AI Driven Image Based Digital Twin Rock Properties – Fast, Consistent, and Cost Effective
Authors:   Saudi Aramco, Alsulami Ghadeer , Ma Shouxiang Mark ,   Saudi Aramco, Cox Katrina ,   Core Laboratories, Britton Allen ,   Core Laboratories
Published: November 2024

Rock properties derived from laboratory core analysis have been used as references for formation evaluation and integrated reservoir studies for decades. Some of the challenges in obtaining core analysis data are that it is expensive to acquire core samples, it is time consuming to perform laboratory measurements, results may not be consistent from one laboratory to another, and it can be costly if many analyses are required. The main objective of this study is to test a new innovative method of extracting probability-based analog rock properties, the digital twin, from high-resolution images (HRI) of thin sections by taking advantage of the power of artificial intelligence (AI). The method is based on a proprietary AI technology, which is comprised of two components. The first is the creation of a database that consists of conventionally measured rock properties of multiple rock types, each of which has been subjected to a thorough quality control program and associated HRI of thin sections. Such a database forms the foundation of this technology. The second component of the process is the development of an image recognition AI model comprised of the HRI of thin sections of the core analysis samples from part one. Once created and validated, such an AI model can be used to analyze, in instances where cores are unavailable, HRI sourced from alternative formation-representative samples such as oddly shaped rotary sidewall cores or even drill cuttings. In these situations, the HRI of thin sections is prepared and compared, through the AI model, to images in the database. If a match is obtained, the rock properties associated with the matched image in the database, which can be thought of as its pore geometry similar digital twin, would be retrieved and serve as probability-based analog data for that sample. This unique combination of core database and image recognition is at the core of this analytical process. The above methodology was applied to a new database of core analysis data and HRI of thin sections, and a customized AI model was developed, tested, and verified with satisfactory results. In addition, we are pushing the envelope of further development of this technology by extending its application to the analysis of drill cuttings and evaluating the effect of cutting sizes on the performance of the AI model using data generated from core plugs. Twenty sets of synthetic cuttings in 5, 4, and 2 mm size fractions created from conventional core plugs, which had previously been used in the creation of the AI model, were prepared. A thin section of each cutting size fraction was scanned, and each scan was then submitted to the AI model for analysis, and analogue matches were identified. Preliminary results demonstrate the potential of applying this technology to estimate rock properties as part of well-mud logging. An innovative method has the potential to expand the capabilities of mud logging to estimate rock petrophysical properties from thin section image analysis in near-real time while drilling.