Integrated Prediction of Shale Oil Reservoir Using Pre-Stack Algorithms for Brittleness and Fracture Detection

Abstract

Abstract Shale oil reservoir characterization is conducted in Western Depression of Liaohe Oilfieid of China. The major storage space is fracture which is preferably developed in shale with high content of silica, calcite or dolomite. A workflow of integrating rock physics models, amplitude preserved processing, and pre-stack inversions is developed and implemented which provides elastic parameters such as Young's modulus, Poisson's ratio and fracture density and orientation. These parameters are used to characterize brittleness of shale and preferable drilling locations for such kind of reservoir. In order to accurately estimate velocity in shale, a better rock physics model called AS-Xu-White is integrated in the workflow especially for guidance of interpretation, which is modified from Xu-white model. Fluid discrimination in fracture with newly developed anisotropic rock physics model is also employed in this work. Introduction Shale constitutes about 75% of sedimentary rocks (Vernik and Liu, 1996). It is not only the major source and seal, but also serves as reservoirs. A large amount of shale gas/oil has been discovered since last century. The rapid development of exploiting techniques increases the unconventional shale gas/oil output greatly, which also lead to growing proportion in hydrocarbon yield. Thus, shale reservoirs exploration has drawn more and more attention. In this paper shale oil reservoir characterization is conducted in Western Depression of Liaohe Oilfieid of China. The hydrocarbon-rich shale in Sha3 member of Shahejie formation in Chenjia sag is selected for the case study (the location is shown in Figure 1). The dark shale in Sha3 member is deposited in semideep-deep lacustrine sedimentary environment, which is very thick and has high ability to generate hydrocarbon with abundant organic matter. The major applications of 3D seismic data in the study of shale reservoir focus on the following three aspects:identifying the faults and karst to direct drilling (Norton et al, 2011);predicting natural fractures indirectly by geometric attributes such as coherence and curvature calculated from post-stack data (Guo et al, 2010), and directly by anisotropic inversion of AVAZ and VVAZ (Ruger, 1998; Goodway et al, 2007);mapping geo-mechanical brittleness and horizontal stress directions for effective hydraulic fracture stimulation (Goodway et al, 2010; Sena et al, 2011). In this paper the shale oil in the target member is not from tight rocks but mainly from natural fractures. Thus the study should focus on identifying fracture-prone zones. An integrated and comprehensive workflow for shale oil reservoir prediction is implemented which provides elastic parameters such as Young's modulus, Poisson's ratio and fracture density and orientation. Before pre-stack inversion, a modified Xu-white model is introduced to simulate the complex fracture system of shale, which leads to better estimate velocity. Since brittleness and fractures are two major factors that control reservoir quality, reservoir prediction could be accessed by identifying fracture-prone brittle areas and detecting the natural fractures, where the isotropic and anisotropic inversion are both used. As a result, the brittle shale distribution determined by crossploting Erho (i.e. a parameter constructed by Young's modulus and density) and Poisson's ratio is in a good agreement with both the well production data and the latest drilling data. On the other hand, fractures predicted by improved anisotropic inversion using limited azimuthal data coincide well with that from the FMI, and are proven to be reasonable from geologic analysis. Fluid discrimination in fracture with newly developed anisotropic rock physics model is also employed in this work. Combining the results with geological analysis, the favorable area is proposed finally.