The fracture gradient is a critical parameter for drilling mud weight design in the energy industry. A new method in fracture gradient prediction is proposed based on analyzing worldwide leak-off test (LOT) data in offshore drilling. Current fracture gradient prediction methods are also reviewed and compared to the proposed method. We analyze more than 200 LOT data in several offshore petroleum basins and find that the fracture gradient depends not only on the overburden stress and pore pressure, but also on the depth. The data indicate that the effective stress coefficient is higher at a shallower depth than that at a deeper depth in the shale formations. Based on this finding, a depth-dependent effective stress coefficient is proposed and applied for fracture gradient prediction. In some petroleum basins, many wells need to be drilled through long sections of salt formations to reach hydrocarbon reservoirs. The fracture gradient in salt formations is very different from that in other sedimentary rocks. Leak-off test data in the salt formations are investigated, and a fracture gradient prediction method is proposed. Case applications are examined to compare different fracture gradient methods and validate the proposed methods. The reasons why the LOT value is higher than its overburden gradient are also explained.

Micro-seismic monitoring during mining operations generates spatiotemporal data that could indicate strata fractures and deformations leading to water inrush anomalies. However, current water inrush prediction methods face challenges from the data non-stationarity and multi-dimensionality, resulting in low prediction precision and effectiveness. This study proposes an innovative data-driven approach for predicting mining water inrush using field 3D micro-seismic monitoring data. The approach couples machine learning and deep learning models to analyze micro-seismic events, pre-processed using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Random Sample Consensus (RANSAC) algorithms for both data denoising and water inrush risk locating. Weighting periods are analyzed in periodic variations of event attributes using the fast Fourier transform (FFT), continuous wavelet transform (CWT), empirical mode decomposition (EMD), and seasonal and trend decomposition using Loess (STL) methods. Anomalies are detected using the long short-time memory (LSTM)+absolute error (AE), isolation forest (iForest) and LSTM+iForest models. The study is conducted using a micro-seismic dataset acquired during intermittent water inflow anomalies in the Xingdong coal mine in China. The approach accurately predicts a major water inrush incident hours prior to its occurrence merging detected anomalies with the obtained weighting periods, which are also used for model calibration. Future studies could focus on performance evaluation and calibration of the deep learning models using micro-seismic datasets from different mining operations, and expanding the approach’s scope by incorporating other geophysical exploration technologies like the electrical methods to further study the presence and movement of water in mines for improving mining safety.

This study investigates multiple types of water resources in the western mining area in China, their supply-demand balance by using the same dimension gray recurrence dynamic model GM (1, 1), and water content coefficients of mines to ease water supply-demand contradiction. A multi-objective programming model is proposed for optimal water resources allocation management. Optimal technical schemes for water resources allocation among different users are obtained. The optimization model improves upon the previous studies by using water demand and water supply forecast. Coordinated development for mining safety, mine water utilization, and remediation and control of water environment is achieved.