To supplement, adjust and improve the division plan of crude oil strata in the west of Mahuangshan Mountain. Combining well logging, core, and drilling analysis and testing data, Petrel software was utilized to classi...To supplement, adjust and improve the division plan of crude oil strata in the west of Mahuangshan Mountain. Combining well logging, core, and drilling analysis and testing data, Petrel software was utilized to classify and compare the 10 layers of the Yan’an Formation. Draw 6 “net-like” skeleton profiles for Ningdong No. 2 and No. 3 wells, involving 31 wells. On the basis of the large-layer strata division, Yan 8 and Yan 9 sections are divided and compared;five “net-like” skeleton profiles were drawn in the 5 well, involving 16 wells. On the basis of the large-layer division, the small-layer division and comparison were performed by Yan 8 and Yan 9. The results show that the Yan 8 oil layer is divided into two stratigraphic units, and Yan 8 and Yan 82, and the Yan 9 oil layer is divided into two stratigraphic units, Yan 91 and Yan 92;the Yan 2 layer in the 2 and 3 well blocks of Ningdong is in the marker layer. The top of the coal seam, Yan 8 layers in the Ningdong 5 well area is on the top of the auxiliary marker seam. The fine division of small layers into tiny layers of sedimentary microfacies, reservoir heterogeneity, development dynamic analysis, remaining oil distribution and other studies provide indispensable data.展开更多
Based on the productivity equation of coalbed methane (CBM) well, considering the impact of coal reservoir reformability on gas well productivity, the main production layer optimization index in the “three-step metho...Based on the productivity equation of coalbed methane (CBM) well, considering the impact of coal reservoir reformability on gas well productivity, the main production layer optimization index in the “three-step method” of optimal combination of production layers is corrected, and then the CBM production layer potential index is introduced to evaluate favorable areas for commingled multi-coal seam production. Through analysis of the key parameters of coal reservoirs affecting the CBM productivity index, a development unit division method for areas with multi-coal seams is established, and a quantitative grading index system is proposed. On this basis, the evaluation process of CBM development favorable area is developed: the mature 3-D modeling technology is used to characterize the reservoir physical properties of multi-coal seams in full-scale;the production layer potential index of each grid is calculated, and the production layer potential index contour under single-layer or commingled multi-layer production are plotted;according to the distribution of the contour of production layer potential index, the quantitative index of CBM development unit is adopted to outline the grade I, II, III coal reservoir distribution areas, and thus to pick out the favorable development areas. The practical application in the Yuwang block of Laochang in Yunnan proved that the favorable area evaluation process proposed can effectively overcome the defects of selecting favorable development areas only relying on evaluation results of a major coal seam pay, and enhance the accuracy of the evaluation results, meeting the requirements of selecting favorable areas for multi-coal seam commingled CBM production.展开更多
Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such a...Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts.The development of machine and deep learning algorithms has reduced the burden of achieving reli-able and good communication schemes in the underwater acoustic environment.This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA),Time Divi-sion Multiple Access(TDMA),and Orthogonal Frequency Division Multiplexing(OFDM)techniques using the hybrid combination of the convolutional neural net-works(CNN)and ensemble single feedforward layers(SFL).The convolutional neural networks are used for channel feature extraction,and boosted ensembled feedforward layers are used for modulation selection based on the CNN outputs.The extensive experimentation is carried out and compared with other hybrid learning models and conventional methods.Simulation results demonstrate that the performance of the proposed hybrid learning model has achieved nearly 98%accuracy and a 30%increase in BER performance which outperformed the other learning models in achieving the communication schemes under dynamic underwater environments.展开更多
文摘To supplement, adjust and improve the division plan of crude oil strata in the west of Mahuangshan Mountain. Combining well logging, core, and drilling analysis and testing data, Petrel software was utilized to classify and compare the 10 layers of the Yan’an Formation. Draw 6 “net-like” skeleton profiles for Ningdong No. 2 and No. 3 wells, involving 31 wells. On the basis of the large-layer strata division, Yan 8 and Yan 9 sections are divided and compared;five “net-like” skeleton profiles were drawn in the 5 well, involving 16 wells. On the basis of the large-layer division, the small-layer division and comparison were performed by Yan 8 and Yan 9. The results show that the Yan 8 oil layer is divided into two stratigraphic units, and Yan 8 and Yan 82, and the Yan 9 oil layer is divided into two stratigraphic units, Yan 91 and Yan 92;the Yan 2 layer in the 2 and 3 well blocks of Ningdong is in the marker layer. The top of the coal seam, Yan 8 layers in the Ningdong 5 well area is on the top of the auxiliary marker seam. The fine division of small layers into tiny layers of sedimentary microfacies, reservoir heterogeneity, development dynamic analysis, remaining oil distribution and other studies provide indispensable data.
基金Supported by the National Natural Science Foundation of China(No.41772155)the National Science and Technology Major Project of China(No.2016ZX05044-002)the Fundamental Research Funds for the Central Universities of China(No.2015XKZD07)
文摘Based on the productivity equation of coalbed methane (CBM) well, considering the impact of coal reservoir reformability on gas well productivity, the main production layer optimization index in the “three-step method” of optimal combination of production layers is corrected, and then the CBM production layer potential index is introduced to evaluate favorable areas for commingled multi-coal seam production. Through analysis of the key parameters of coal reservoirs affecting the CBM productivity index, a development unit division method for areas with multi-coal seams is established, and a quantitative grading index system is proposed. On this basis, the evaluation process of CBM development favorable area is developed: the mature 3-D modeling technology is used to characterize the reservoir physical properties of multi-coal seams in full-scale;the production layer potential index of each grid is calculated, and the production layer potential index contour under single-layer or commingled multi-layer production are plotted;according to the distribution of the contour of production layer potential index, the quantitative index of CBM development unit is adopted to outline the grade I, II, III coal reservoir distribution areas, and thus to pick out the favorable development areas. The practical application in the Yuwang block of Laochang in Yunnan proved that the favorable area evaluation process proposed can effectively overcome the defects of selecting favorable development areas only relying on evaluation results of a major coal seam pay, and enhance the accuracy of the evaluation results, meeting the requirements of selecting favorable areas for multi-coal seam commingled CBM production.
文摘Achieving sound communication systems in Under Water Acoustic(UWA)environment remains challenging for researchers.The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts.The development of machine and deep learning algorithms has reduced the burden of achieving reli-able and good communication schemes in the underwater acoustic environment.This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA),Time Divi-sion Multiple Access(TDMA),and Orthogonal Frequency Division Multiplexing(OFDM)techniques using the hybrid combination of the convolutional neural net-works(CNN)and ensemble single feedforward layers(SFL).The convolutional neural networks are used for channel feature extraction,and boosted ensembled feedforward layers are used for modulation selection based on the CNN outputs.The extensive experimentation is carried out and compared with other hybrid learning models and conventional methods.Simulation results demonstrate that the performance of the proposed hybrid learning model has achieved nearly 98%accuracy and a 30%increase in BER performance which outperformed the other learning models in achieving the communication schemes under dynamic underwater environments.
