Geomechanical properties are essential for safe drilling, successful completion, and exploration of both conventional and unconventional reservoirs, e.g. deep shale gas and shale oil. Typically, these properties could be calculated from sonic logs. However, in shale reservoirs, it is time-consuming and challenging to obtain reliable logging data due to borehole complexity and lacking of information, which often results in log deficiency and high recovery cost of incomplete datasets. In this work, we propose the bidirectional long short-term memory (BiLSTM) which is a supervised neural network algorithm that has been widely used in sequential data-based prediction to estimate geomechanical parameters. The prediction from log data can be conducted from two different aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data for cross validation; 2) Cross-Well prediction, a group of wells from the same geographical region are divided into training set and testing set for cross validation, as well. The logs used in this work were collected from 11 wells from Jimusaer Shale, which includes gamma ray, bulk density, resistivity, and etc. We employed 5 various machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calculate geomechanical properties, of which accuracy is also improved in contrast to conventional methods.

This content is only available via PDF.
You can access this article if you purchase or spend a download.