Abstract
China's oil and natural gas imports are increasing year by year. In recent years, China's dependence on foreign oil has reached more than 70%. For the safety of national energy reserves, China needs to increase efforts to explore unconventional resources such as shale gas. More than 80 horizontal wells are drilled every year in the YX Shale Gas Field. The difference in logging responses between vertical and horizontal wells brings great uncertainty to reservoir evaluation.
This study is based on triple-combo logs, elements, dipole acoustic waves, electrical imaging, nuclear magnetic resonance (NRM) and other data, using core-log calibration to establish a vertical well logging reservoir evaluation model. Due to different measurement methods and measurement environments, the logging data collected in horizontal wells need to be normalized before they can be applied to the reservoir evaluation model established in vertical wells. Due to the influence of many factors, the slowness of acoustic waves logged in horizontal wells is often smaller than that of vertical wells in the same comparable zones. This paper innovatively carried out the study of horizontal well slowness difference based on big data core slowness difference experiment, dipole sonic logging anisotropy, horizontal well trajectory, formation dip and other data, established logging curve normalization model.
This article shows an example of unconventional logging evaluation in the shale gas field in the Sichuan Basin, China. After the verticalization of slowness of horizontal well, the value drops by 5-10us/ft, which is in good contrast with the same comparable zone of the pilot hole, which effectively reduces the uncertainty of the horizontal well reservoir logging evaluation. Observation under the ion-milled backscatter scanning electron microscope (BSE) revealed the nano-level pore structure of the reservoir. The pore types of the reservoir are mainly inorganic pores such as kerogen-associated organic pores, intergranular pores and intragranular pores. The reservoir element, lithology, permeability, organic matter (TOC), gas content, and water saturation calculated by core-log calibration can accurately determine the sweet spot of the reservoir.
Logging curve normalization model and shale gas reservoir logging evaluation model based on anisotropy research can effectively guide the identification of sweet spots in shale gas reservoirs. The reservoir parameters calculated by logging are in good agreement with the core experiment results. This type of logging evaluation model can be applied to other shale gas reservoir evaluations in China.
