A Workflow Incorporating an Artificial Neural Network to Predict Subsurface Porosity for CO₂ Storage Geological Site Characterization

• Main Authors: George Koperna, Hunter Jonsson, Richie Ness, Shawna Cyphers, JohnRyan MacGregor
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Processes
• Subjects: Carbon Capture Storage; petrophysics; machine learning
• Online Access: https://www.mdpi.com/2227-9717/8/7/813

The<b> </b>large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The trained neural network was applied to 36 oil wells across the Citronelle Field and used to generate synthetic porosities of the storage reservoir and overlying stratigraphy. Ultimately, permeability of the storage reservoir was estimated using a combination of synthetic porosity and an empirically derived relationship between porosity and permeability determined from core.