The gas-bearing reservoir in X area is mainly the tight sandstone reservoir characterized by low porosity and permeability, frequently lateral variation and poor connectivity of single sand. The previous research resu...The gas-bearing reservoir in X area is mainly the tight sandstone reservoir characterized by low porosity and permeability, frequently lateral variation and poor connectivity of single sand. The previous research results reveal that the general seismic attributes analysis cannot meet the requirement of fluid identification. This is because the relationship between seismic attributes and their implication is uncertain and ambiguous, which decreases the precision of both reservoir prediction and fluid identification. To overcome the problem, multi-attribute crossplot technology is proposed from the mathematical statistical point of view rather than the correspondence between the seismic attributes and their geological implication. In this method, the wells which have the same statistical law are classified firstly, and then all the interest wells are retained while the wells beyond the statistical law are eliminated, and the seismic attributes sensitive to the same types of eliminated wells are optimized and used to generate crossplots. The nonzero area of their crossplots results just predicts the potential distribution. The discontinuity of subsurface geological conditions results in the non-continuous shape and the seismic bin lead to the mosaic form. The optimization of sensitive attributes relative to the same types of wells is independent from each other, and thus the order of attributes in crossplots does not affect the final prediction results. This method is based on the statistical theory and suitable for the areas such as the study area abundant of lots of well data. Application to X area proves the effectiveness of this method and predicts plane distribution about different types of gas production. Due to the effect of faults and other geological factors, the partition prediction results using multi-attribute crossplots reach 95% of coincidence which is obviously and far higher than the results of the whole area. The final prediction results show that the potential areas with medium and high gas production are mainly concentrated in the northern part of the study area, where lots of development research will be strengthened.展开更多
In this article,based on the acoustic measurements of core samples obtained from the low to medium porosity and permeability reservoirs in the WXS Depression,the densities and P and S wave velocities of these core sam...In this article,based on the acoustic measurements of core samples obtained from the low to medium porosity and permeability reservoirs in the WXS Depression,the densities and P and S wave velocities of these core samples were obtained.Then based on these data,a series of elastic parameters were computed.From the basic theory and previous pore fluid research results,we derived a new fluid identification factor（F）.Using the relative variations,Ag/w and Ao/w,of the elastic parameters between gas and water saturated samples and between oil and water saturated samples,λρ,σHSFIF,Kρ,λρ-2μρ,and F as quantitative indicators,we evaluate the sensitivity of the different fluid identification factors to identify reservoir fluids and validate the effects by crossplots.These confirm that the new fluid identification factor（F） is more sensitive for distinguishing oil and water than the traditional method and is more favorable for fliud identification in low to medium porosity and permeability reservoirs.展开更多
文摘The gas-bearing reservoir in X area is mainly the tight sandstone reservoir characterized by low porosity and permeability, frequently lateral variation and poor connectivity of single sand. The previous research results reveal that the general seismic attributes analysis cannot meet the requirement of fluid identification. This is because the relationship between seismic attributes and their implication is uncertain and ambiguous, which decreases the precision of both reservoir prediction and fluid identification. To overcome the problem, multi-attribute crossplot technology is proposed from the mathematical statistical point of view rather than the correspondence between the seismic attributes and their geological implication. In this method, the wells which have the same statistical law are classified firstly, and then all the interest wells are retained while the wells beyond the statistical law are eliminated, and the seismic attributes sensitive to the same types of eliminated wells are optimized and used to generate crossplots. The nonzero area of their crossplots results just predicts the potential distribution. The discontinuity of subsurface geological conditions results in the non-continuous shape and the seismic bin lead to the mosaic form. The optimization of sensitive attributes relative to the same types of wells is independent from each other, and thus the order of attributes in crossplots does not affect the final prediction results. This method is based on the statistical theory and suitable for the areas such as the study area abundant of lots of well data. Application to X area proves the effectiveness of this method and predicts plane distribution about different types of gas production. Due to the effect of faults and other geological factors, the partition prediction results using multi-attribute crossplots reach 95% of coincidence which is obviously and far higher than the results of the whole area. The final prediction results show that the potential areas with medium and high gas production are mainly concentrated in the northern part of the study area, where lots of development research will be strengthened.
基金supported by the the Key Project of Chinese Ministry of Education (Grant No.109035)the National Natural Science Foundation Key Project (Grant No.40830423)Key Projects of Students Extra-curricular Science and Technology Research Program of Schlumberger (Grant No.SLBX0908)
文摘In this article,based on the acoustic measurements of core samples obtained from the low to medium porosity and permeability reservoirs in the WXS Depression,the densities and P and S wave velocities of these core samples were obtained.Then based on these data,a series of elastic parameters were computed.From the basic theory and previous pore fluid research results,we derived a new fluid identification factor（F）.Using the relative variations,Ag/w and Ao/w,of the elastic parameters between gas and water saturated samples and between oil and water saturated samples,λρ,σHSFIF,Kρ,λρ-2μρ,and F as quantitative indicators,we evaluate the sensitivity of the different fluid identification factors to identify reservoir fluids and validate the effects by crossplots.These confirm that the new fluid identification factor（F） is more sensitive for distinguishing oil and water than the traditional method and is more favorable for fliud identification in low to medium porosity and permeability reservoirs.
