At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict th...At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.展开更多
Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when tradit...Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.展开更多
An effective maintenance schedule can largely improve productivity and reduce maintenance costs for enterprises. Many factors, such as resource, production, cost and crew, should be considered when we make maintenance...An effective maintenance schedule can largely improve productivity and reduce maintenance costs for enterprises. Many factors, such as resource, production, cost and crew, should be considered when we make maintenance plans. However, it is difficult to meet all requests of the production targets in practical engineering.The multiple attribute decision making for equipment maintenance is proposed in this paper, and the multiple objectives decision making method is utilized to solve the problem in the maintenance process. In the particular environment and resources, a case is studied to illustrate the model and methods. The model gives the optimal maintenance plan with the analytic hierarchy process method in accordance with the particular criteria. It has been proved that this model and decision methods are scientific and operable.展开更多
基金supported by the "12th Five Year Plan" National Science and Technology Major Special Subject:Well Logging Data and Seismic Data Fusion Technology Research(No.2011ZX05023-005-006)
文摘At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.
基金supported by the Scientific Research Staring Foundation of University of Electronic Science and Technology of China(No.ZYGX2015KYQD049)
文摘Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.
基金the National Natural Science Foundation of China(No.71471147)the Natural Science Research Project of Shaanxi Province(No.2015JQ7273)the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(No.Z2016087)
文摘An effective maintenance schedule can largely improve productivity and reduce maintenance costs for enterprises. Many factors, such as resource, production, cost and crew, should be considered when we make maintenance plans. However, it is difficult to meet all requests of the production targets in practical engineering.The multiple attribute decision making for equipment maintenance is proposed in this paper, and the multiple objectives decision making method is utilized to solve the problem in the maintenance process. In the particular environment and resources, a case is studied to illustrate the model and methods. The model gives the optimal maintenance plan with the analytic hierarchy process method in accordance with the particular criteria. It has been proved that this model and decision methods are scientific and operable.
