Engineered Materials, Machine Learning Enhance Carbon Storage Well Integrity

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214844, “Engineered Materials and Machine Learning for Carbon Storage Well Integrity,” by Jacob Pollock, Oceanit Laboratories, and Hani Elshahawi, SPE, NoviDigiTech. The paper has not been peer reviewed. _ The success of CO2 injection operations, and the endurance of long-term storage, are partially dependent on well design and the materials used for well construction. The authors describe a stack of technologies they have developed that enable enhanced well robustness, monitoring of carbon-storage facilities, and modeling of their performance. The combination of these technologies enables the tracking of downhole and subsurface fluid distribution and flow. Furthermore, the scalable technologies can be used to exploit remaining reservoirs while preventing CO2-plume migration. Introduction Besides site selection and geological features that influence plume migration, well-construction design and materials have a major effect on successful storage. Injection wells are drilled into the storage site and completed with multiple cemented casing strings, with access to the reservoir through perforations or gravel packs. Cement supports and protects the metal casings from corrosive fluids while isolating the storage reservoir from freshwater aquifers by sealing off the wellbore annulus (Fig. 1). Cement integrity and durability are critical to successful carbon capture, storage, and use (CCUS) operations. Despite their importance, current cement-evaluation methods suffer from limited resolution, poor discrimination, and ambiguous interpretation. The authors have developed steel/cement interfacial bonding nanotechnology that enhances both mechanical bonding and acoustic coupling, along with acoustic metamaterials for sensing cement and tracking particles. The acoustic band gap of the materials was demonstrated to improve acoustic contrast and provide information about the cement environment based on frequency and amplitude. Furthermore, data-processing and machine-learning methods have been developed to interpret subsurface readings with more accuracy and deeper insight. Neural networks can be used to interpret the results of smart material detection and sensing, providing new information that was previously unattainable. Engineered Materials for Well Construction New classes of construction components with enhanced properties may be self-healing or passively sensing or may have outstanding physical properties such as mechanical strength. Nanoscale treatments can alter the surface characteristics of materials, endowing them with engineered interfacial behavior. Passive-sensing materials can alter incoming energy waves that can be detected to discern information about material condition and environment. These technologies can improve the properties of well-construction materials for carbon storage, increasing integrity and allowing monitoring of well and reservoir states. The results of successful laboratory and field trials of each technology described in this section are provided in the complete paper.