Forming Condition and Geology Prediction Techniques of Deep Clastic Reservoirs

1 Introduction As new exploration domain for oil and gas,reservoirs with low porosity and low permeability have become a hotspot in recent years(Li Daopin,1997).With the improvement of technology,low porosity and low

Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.

Prediction of the geological indicators in TBM tunnel based on optimized proportion of surrounding rock grades

With tunnel boring machine being used in underground engineering,accurate geological indicators have been the important basis for tunnel boring machine(TBM)construction.Back propagation neural network(BPNN)has been used to predict the geological indicators of tunnels in previous studies.Nevertheless,these studies ignored the imbalance proportion of surrounding rock grades,leading to the indiscriminate use of data,thus affecting the predictive effect of BPNN.In order to prove the importance of the proportion of surround-ing rock grade in geological prediction,we mainly attempt to utilize particle swarm optimization(PSO)to optimize the proportion of sample data,and integrate with BPNN to establish a PSO-BPNN theoretical model to predict geological indicators.At the same time,combined with the actual engineering data,5 tunneling indicators were selected as input and 4 geological indicators were selected as out-put by a variety of dimensionality reduction methods.The geological indicators are density,uniaxial compressive strength,internal fric-tion angle(u)and Poisson’s ratio(e).On this basis,the PSO-BPNN prediction model was established in detail.By comparing the prediction of traditional BPNN,PSO-BPNN and other optimization-integrated models,the result shows that optimized proportion of surrounding rock grades reduces the prediction error and improves the interpretability of the prediction model.Meanwhile,we com-bined the theory of surrounding rock partition to illustrate the rationality of surrounding rock proportion in PSO result,that is,the proportion of complex surrounding rock should be increased appropriately to improve the prediction result.Ultimately,based on the optimization-integrated models with engineering data and the surrounding rock classification theory,the importance of proportion of surrounding rock grades for tunnel geological prediction is confirmed.

Chemical kinetics evaluation and its application of natural gas generation derived from the Yacheng Formation in the deep-water area of the Qiongdongnan Basin,China

The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes（C1, C2, C3 and C4-6）. The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons（C4-6） is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio（TR） is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.

Construction risks of Huaying mount tunnel and countermeasures

The Chongqing-Guang＇an motorway is planned to cross Huaying mount at Jingguan town of Chongqing city. The whole mount is a colossal anticline whose core is consisted of coal measure strata （upper Permian Longtan formation P21） and the limbs are limestone strata （middle Triassic Leikoupo formation T21 and lower Triassic Jialingjiang formation T1j）. The tunneling is full of risks of collapse, gas explosion or gas outburst, water （mud） inrush, gas inrush because of existence of faults, high pressure gas, karst tectonics and coal goafs around the tunnel. In order to cope with the high risk, two main countermeasures were taken to ensure security of construction. One is geology prediction, and the other is automatic wireless real-time monitoring system, which contains monitoring of video, wind speed, poisonous gas （CH4, CO, H2S, SO2）, people location, and automatic power-off equipment while gas contents being more than warning threshold. These ascertained the engineering safety effectively.