The Qiwu Mine is located in the Ten Xian coal field of Shandong province.It experienced repeated volcanic activity,after the coal beds formed,where magma intrusion was significant The effect of coal reservoir porosity...The Qiwu Mine is located in the Ten Xian coal field of Shandong province.It experienced repeated volcanic activity,after the coal beds formed,where magma intrusion was significant The effect of coal reservoir porosity after magma intrusion was studied by analysis of regional and mine structure and magmatic activity.Experimental methods including maceral measurement under the microscope and mercury porosimetry were used for testing the pore structure.The authors believe that magma intrusion into low-rank bituminous coal causes reservoir porosity to gradually increase：the closer to the magmatic rock a sample is,the less the porosity.The pore size distribution also changes.In the natural coal bed the pore size is mainly in the transitive and middle pore range.However,the coal changes to anthracite next to the magmatic rock and larger pores dominate.Regional magma thermal evolution caused coal close to magmatic rock to be roasted,which reduced the volatile matter,developed larger holes,and destroyed plant tissue holes.The primary reason for a porosity decrease in the vicinity of magmatic rock is that Bituminite resulting from the roasting fills the holes that were present initially.展开更多
Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosi...Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.展开更多
The porosity of a rock is one of the most important reservoir properties. It controls the reservoir storage capacity. In other words, porosity quantifies the amount of fluids that the rock can store. Most of the world...The porosity of a rock is one of the most important reservoir properties. It controls the reservoir storage capacity. In other words, porosity quantifies the amount of fluids that the rock can store. Most of the world＇s giant fields produce hydrocarbons from carbonate reservoirs. Carbonate rocks contain more than 50% of the world＇s hydrocarbon reserves. Porosity and compressional wave velocity of 41 carbonate samples were determined under ambient conditions in laboratory. The samples were collected from seven shallow wells in west Tushka area, south Western Desert, Egypt. This paper evaluates the well known Wyllie and Raymer equations, an empirical linear equation, and a generalized model for porosity estimation from compressional wave velocity of saturated carbonate samples. Based on the comparison of the predicting identified to provide the most reliable porosity estimation. qualities, the Raymer equation and the empirical linear equation were展开更多
基金the National Basic Research Program of China(No.2009CB219605)the Key Program of the National Natural Science Foundation of China(No.40730422)the National Major Project of Science and Technology(No.2008ZX05034-04)
文摘The Qiwu Mine is located in the Ten Xian coal field of Shandong province.It experienced repeated volcanic activity,after the coal beds formed,where magma intrusion was significant The effect of coal reservoir porosity after magma intrusion was studied by analysis of regional and mine structure and magmatic activity.Experimental methods including maceral measurement under the microscope and mercury porosimetry were used for testing the pore structure.The authors believe that magma intrusion into low-rank bituminous coal causes reservoir porosity to gradually increase：the closer to the magmatic rock a sample is,the less the porosity.The pore size distribution also changes.In the natural coal bed the pore size is mainly in the transitive and middle pore range.However,the coal changes to anthracite next to the magmatic rock and larger pores dominate.Regional magma thermal evolution caused coal close to magmatic rock to be roasted,which reduced the volatile matter,developed larger holes,and destroyed plant tissue holes.The primary reason for a porosity decrease in the vicinity of magmatic rock is that Bituminite resulting from the roasting fills the holes that were present initially.
文摘Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.
文摘The porosity of a rock is one of the most important reservoir properties. It controls the reservoir storage capacity. In other words, porosity quantifies the amount of fluids that the rock can store. Most of the world＇s giant fields produce hydrocarbons from carbonate reservoirs. Carbonate rocks contain more than 50% of the world＇s hydrocarbon reserves. Porosity and compressional wave velocity of 41 carbonate samples were determined under ambient conditions in laboratory. The samples were collected from seven shallow wells in west Tushka area, south Western Desert, Egypt. This paper evaluates the well known Wyllie and Raymer equations, an empirical linear equation, and a generalized model for porosity estimation from compressional wave velocity of saturated carbonate samples. Based on the comparison of the predicting identified to provide the most reliable porosity estimation. qualities, the Raymer equation and the empirical linear equation were
