An innovative method for the characterization of oil content in lacustrine shale-oil systems: A case study from the Middle Permian Lucaogou Formation in the Jimusaer Sag, Junggar Basin

Abstract

Determination of oil content not only affects the exploration and development of potential favorable intervals in lacustrine shale-oil systems but also impacts research on the main factors controlling the differential accumulation of shale oil. Current approaches usually characterize a certain fraction of the hydrocarbons in shale oil. For example, the volatile hydrocarbon (S1) fraction is considered to represent the hydrocarbons (C7–C33) that would be volatilized below 300 °C. In this article, an innovative method called the oil content evaluation index (OCEI) is proposed, in which the shale oil composition is considered and used to evaluate the vertical distribution of oil content utilizing conventional logging. In the OCEI method, the evaluation index (LI*) represents the content of liquid hydrocarbons per unit mass of rock; this index successfully characterizes the discrete experimental data S1 and chloroform extract yield. The evaluation index (CI*) represents the oil-bearing area under the core description. The evaluation index (GI*) characterizes the content of gaseous hydrocarbons in a constant volume. Based on the relationship between these evaluation indexes and the actual production, the relative weight of each evaluation index is determined through the grey relational analysis method. Finally, the OCEI value is the sum of the product of these normalized evaluation indicators and their corresponding weights. In a case study, the method is successfully implemented to predict the pay zones of the Lucaogou Formation in the Jimsar Sag, Junggar Basin, China. The threshold value of OCEI is set to 0.39 based on its relationship with production data. When the OCEI value of a certain interval is greater than 0.39, then this interval is a favorable pay zone. The evaluation results of two wells in the Jimusaer Sag further demonstrate that the model can effectively and reliably predict potential favorable intervals in shale oil systems.