A Quantitative Approach to Characterizing Natural Fractures in Tight Gas Sands, Using Data from Mud Loss

Abstract

Abstract Natural fractures have a significant impact on the development of tight sandstone gas reservoirs. However, over the years, limited types of data have been used in characterizing fractures, leading to generally unsatisfactory prediction accuracy. This paper presents a quantitative prediction approach using data from mud loss during drilling, taking the J Gas Field in the Ordos Basin, China, as a case study. The target zone is the H1 member of the Upper Paleozoic, considered a set of superimposed braided-river sand bodies. In this geological setting, horizontal wells have been the preferred choice for exploiting Zone H1. The approach comprises the following steps: First, fracture development characteristics were obtained by studying core samples, image logs, lithology, bed thickness, and seismic attributes. Second, the importance of data from mud loss on fracture prediction was discussed, and the relationship between fractures and faults was analyzed. Third, log responses after core calibration were collected at mud loss points, and potential fractures were identified through well logging. Finally, a 3D model of fracture intensity was created to provide a quantitative characterization of fracture distribution, and the prediction accuracy was verified. The key findings are as follows: (1) Mud loss data can help in locating subsurface clusters of fractures, as mud loss is inherently linked to fracture dimensions. Therefore, mud loss can serve as a valuable index for describing fractures. (2) Major faults play a crucial role in controlling the distribution of fractures, with the orientation of faults also influencing this distribution. For instance, East-West faults have a more pronounced impact. (3) The occurrence of mud loss has a dual nature. On one hand, moderate mud loss can be an indicator of promising gas production. On the other hand, uncontrolled mud loss can result in severe formation damage and unexpected drilling termination. (4) The constructed fracture intensity model offers a quantitative prediction of fracture distribution, and the result demonstrates a satisfactory prediction accuracy. What sets this paper apart is the introduction of a quantitative methodology for predicting natural fractures, making full use of often neglected mud loss data.