the oceans, to validate the model to data from the NETL High-Pressure Water Tunnel (HPWT) and the Gulf Integrated Spill Research Consortium (GISR) seep cruises, and to demonstrate the capability of the model to quantify bubble characteristics and concentration from M3 and EM 302 multibeam echosounder data collected during the GISR cruises.
Phase 1 will focus on laboratory and field data analysis and will achieve project Objective 1 to analyze existing data from the NETL HPWT and Objective 2 to synthesize data from the GISR natural seep cruises.
In Phase 2, the effort will be on project Objective 3 to refine and validate the seep model to predict the laboratory and field data obtained through Phase 1. The team will also collect new data to calibrate the backscatter response of the M3 multibeam echosounder, which will be needed in phase 3 of the project.
Phase 3 will accomplish project Objective 4 to demonstrate the capability of the refined and validated seep model to interpret multibeam data and will perform the work to disseminate the results and data of the project to the public. Together, these objectives will meet the project goal of developing a validated natural seep model and demonstrating its skill in interpreting laboratory HPWT data and field data from natural seeps.
Texas A&M Engineering Experiment Station, College Station, TX 77845
Acoustic and in situ observations of methane and gas bubble flares from natural seeps in the oceans increasingly demonstrate that gas hydrate deposits in the sediments and leakage of bubbles into the water column are ubiquitous occurrences on the continental margins around the world, including the coastlines of the United States and the Arctic. Bubbles that enter the water column transport methane vertically upward, and it is important to develop models to predict their dissolution and fate to understand the input of methane to the ocean-atmosphere system from methane hydrate deposits. Models exist in the literature to predict the bubble dynamics in the water column, but they are limited in their ability to predict the formation time for hydrate skins that can coat bubbles in the deep ocean and to understand the appropriate mass transfer rates for hydrate-coated bubbles. This project seeks to address this gap by synthesizing insight from existing high-pressure laboratory and in situ field data to refine and validate an advanced computer model for methane bubble dynamics and to demonstrate the model performance using field acoustic data from the Gulf of Mexico. This work is important to clarify the processes by which gas hydrate deposits, an important reservoir of the global carbon budget, are maintained and evolve within the natural ocean environment.
The validated model will predict the evolution of hydrate-coated methane bubbles from the sea floor, including their rates of dissolution into the water column, to their point of maximum rise or the sea surface. This provides a holistic understanding of free methane gas in the ocean water column, which is important to predict dissolved methane input to the oceans from natural seeps, a key element of the global carbon cycle, and potential ventilation to the atmosphere, both under baseline conditions as well as in response to future climate scenarios.
Ultimately, the main outcome and benefit of this work will be to clarify the processes by which hydrate-coated methane bubbles rise and dissolve into the ocean water column, which is important to predict the fate of methane in the water column, to understand the global carbon cycle, and to understand how gas hydrate deposits are maintained and evolve within geologic and oceanic systems, both at present baselines and under climate-driven warming.
Researchers have completed the comparison of TAMOC with the HPWT dataset and have quantified the model performance. The model shows very low bias (less than 3%) and low error (68% of the data have errors less than 5%; 95% of the data have errors below 10%). Efforts were focused on predicting the observed mass transfer rates from the HPWT observations and simulating each experiment using TAMOC. The observed mass transfer rates can be predicted analytically. Therefore, these rates are used in the model simulations and model-data comparison.
With the development of the analytical and calibration tools to analyze the M3 and EM 302 sonar data, TAMU has proven its capability to perform the necessary modeling and calibration to allow them to obtain the same dataset (in the absence of hydrate skins) during an experiment the at the OTRC at TAMU. These OTRC experiments were completed in February and March of 2019, and data analysis is currently underway.
During the next quarter, the team will focus their research efforts on the validation of the model at field scale and application of the model to predict bubble concentration using acoustic tools in the field. The project team will also begin reporting the project findings through journal manuscripts that are expected to be submitted over the remaining period of the project.
Phase 1: $144,311
Phase 2: $121,128
Phase 3: $ 96,094
Planned Total Funding – $361,533
Phase 1 – $36,086
Phase 2 – $30,374
Phase 3 – $24,038
Planned Total Funding – $90,498
Quarterly Research Performance Progress Report [PDF] January - March, 2019
Quarterly Research Performance Progress Report [PDF] October - December, 2018
Quarterly Research Performance Progress Report [PDF] July - September, 2018
Quarterly Research Performance Progress Report [PDF] April - June, 2018
Quarterly Research Performance Progress Report [PDF] January - March, 2018
Quarterly Research Performance Progress Report [PDF] October - December, 2017
Quarterly Research Performance Progress Report [PDF] July - September, 2017
Quarterly Research Performance Progress Report [PDF] April - June, 2017
Quarterly Research Performance Progress Report [PDF] January - March, 2017
Quarterly Research Performance Progress Report [PDF] October - December, 2016