Digital Sedimentological Core Description Through Machine Learning
Authors: Mezghani Mokhles , Masrahy Mohammed 
Published: November 2020

Abstract
Describing subsurface geological core is a critical task in the oil and gas industry to understand hydrocarbon reservoirs and define the lateral and vertical variabilities in geomodels. This task is conducted by visually inspecting the core and recording important rock texture characteristics (e.g., grain size, roundness, sorting) and sedimentary structures (e.g., bedding, lamination, type of deformations, bioturbation). Figure 1 shows an example of the results. Due to the high level of detail and the amount of data to be recorded by such a task, a geologist typically spends a considerable amount of time observing and analyzing the core. The results will necessarily carry a certain degree of subjectivity, depending on the geologist's expertise. Moreover, this workflow requires the physical availability of both geologist and core in the same place (commonly in a core laboratory) in order to describe the core when it is needed. Knowing the high cost of the coring process, it will be very prejudicial to ignore such valuable data while building subsurface geological models. This challenge is actually well handeled in our company by leveraging on the digital transformation program that we are currently undertaking. The objective of this work is to develop a workflow to automate the core description task by using machine learning. A large set of core samples will be used to train the model using high-resolution core photos and their associated digital core description. The trained model can be used to predict core description for cores where only high-resolution core photos are available. This workflow is of high impact on our operations. Indeed, it can be applied for legacy core samples where the core photos have been acquired. It can be applied for the fresh core samples too. As soon as the core photography is completed, a real-time core description can be provided to the geologist for his review and approval.
This workflow is validated by predicting core description for cores already described (visual description). Ten wells have been used during this validation study. For these wells, both core photos and digital core description are available. This dataset includes approximately a total of 3,500 feet of core. The testing and validation was done using a blind testing approach, where eight wells have been used to train the model and two wells for prediction. A good agreement between the manual description and automated description is obtained. Even if the manual description is not unique and is impacted by the geologist experience and subjectivity, having this overall agreement is a very positive indicator regarding the robustness of this workflow. In addition to robustness, the automated workflow is offering consistent (repeatable) results without any subjectivity. Finally, the real-time aspect of this workflow is adding a new dimension to the workflow efficiency. Indeed, once the model is trained, few minutes (depending on the well core footage) are needed to generate the full well core description.