Abstract Characterization of geothermal reservoirs is an important step for exploration and development of geothermal energy, which is reliable and sustainable for the future. Based on the inversion results of seismic reflection data, lithofacies and porosity are predicted beyond well locations on a potential geothermal reservoir in the north of Copenhagen, onshore Denmark. To classify the lithofacies, a new system of Artificial Neural Networks-Hidden Markov Models is proposed to consider the complex spatial distribution of rock properties and the intrinsic depositional rules. Artificial Neural Networks can overcome the common Gaussian assumption for the distribution of rock properties. At the same time, the transition matrix in Hidden Markov Models provides the conditional probability for the lithofacies transitions along the vertical direction. After classification, the resulting lithofacies are used to constrain the porosity prediction, in which the Artificial Neural Networks is trained and applied within each type of lithofacies, as a regression process. The novelty of this approach is in the integration of statistics and computer science algorithms that allows capturing hidden and complex relations in the data that cannot be explained by traditionally deterministic geophysical equations. This workflow could also improve the prediction accuracy and the uncertainty quantification of the porosity distribution given rock properties.

中文翻译：

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丹麦地热储层岩相分类及地震反演的相依赖孔隙度估计

摘要 地热储层表征是地热能勘探开发的重要步骤，地热能是未来可靠且可持续的。根据地震反射数据的反演结果，预测了丹麦陆上哥本哈根北部潜在地热储层井位以外的岩相和孔隙度。为了对岩相进行分类，提出了一种新的人工神经网络-隐马尔可夫模型系统，以考虑岩石性质的复杂空间分布和内在沉积规律。人工神经网络可以克服岩石特性分布的常见高斯假设。同时，隐马尔可夫模型中的转换矩阵为岩相沿垂直方向的转换提供了条件概率。分类后，得到的岩相用于约束孔隙度预测，其中人工神经网络在每种类型的岩相中进行训练和应用，作为回归过程。这种方法的新颖之处在于整合了统计学和计算机科学算法，可以捕捉数据中隐藏的复杂关系，而这些关系无法用传统的确定性地球物理方程来解释。该工作流程还可以提高给定岩石特性的孔隙度分布的预测精度和不确定性量化。这种方法的新颖之处在于整合了统计学和计算机科学算法，可以捕捉数据中隐藏的复杂关系，而这些关系无法用传统的确定性地球物理方程来解释。该工作流程还可以提高给定岩石特性的孔隙度分布的预测精度和不确定性量化。这种方法的新颖之处在于整合了统计学和计算机科学算法，可以捕捉数据中隐藏的复杂关系，而这些关系无法用传统的确定性地球物理方程来解释。该工作流程还可以提高给定岩石特性的孔隙度分布的预测精度和不确定性量化。