Methane hydrates are solid structures containing methane inside of a water lattice that form under low temperature and relatively high pressure. Appropriate hydrate‐forming conditions exist along continental shelves or are associated with permafrost. Hydrates have garnered scientific interest via their potential as a source of natural gas and their role in the global carbon cycle. While methane hydrates have been collected in multiple diverse geographic settings, their quantities and distribution in sediments remain poorly constrained due to sparse relevant data. Using statistical and machine learning approaches, we have developed a workflow to probabilistically predict methane hydrate occurrence from local microbial methane sourcing. This approach utilizes machine‐learned global maps produced by the Global Predictive Seabed Model (GPSM) as inputs for the statistical sampling software, Dakota, and multiphase reservoir simulation software, PFLOTRAN. Dakota performs Latin hypercube sampling of the GPSM‐predicted values and uncertainties to generate unique sets of input parameters for 1‐D PFLOTRAN simulations of gas hydrate and free gas formation resulting from methanogenesis to steady state. We ran 100 1‐D simulations spanning a kilometer in depth at 5,297 locations near Blake Ridge. Masses of hydrate and free gas formed at each location were determined by integrating the predicted saturation profiles. Elevated hydrate formation is predicted to occur at depths >500 meters below sea level at this location, and is strongly associated with high seafloor total organic carbon values. We produce representative maps of expected hydrate occurrence for the study area based on multiple realizations that can be validated against geophysical observations.

中文翻译：

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基于机器学习和概率储层模拟的布雷克岭天然气水合物形成预测

甲烷水合物是在低温和相对高压下形成的在水晶格内部含有甲烷的固体结构。大陆架上存在适当的水合物形成条件或与多年冻土有关。水合物作为天然气的潜力及其在全球碳循环中的作用已赢得了科学兴趣。尽管甲烷水合物是在多个不同的地理环境中收集的，但由于稀疏的相关数据，甲烷水合物的数量和在沉积物中的分布仍然受到限制。使用统计和机器学习方法，我们开发了一种工作流程来概率性地预测来自本地微生物甲烷来源的甲烷水合物的发生。这种方法利用了全球预测海床模型（GPSM）生成的机器学习的全球地图作为统计采样软件Dakota和多相储层模拟软件PFLOTRAN的输入。Dakota对GPSM预测的值和不确定性进行拉丁超立方采样，以生成唯一的输入参数集，用于一维PFLOTRAN模拟甲烷化作用到稳态导致的水合物和游离气形成。我们在布雷克岭附近的5,297个位置进行了100个一维模拟，深度跨越一公里。通过积分预测的饱和度轮廓，可以确定在每个位置形成的水合物和自由气体的质量。在该位置，水合物的形成预计会发生在海平面以下> 500米的深度，并与较高的海底总有机碳值紧密相关。我们基于可针对地球物理观测进行验证的多种认识，为研究区域生成了预期水合物发生的代表性地图。