Abstract
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.
Highlights
The attribute crossplots analysis is commonly used to predict the reservoir and identify the fluid by analyzing the sensitive seismic attributes anomaly caused by reservoir for fluid via forward modeling
Chao Wang (2015) [2] proposed two new seismic attribute technologies including anisotropy coherence technique (ACT) and fracture intensity inversion (FII) to provide an effective way to predict the distribution of DPF in similar geological settings
We classify the wells having the same statistical laws from the mathematical statistical point of view rather than the corresponding relations between the seismic attributes and their geological implication, and predict the interest fluid according to gas-bearing properties of known wells included in the same statistical class
Summary
The attribute crossplots analysis is commonly used to predict the reservoir and identify the fluid by analyzing the sensitive seismic attributes anomaly caused by reservoir for fluid via forward modeling. Different researchers of different times study either more seismic attributes sensitive to reservoir and fluid or advanced methods for computing crossplots to improve the reliability of attribute crossplots analysis in reservoir prediction and fluid identification. Michelena (2011) [1] proposed facies probabilities from multidimensional crossplots of seismic attributes and helped to improve sand identification where sands were more anisotropic than the background in the application of tight gas. Chao Wang (2015) [2] proposed two new seismic attribute technologies including anisotropy coherence technique (ACT) and fracture intensity inversion (FII) to provide an effective way to predict the distribution of DPF in similar geological settings. We classify the wells having the same statistical laws from the mathematical statistical point of view rather than the corresponding relations between the seismic attributes and their geological implication, and predict the interest fluid according to gas-bearing properties of known wells included in the same statistical class
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