Oil & Gas Geology ›› 2020, Vol. 41 ›› Issue (6): 1299-1309.doi: 10.11743/ogg20200618

• Methods and Technologies • Previous Articles     Next Articles

Prediction of fractures in tight gas reservoirs based on likelihood attribute —A case study of the 2nd member of Xujiahe Formation in Xinchang area, Western Sichuan Depression, Sichuan Basin

Meng Li(), Xiaofei Shang, Huawei Zhao, Shuang Wu, Taizhong Duan   

  1. Petroleum Exploration and Production Research Institute, SINOPEC, Beijing 100083, China
  • Received:2019-09-10 Online:2020-12-28 Published:2020-12-09

Abstract:

Traditional fracture prediction methods based on seismic attributes fail to meet the demand for high-precision prediction of fractures of different scales in the exploration and development of tight sandstone gas reservoirs.By introducing the likelihood attribute into the identification of fractures in tight sandstone gas reservoirs in the Xujiahe Formation, Sichuan Basin, a seismic prediction method for fractures based on likelihood and derived attributes is established.The Otsu threshold segmentation method and constraints from image log interpretation are also employed to classify the reservoir space into fault zones, fracture zones and underdeveloped fracture zones and to perform a fine predication of fractures in the second member of Xujiahe Formation (Xu 2 member) in Xinchang area.Results show that the thinned likelihood attribute can reveal the most possible fracturing locations and probabilities.The fracture density reveals the fracturing intensity, which can be used to predict oil/gas production of a certain location.The fracture prediction based on likelihood attribute has effectively improved the seismic prediction accuracy of tight gas reservoirs in the Xu 2 member in the Xinchang area, and is of referential value to the prediction of other fractured reservoirs.

Key words: likelihood attribute, threshold segmentation, high gas production zone, fracture prediction, tight gas reservoir, Xujiahe Formation, Xinchang area, West Sichuan Depression

CLC Number: