Evaluation method of rock mechanical parameters and brittleness characteristics based on rock cuttings fractal theory

Abstract

In the process of oil and gas exploration and development, it is fundamental to obtain formation information in time to optimize fracturing construction. To quickly evaluate the rock mechanical parameters, drillability grades and the brittleness index of the formation, this paper introduces the rock cuttings fractal theory. Among them, the rock mechanical parameters include compressive strength, static Young’s modulus, static Poisson’s ratio, cohesion, and internal friction coefficient. In this work, the rock fragment distribution model based on the mass-frequency relationship of the rock was established. Then the rock mechanical parameters and the drillability grades were obtained through uniaxial and triaxial compression tests and drillability measurements. The brittleness index was calculated using Young’s modulus and Poisson’s ratio. After testing, the cores were standardized and screened, and the fractal dimensions of rock cuttings were obtained using the rock fragment distribution model. Based on the above research, the rock mechanical parameters were correlated with the fractal dimension of cuttings and the in-situ logging data, respectively. This resulted in the establishment of the logging interpretation model and the fractal model of the rock cuttings mechanical properties in the study area. The models were implemented on the MZ402 well for the purposes of comparison and validation. The results demonstrate that the margin of error is within 12.37%, and that the error meets the engineering standards. Then, taking the Fengcheng Formation of Ma125 well as an example, the real time evaluation method of the rock mechanical parameters and the brittleness index based on cutting fractal theory while drilling was developed. The fractal dimension and the brittleness index of the rock cuttings were employed as samples to determine the k of 3 using elbow method, and the reservoir engineering sweet spot was divided into three types. Subsequently, the K-means clustering method was employed for the purpose of dividing the engineering sweet spot range. This method facilitates the precise identification of the reservoir engineering sweet spot and provides a foundation for the optimization of the fracturing construction.