Rock thin-section analysis and identification based on artificial intelligent technique

Abstract

Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth. It is also an important evaluation method for oil and gas exploration and development. It can be used to identify the petrological characteristics of reservoirs, determine the type of diagenesis, and distinguish the characteristics of reservoir space and pore structure. It is necessary to understand the physical properties and sedimentary environment of the reservoir, obtain the relevant parameters of the reservoir, formulate the oil and gas development plan, and reserve calculation. The traditional thin-section identification method has a history of more than one hundred years, which mainly depends on the geological experts' visual observation with the optical microscope, and is bothered by the problems of strong subjectivity, high dependence on experience, heavy workload, long identification cycle, and incapability to achieve complete and accurate quantification. In this paper, the models of particle segmentation, mineralogy identification, and pore type intelligent identification are constructed by using deep learning, computer vision, and other technologies, and the intelligent thin-section identification is realized. This paper overcomes the problem of multi-target recognition in the image sequence, constructs a fine-grained classification network under the multi-mode and multi-light source, and proposes a modeling scheme of data annotation while building models, forming a scientific, quantitative and efficient slice identification method. The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency, but also realizes the intuitive, accurate and quantitative identification results, which is a subversive innovation and change to the traditional thin-section identification practice.