Study of the relationship between fractures and highly productive shale gas zones, Longmaxi Formation, Jiaoshiba area in eastern Sichuan

Shale fractures are an important factor controlling shale gas enrichment and high-productivity zones in the Longmaxi Formation, Jiaoshiba area in eastern Sichuan. Drilling results have, however, shown that the shale fracture density does not have a straightforward correlation with shale gas productivity. Based on logging data, drilling and seismic data, the relationship between shale fracture and shale gas accumulation is investigated by integrating the results of experiments and geophysical methods. The following conclusions have been drawn：（1） Tracer diffusion tests indicate that zones of fracture act as favorable channels for shale gas migration and high-angle fractures promote gas accumulation.（2） Based on the result of azimuthal anisotropy prediction, a fracture system with anisotropy strength values between 1 and 1.15 represents a moderate development of high-angle fractures, which is considered to be favorable for shale gas accumulation and high productivity, while fracture systems with anisotropy strength values larger than 1.15 indicate over-development of shale fracture, which may result in the destruction of the shale reservoir preservation conditions.

Construction of a novel brittleness index equation and analysis of anisotropic brittleness characteristics for unconventional shale formations

The brittleness prediction of shale formations is of interest to researchers nowadays.Conventional methods of brittleness prediction are usually based on isotropic models while shale is anisotropic.In order to obtain a better prediction of shale brittleness,our study firstly proposed a novel brittleness index equation based on the Voigt–Reuss–Hill average,which combines two classical isotropic methods.The proposed method introduces upper and lower brittleness bounds,which take the uncertainty of brittleness prediction into consideration.In addition,this method can give us acceptable predictions by using limited input values.Secondly,an anisotropic rock physics model was constructed.Two parameters were introduced into our model,which can be used to simulate the lamination of clay minerals and the dip angle of formation.In addition,rock physics templates have been built to analyze the sensitivity of brittleness parameters.Finally,the effects of kerogen,pore structure,clay lamination and shale formation dip have been investigated in terms of anisotropy.The prediction shows that the vertical/horizontal Young’s modulus is always below one while the vertical/horizontal Poisson’s ratio(PR)can be either greater or less than 1.Our study finds different degrees of shale lamination may be the explanation for the random distribution of Vani(the ratio of vertical PR to horizontal PR).

Fracture prediction approach for oil-bearing reservoirs based on AVAZ attributes in an orthorhombic medium

Fracture systems in nature are complicated. Normally vertical fractures develop in an isotropic background. However, the presence of horizontal fine layering or horizontal fractures in reservoirs makes the vertical fractures develop in a VTI（a transversely isotropic media with a vertical symmetry axis） background. In this case, reservoirs can be described better by using an orthorhombic medium instead of a traditional HTI（a transversely isotropic media with a horizontal symmetry axis） medium. In this paper, we focus on the fracture prediction study within an orthorhombic medium for oil-bearing reservoirs. Firstly, we simplify the reflection coefficient approximation in an orthorhombic medium. Secondly, the impact of horizontal fracturing on the reflection coefficient approximation is analyzed theoretically. Then based on that approximation, we compare and analyze the relative impact of vertical fracturing, horizontal fracturing and fluid indicative factor on traditional ellipse fitting results and the scaled B attributes. We find that scaled B attributes are more sensitive to vertical fractures, so scaled B attributes are proposed to predict vertical fractures. Finally, a test is developed to predict the fracture development intensity of an oil-bearing reservoir. The fracture development observed in cores is used to validate the study method. The findings of both theoretical analyses and practical application reveal that compared with traditional methods, this new approach has improved the prediction of fracture development intensity in oil-bearing reservoirs.

Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China

For a typical marine shale reservoir in the Jiaoshiba area, Sichuan Basin of China, P-impedance is sensitive for identifying lithology but not suitable for indicating good shale reservoirs. In comparison, density is an important quantity, which is sensitive for identifying the organic-rich mud shale from non-organic-rich mud shale. Due to the poor data quality and incidence angle range, density cannot be easily inverted by directly solving the ill-posed pre-stack seismic inversion in this area. Meanwhile, the traditional density regularizations implemented by directly using the more robust P-impedance inversion tend to be inaccurate for recovering density for this shale reservoir. In this paper, we combine the P-impedance and the minus uranium to construct the pseudo-P-impedance（PIp） at well locations. The PIp is observed to be sensitive for identifying organic-rich mud shale and has a good correlation with density in this area. We employ the PIp–density relation into the pre-stack inversion framework to estimate density. Three types of regularization are tested on both numerical and field data： These are no regularization, traditional regularization and the proposed approach. It is observed that the proposed method is better for recovering the density of organic-rich mud shale in the Jiaoshiba area.

Intelligent prediction and integral analysis of shale oil and gas sweet spots

Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.