Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fra...Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.展开更多
The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algori...The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.展开更多
This paper analyzes the gas source of the horizontally sectioned fully mechanized caving face in the steeply inclined and extra-thick seam of Adaohai Coal Mine, and numerically simulates the stress distribution and pr...This paper analyzes the gas source of the horizontally sectioned fully mechanized caving face in the steeply inclined and extra-thick seam of Adaohai Coal Mine, and numerically simulates the stress distribution and pressure relief of the lower section coal after the upper section working face is mined. It theoretically analyzed the reasonable layout of the drainage boreholes, and designed the drainage borehole layout accordingly. In the upper and lower section of the working face, the actual drainage effect of the boreholes was inspected, and the air exhaust gas volume in the working face was statistically analyzed. It was confirmed that the layout of boreholes was reasonable, the gas control effect of working face was greatly improved and fully met the needs of safe mining. The control effect was greatly improved and the need for safe mining was fully met, and thus a gas drainage technology suitable for the coal seam storage conditions and mining technology of the Adaohai Coal Mine was found. That is to say: the gas emission from the working face of the section mining mainly comes from its lower coal body. Pre-draining the lower coal body of the section and depressurizing gas interception and drainage are the key to effectively solve the problem of gas emission from the working face. Drainage boreholes in the working face of the section should be arranged at high and low positions. The high-level boreholes are located about 2 m from the top of the working face, and the high-level boreholes are about 9 m away from the top of the working face. Through the pre drainage of high and low-level boreholes in advance and the interception and pressure relief drainage, the gas control in the horizontal sublevel fully mechanized caving mining face in steep and extra thick coal seam can realize a virtuous cycle.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52174044,52004302)Science Foundation of China University of Petroleum,Beijing(No.ZX20200134,2462021YXZZ012)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX 2020-01-07).
文摘Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.
基金Supported by the National Key R&DPlan Project(2022YFE0129900)National Natural Science Foundation of China(52074338).
文摘The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.
文摘This paper analyzes the gas source of the horizontally sectioned fully mechanized caving face in the steeply inclined and extra-thick seam of Adaohai Coal Mine, and numerically simulates the stress distribution and pressure relief of the lower section coal after the upper section working face is mined. It theoretically analyzed the reasonable layout of the drainage boreholes, and designed the drainage borehole layout accordingly. In the upper and lower section of the working face, the actual drainage effect of the boreholes was inspected, and the air exhaust gas volume in the working face was statistically analyzed. It was confirmed that the layout of boreholes was reasonable, the gas control effect of working face was greatly improved and fully met the needs of safe mining. The control effect was greatly improved and the need for safe mining was fully met, and thus a gas drainage technology suitable for the coal seam storage conditions and mining technology of the Adaohai Coal Mine was found. That is to say: the gas emission from the working face of the section mining mainly comes from its lower coal body. Pre-draining the lower coal body of the section and depressurizing gas interception and drainage are the key to effectively solve the problem of gas emission from the working face. Drainage boreholes in the working face of the section should be arranged at high and low positions. The high-level boreholes are located about 2 m from the top of the working face, and the high-level boreholes are about 9 m away from the top of the working face. Through the pre drainage of high and low-level boreholes in advance and the interception and pressure relief drainage, the gas control in the horizontal sublevel fully mechanized caving mining face in steep and extra thick coal seam can realize a virtuous cycle.
