Resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S_h) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S_h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S_h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.

电阻率和声波测井被广泛用于使用两种流行的方法之一（（1）声速和（2）电阻率）估算各种沉积系统中的天然气水合物饱和度，但是经常忽略这两种方法的局限性，包括（i）在电阻率法中对经验指数进行井井有条的校准，（ii）在声速法中假设气体水合物的已知孔隙形态，以及（iii）在声速法中存在未知的矿物学和体积模量项。基于横向磁化弛豫时间（T的分析）得出的NMR密度孔隙度的天然气水合物饱和度2）被认为是最精确的方法，但在相对较新的钻探地点，基于NMR的测井的采集受到限制；另外，由于与获取NMR测井曲线有关的更高成本和操作部署限制，其在常规油气藏中的使用并不常见。这项研究提出了一种新方法，该方法可以使用具有神经网络（或随机梯度下降回归）的孔隙度，堆积密度和压缩波（P波）速度测井曲线来预测任何井的天然气水合物饱和度（S _h）校准和/或现有方法的其他上述缺点。通过检查S _h之间的潜在依赖性来开发该方法以及从6条常规日志中选择的，具有12种不同的机器学习（ML）算法的不同的测井记录组合。根据三项不同研究报告的结果，该方法预测S _h的准确性约为84％，优于地震和电阻率法的准确性（≤75％）。该方法在与多年冻土有关的天然气水合物的特定情况下的鲁棒性通过在阿拉斯加北坡上钻探的两口井的测井资料得到证明。