Dissociation of methane hydrates in shallow marine sediments due to increasing global temperatures can lead to the venting of methane gas or seafloor destabilization. Along the U.S. Atlantic margin there is a well-documented history of slope failure and numerous gas seeps have been recorded. However, it is not fully understood whether the observed gas seepages can lead to slope failure as seafloor data is often sparse. We used machine learning algorithms to predict total organic carbon (TOC) and porosity at the seafloor on the U.S. Atlantic margin. Within this region, an area of high TOC predictions (1.5—2.2% dry weight) occurred along the continental slope from (35.4°N, 75.0°W) to (39.0°N, 72.0°W), aligning with documented gas seeps in the region. Elsewhere, predicted values of TOC were near or below 1% dry weight. In the area of high TOC, we modeled hydrate and gas formation over a 120,000 years glacial cycle. Along the feather edge, average hydrate saturations at the base of the hydrate stability zone (BHZ) were between 0.2% and 0.7% with some models predicting hydrate saturation above 3% and average peak gas saturations ranged from 4% to 6.5%. At these locations we modeled the pore pressure response of sediments at the BHZ to hydrate dissociation due to an increase in temperature. We focused on purely drained and undrained loading environments and used a non-linear Hoek-Brown failure envelope to assess whether failure criteria were met. In a drained loading environment, where excess pore pressure is instantly dissipated, we found that the change in effective stress due to hydrate dissociation is small and no failure is expected to occur. In an undrained loading environment, where excess pore pressure does not dissipate, the change in effective stress due to hydrate dissociation is larger and shear failure is expected to occur even at low hydrate saturations (0.2%—1%) forming final gas saturations below 0.1%. Therefore, we conclude that the dissociation of hydrates along the feather edge can lead to the conditions necessary for sediment failure.

In shallow marine sediments, gas accumulations and hydrates are significant geohazards to subsea infrastructure, drilling, and production. Therefore, predicting their occurrence is crucial to ensure offshore drilling safety and submarine infrastructure security. In this study, we generate predictions with uncertainty estimates with the goal of providing ageohazard assessment before any shallow hazard surveys are performed.We used a geospatial machine learning model to predict total organic carbon (TOC) and porosity at the seafloor in the northern Gulf of Mexico, and model sedimentation and consolidation in one dimension (1D) with microbial methanogenesis. Our model assumed that seafloor organic carbon is the source material for shallow hydrocarbon occurrences. The machine learning model outputs and uncertainties were sampled statistically to generate a suite of seafloor property realizations that were fed into our 1D model. Our predictions illustrate that gas and hydrate are more likely to be present along the shelf where seafloor TOC values are high, and are less likely to be present in deepwater areas (>500 m water depth) where seafloor TOC values are low. The results show that shallow water depth, lower sedimentation rate, and higher seafloor TOC are correlated with higher predicted gas saturations and shallower gas accumulation.Deepwater areas with significant reported oil production, such as AlaminosCanyon Block 857 (Great White), Green Canyon Block 640 (Tahiti/Caesar/Tonga), and Garden Banks Block 215 (Baldpate/Conger), are less likely to have shallow gas hazards. Any seafloor seeps identified in areas of high drilling activity likely originate from deep reservoirs, not from shallow gas accumulation.This work provides granular predictions of shallow geohazards on a basin scale and offers a holistic approach to identifying shallow hazards using big data and machine learning techniques. Leveraging geospatial machine learning models improves the predictions of subsurface hydrocarbons, despite sparse sampling of seafloor properties, and can be made pre-drill and without additional observations like sediment samples. This method can complement and augment existing hazard survey techniques.

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