Borehole-Based Monitoring of Mining-Induced Movement in Ultrathick-and-Hard Sandstone Strata of the Luohe Formation

Water outbursts and rock bursts often occur during the mining of coal seams under water-rich sandstone strata with thicknesses exceeding 50 m, otherwise called ultrathick-and-hard strata (UTHS), which are common throughout the mining areas of northwestern China. It is important to understand the behaviors of their movement and the evolution of their internal fractures to inform the formulation of effective disaster prevention. Due to the presence of the Luohe Formation UTHS in the overburden of the Tingnan Coal Mine in the Binchang mining area and the powerful mining-induced pressure (MIP) events that occurred during the excavation of Panel #2, the internal strata movement of the overburden and the evolution of its fractures were monitored in situ by fiber optic and multipoint borehole extensometers (MPBX) during the excavation of Working Face #207. It was found that a large number of ring-shaped fractures were observed at 24.8–81 m above the lower boundary of the Luohe Formation—in areas above the goaf of Working Face #206—before Working Face #207 was mined. When Working Face #207 was mined, the fractures that were originally located in the deep strata of the Luohe Formation started to close and migrate towards shallow strata. Crack closure and migration were also observed during the monitoring of internal strata movement. Furthermore, the final displacements of Y1-1-1#, Y1-2-2#, and Y1-2-3# relative to the surface were 77, 248, and 134 mm, which were very small relative to the surface subsidence of 1380 mm. It was found that mining-induced perturbations caused the Luohe Formation UTHS to subside continuously and no risk of a large and sudden break would occur in the Luohe Formation UTHS during the mining of Working Face #207. The results of this study provide important data for the safety of mining operations at Working Face #207, which were validated by microseismic monitoring during the mining of it.

Horizon Picking from SBP Images Using Physicals-Combined Deep Learning

Horizon picking from sub-bottom profiler (SBP) images has great significance in marine shallow strata studies. However, the mainstream automatic picking methods cannot handle multiples well, and there is a need to set a group of parameters manually. Considering the constant increase in the amount of SBP data and the high efficiency of deep learning (DL), we proposed a physicals-combined DL method to pick the horizons from SBP images. We adopted the DeeplabV3+ net to extract the horizons and multiples from SBP images. We generated a training dataset from the Jiaozhou Bay survey (Shandong, China) and the Zhujiang estuary survey (Guangzhou, China) to increase the applicability of the trained model. After the DL processing, we proposed a simulated Radon transform method to eliminate the surface-related multiples from the prediction by combining the designed pseudo-Radon transform and correlation analysis. We verified the proposed method using actual data (not involved in the training dataset) from Jiaozhou Bay and Zhujiang estuary. The positions of picked horizons are accurate, and multiples are suppressed.

Consideration of the Influence of Carbonate Cement on the Accuracy of Prediction of Well Start-Up Flow Rates in Deep Reservoirs of the West Siberian Oil and Gas Basin on the Example of Reservoir U1 Formation

Oil reservoirs are often affected by tectonic processes throughout their lifetime. Tectonic processes contribute to the impact on the formation of a number of mechanical and chemical factors. These factors change the composition and structure of the reservoir and this affects the reservoir properties of the reservoir. Deep-seated reservoirs experience a longer and more intense impact of tectonic processes. A more detailed study of the composition and properties of reservoirs for an accurate forecast of reservoir properties and their productivity potential is due to this. Standard log interpretation methods have been developed based on shallow strata. These methods do not allow taking into account secondary changes in the reservoir and make the calculations of the starting flow rates of wells reliable. J1 stratum West Wing on Nizhnevartovsky set is a prime example of this.

Analysis and Research on Mobile Drilling Rig for Deep Seabed Shallow Strata

Drilling rigs for deep seabed shallow strata are commonly used to explore ocean cobalt-rich crust resources and other fields. This paper mainly presents the structure and mechanism of a mobile drilling rig for use in acquiring seafloor cores that are up to 1.5 m in length. The software Simcenter Amesim is used to establish the mobile drilling rig's hydraulic propulsion system model, which is the basis and a core part of the rig. Moreover, closed-loop and PID (proportion-integral-differential) control methods are separately used to control the hydraulic propulsion system for simulation analysis. Comparison of the simulation results shows that the PID control method is more convincing in verifying the design rationality of the hydraulic propulsion system. In the simulation of the PID-controlled hydraulic propulsion system, the co-simulation technology of Simcenter Amesim and MATLAB/Simulink not only establishes the hydraulic and control models but also determines the relevant simulation parameters, thereby helping improve system simulation efficiency. In its verification deployment in the South China Sea, the mobile drilling rig has been operated many times at different depths, and some cores have been successfully obtained. It was also used during the 55th Voyage of China Oceanic Scientific Expedition, which was supported by the China Ocean Mineral Resources R&D Association. Several sites were explored, and a large number of cobalt-rich crust cores were obtained. Theory and sea trials are explained to support further research on the survey of abyssal resources.

Shallow gas hydrates off southwest Taiwan and their mechanisms

We have collected two shallow gas hydrate samples at two sites having different geological settings off southwest Taiwan during the cruise MD214 in 2018. The first core site, MD18-3542, is on the South Yuan-An East Ridge at ~ 1200 m water deep, where a structural unconformity covered by fine-silt sediments appears at ~ 5.5 m below the seafloor. The second core site, MD18-3543, is close to the Good-Weather Ridge at ~ 1100 m water deep, where a gas-related pockmark structure and authigenic carbonates are present at shallow strata with fine-silt sediments near the seafloor. Sediment properties of core MD18-3542 are distinctively different above and below the layer corresponding to the unconformity. Both cores show obvious gaps or voids in the lower core halves. The core features could be linked to the dissociated methane upward migrating from deep strata. Core site settings with upwelling methane would favor the formation of shallow gas hydrates. At site MD18-3542, the shallow hydrate could be formed due to high concentration methane kept beneath the unconformity covered by fine-silt sediments. At site MD18-3543, the shallow hydrate could be formed due to an extremely high flux of upwelling methane trapped either beneath the authigenic carbonates or fine-silt sediments.

On a Deep Learning Method of Estimating Reservoir Porosity

Porosity is an important parameter for the oil and gas storage, which reflects the geological characteristics of different historical periods. The logging parameters obtained from deep to shallow strata show the stratigraphic sedimentary characteristics in different geological periods, so there is a strong nonlinear mapping relationship between porosity and logging parameters. It is very important to make full use of logging parameters to predict the shale content and porosity of the reservoir for precise reservoir description. Deep neural network technology has strong data structure mining ability and has been applied to shale content prediction in recent years. In fact, the gated recurrent unit (GRU) neural network has further advantage in processing serialized data. Therefore, this study proposes a method to predict porosity by combining multiple logging parameters based on the GRU neural network. Firstly, the correlation measurement method based on Copula function is used to select the logging parameters most relevant to porosity parameters. Then, the GRU neural network is used to identify the nonlinear mapping relationship between logging data and porosity parameters. The application results in an exploration area of the Ordos basin show that this method is superior to multiple regression analysis and recurrent neural network method, which indicates that the GRU neural network is more effective in predicting a series of reservoir parameters such as porosity.

Constructing 3D geological models based on large-scale geological maps

The construction of 3D geological models based on geological maps is a subject worthy of study. The construction of geological interfaces is the key process of 3D geological modeling. It is hard to build the bottom interfaces of quaternary strata only using boundaries in large-scale geological maps. Moreover, it is impossible to construct bedrock geological interfaces through sparse occurrence data in large-scale geological maps. To address the above-mentioned two difficulties, we integrated two key algorithms into a new 3D modeling workflow. The buffer algorithm was used to construct virtual thickness contours of quaternary strata. The Inverse Distance Weighted (IDW) algorithm was applied to occurrence interpolation. Using a regional geological map of a city in southern China, the effectiveness of our workflow was verified. The complex spatial geometry of quaternary bottom interfaces was described in detail through boundaries buffer. The extension trends of bedrock geological interfaces were reasonably constraint by occurrence interpolation. The 3D geological model constructed by our workflow accords with the semantic relationship of tectonics. Through the model, the complex spatial structure of urban shallow strata can be displayed stereoscopically. It can provide auxiliary basis for decision-making of urban underground engineering.

The Research on the Mobile Drilling Rig for Deep Seabed Shallow Strata

At present, the drilling rig for deep seabed shallow strata is widely used in the exploration of ocean cobalt-rich crust resources and other fields, which not only can obtain solid core samples at a specific station, but the operation process is relatively safe. This paper mainly presents the structure and mechanism of mobile drilling rig in acquiring the seafloor cores (up to 1.5 m long). Based on the function of the AMESim software, the hydraulic propulsion system model of mobile drilling rig is established, which is the basis and core part of the mobile drilling rig. Moreover, the control methods of closed-loop and PID are respectively used to control the hydraulic propulsion system for simulation analysis. Through the comparison of simulation results, it is found that the PID control method is more convincing in verifying the design rationality of hydraulic propulsion system. In the simulation of the PID-controlled hydraulic propulsion system, the co-simulation technology of AMESim and Matlab/Simulink not only establishes the hydraulic model and control model, but also determines the relevant simulation parameters, which is helpful to improve the system simulation efficiency. In its verification deployment in the South China Sea, the mobile drilling rig has been operated for many times at different depths, and some cores have been successfully obtained. Furthermore, the mobile drilling rig has been used during the 55th Voyage of China Oceanic Scientific Expedition supported by China Ocean Mineral Resources R&D Association. Several sites were explored and a large number of cobalt-rich crust cores were obtained. The powerful theory and sea trails are provided to support for the further research on survey of the abyssal resource.

The Temperature Evaluation of the Buried Hill Geothermal Reservoirs in the Jizhong Depression, Bohai Bay Basin, China

The Jizhong Depression is located in the western Bohai Bay Basin, eastern China. The deep strata are mainly composed of carbonate buried hill, and the shallow strata are a mainly siliciclastic deposition. In the present work, the Na-K-Mg triangle diagram and geothermometers were used to investigate the geochemical characteristics of shallow groundwater and reservoir temperature features of three geothermal reservoirs in the depression, including the Ordovician, the Cambrian, and the Precambrian Wumishan Formation. The results showed that the geothermal water in the depression could be divided into three groups: group I, Cl· HCO 3 -Na type; group II, Cl-Na type; and group III, Cl-Na·Ca type. By using the Na-K-Mg triangle diagram, group II and group III geothermal water samples were identified as the partially equilibrated water, whose temperature of the geothermal reservoir can be calculated based on the cation geothermometers. The ranges of the calculated temperature of the shallow strata and the deep strata are 91~146°C and 147~176°C, respectively. It has the good results obtained with some cation geothermometers in a geothermal system hosted in carbonate rocks like the studied area. The analysis workflow and calculation data obtained in this work contribute to the evaluation of the temperature field and the exploration and development of the geothermal resources in the Bohai Bay Basin.