Jianbo Liao

Jianbo Liao


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
14 February 2024 in Processes
Verified
Journal Article
Open access
PDF file
ZZzhang zhaohui
zhang zhaohui
JLJianbo Liao
Jianbo Liao
ZLZhiyong Li
Zhiyong Li
Tight sandstone oil reservoirs in the Upper Triassic Yanchang Formation of the Ordos Basin are the most promising exploration and development fields owing to their huge production potential. Even though they have received considerable attention in recent years, common productivity prediction methods were not well applied during pre-development owing to their strong internal heterogeneity. In this study, the factors influencing oil production of the Chang 9 Member in the Jiyuan area were investigated and summarized based on drill cores, such as sediment characteristic analysis, lithofacies analysis, other analytical tests, and conventional logging curves. The findings show that fine-sandstone reservoirs with smooth sand body architectures are the main types of tight sandstone commercial oil reservoirs. Furthermore, having high porosity and oil content are prerequisites for commercial oil reservoirs, and the cumulative thickness of effective reservoirs serves as a crucial resource base for them. Through the analysis of logging curves, the relative center of gravity, deviation root of variance, petrophysical index, effective reservoir thickness, and evaluation indicator were used to predict daily oil production, thereby establishing the identification criteria for the ranking of tight sandstone oil reservoirs using logging. Then, the productivity of each reservoir in a single well was predicted by processing the log data from each well using the proposed method, and the productivity prediction results agreed well with the tested results of the perforated interval. This approach highlights the integrated influence of geological factors, physical properties, and the thickness scale of an effective reservoir, thereby improving the predictive capacity of logging curves. Additionally, the proposed methods significantly reduce the need for reservoir parameters better than previous ones, streamline operations, and improve practical applications.