Integrated Image Processing and Computational Techniques to Characterize Formation Damage

Abstract

Filtrate and solid invasion from drilling fluids are two key sources of formation damage, and can result in formation permeability impairment. Typically, spurt invasion of mud solids causes the evolution of an external mud cake which tends to reduce further solids and filtrate influx. However, uncontrolled spurt and filtrate invasion are detrimental because they reduce the permeability of the formation. Mud composition, formation rock's permeability and porosity, and temperature can influence both spurt and filtrate invasion. The sizes of mud solids relative to the average pore size of a rock are also important in predicting the extent of mud invasion and permeability impairment. In this paper, a dynamic modeling approach is presented for mud solids deposition on the pores of rock samples for different lithologies. The modeling results were compared to experimental values. To simulate a close-to-real field mud invasion and damage scenario, rock samples were first subjected to a dynamic-radial fluid loss test under controlled laboratory conditions. The geometry of the simulated drill pipe and inner diameter of the cores allowed for uniform mud cake evolution around the wall of the cores. Three different rock samples (Michigan sandstone, Indiana limestone, and Austin chalk) were investigated. Two water-based mud (WBM) samples were formulated to simulate high and low fluid loss recipes. Next, scanning electron microscopy (SEM) imaging of the dry cores coupled with image processing was used to determine the porosity and pore size distribution of the internal mud cake. The structure of the porous rocks as well as the mud cake were modeled using the bundle of curved tubes approach. In addition, the deposition probability of mud solid particles was considered through filtration theories. Experimental results showed up to 40% reduction in mud invasion and damage to the rocks using the low fluid loss recipe. The model developed in this study closely matched the experimental results. The model revealed a maximum relative error of about 9.6% for one out of the six case studies, and an average relative error of 3.3% for other case studies. The novelty in this study is the quantitative utilization of SEM images by applying watershed segmentation algorithm to detect and measure the size of mud cake pore spaces. This approach can be implemented in the design of drilling fluids that can reduce formation damage.

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