The development of unconventional resources, such as shale gas and tight sane gas, requires the integration of multi-disciplinary knowledge to resolve many engineering problems in order to achieve economic production ...The development of unconventional resources, such as shale gas and tight sane gas, requires the integration of multi-disciplinary knowledge to resolve many engineering problems in order to achieve economic production levels. The reservoir heterogeneit3 revealed by different data sets, such as 3D seismic and microseismic data, can more full3 reflect the reservoir properties and is helpful to optimize the drilling and completioT programs. First, we predict the local stress direction and open or close status of the natura fractures in tight sand reservoirs based on seismic curvature, an attribute that reveals reservoi heterogeneity and geomechanical properties. Meanwhile, the reservoir fracture network is predicted using an ant-tracking cube and the potential fracture barriers which can affec hydraulic fracture propagation are predicted by integrating the seismic curvature attribute anc ant-tracking cube. Second, we use this information, derived from 3D seismic data, to assis in designing the fracture program and adjusting stimulation parameters. Finally, we interpre the reason why sand plugs will occur during the stimulation process by the integration of 3E seismic interpretation and microseismic imaging results, which further explain the hydraulic fracure propagation controlling factors and open or closed state of natural fractures in tigh sand reservoirs.展开更多
Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping...Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.展开更多
In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more preci...In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more precise. Constrained sparse spike inversion(CSSI) has advantage in oil and gas reservoir predication because it does not rely on the original model. By analyzing the specific algorithm of CSSI,the accuracy of inversion is controlled. Oriente Basin in South America has the low amplitude in geological structure and complex lithologic trap. The well predication is obtained by the application of CSSI.展开更多
文摘The development of unconventional resources, such as shale gas and tight sane gas, requires the integration of multi-disciplinary knowledge to resolve many engineering problems in order to achieve economic production levels. The reservoir heterogeneit3 revealed by different data sets, such as 3D seismic and microseismic data, can more full3 reflect the reservoir properties and is helpful to optimize the drilling and completioT programs. First, we predict the local stress direction and open or close status of the natura fractures in tight sand reservoirs based on seismic curvature, an attribute that reveals reservoi heterogeneity and geomechanical properties. Meanwhile, the reservoir fracture network is predicted using an ant-tracking cube and the potential fracture barriers which can affec hydraulic fracture propagation are predicted by integrating the seismic curvature attribute anc ant-tracking cube. Second, we use this information, derived from 3D seismic data, to assis in designing the fracture program and adjusting stimulation parameters. Finally, we interpre the reason why sand plugs will occur during the stimulation process by the integration of 3E seismic interpretation and microseismic imaging results, which further explain the hydraulic fracure propagation controlling factors and open or closed state of natural fractures in tigh sand reservoirs.
基金Supported by the National Natural Science Foundation of China(61074153)
文摘Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.
基金Supported by the Fundamental Research Funds for the Central Universities(No.2011PY0186)
文摘In the conditions of low Signal-to-Noise Ratio(SNR) of seismic data and a small quality of log information,the consequences of seismic interpretation through the impedance inversion of seismic data could be more precise. Constrained sparse spike inversion(CSSI) has advantage in oil and gas reservoir predication because it does not rely on the original model. By analyzing the specific algorithm of CSSI,the accuracy of inversion is controlled. Oriente Basin in South America has the low amplitude in geological structure and complex lithologic trap. The well predication is obtained by the application of CSSI.
