Application of Machine-Learning to Construct Simulation Models from High-Resolution Fractured Formation

Ryan Santoso, Xupeng He, Hussein Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations


In modeling fractured reservoirs, outcrops may offer useful insights about the subsurface characterization of the heterogeneous rock formation. They provide analogs that could be replicated in the reservoir to capture the fracture and matrix characteristics, which are crucial to assess the governing recovery mechanisms. Constructing outcrop-based reservoir models is a labor-intensive process, which is subject to personal interpretation and error. In this work, we propose a novel workflow for modeling fractured reservoirs within a deep learning framework. The workflow consists of three main steps that include fracture network recognition to map explicitly the fractures from digital images, fracture characterization to provide an assessment of the fracture effective hydraulic apertures, and reservoir model construction to integrate the multi-scale data and construct the up-scaled simulation model. In this paper, we focus on the first step in the workflow. The fracture network recognition starts with segmentation for the images of the fractured formation. The ultimate objective is to identify the fractures from RGB, greyscale, or hyperspectral images. We developed a U-Net-based algorithm to perform the segmentation using 64×64 pixel-resolution. This resolution is carefully selected to accelerate the fracture recognition process and to narrow down the variability in the training set. The inputs are images of the fractured medium with any resolution which are pre-processed before feeding it to the recognition process. The output is a list of the identified fractures, where each fracture is composed of a set of segments, and each segment is defined by the coordinates of its end-points. The output format could be readily processed by any fracture modeling software. We demonstrate our workflow to recognize and identify fractures from different 2D images, where we discuss the machine-learning (ML) training and testing stages. The algorithm shows accurate predictions and identifications for the fractures. This workflow has the potential to be extended and applied at the field scale.
Original languageEnglish (US)
Title of host publicationAbu Dhabi International Petroleum Exhibition & Conference
PublisherSociety of Petroleum Engineers
ISBN (Print)9781613996720
StatePublished - Nov 8 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank King Abdullah University of Science and Technology (KAUST) for funding and support with MATLAB academic licenses.


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