Well integrity is paramount for the safe, environmentally responsible, and economically viable operation of wells throughout their lifecycle, encompassing conventional oil and gas production, unconventional resource extraction (e.g., shale gas and tight oil), and geological storage applications (CO₂, H₂, and natural gas). This review presents a comprehensive synthesis of well integrity challenges, failure mechanisms, monitoring technologies, and management strategies across these operational domains. Key integrity threats—including cement sheath degradation (chemical attack, debonding, cracking, microannuli), casing failures (corrosion, collapse, burst, buckling, fatigue, wear, and connection damage), sustained casing pressure (SCP), and wellhead leaks—are examined in detail. Unique challenges posed by hydraulic fracturing in unconventional wells and emerging risks in CO₂ and hydrogen storage, such as corrosion, carbonation, embrittlement, hydrogen-induced cracking (HIC), and microbial degradation, are also highlighted. The review further explores the evolution of integrity standards (NORSOK, API, ISO), the implementation of Well Integrity Management Systems (WIMS), and the integration of advanced monitoring technologies such as fiber optics, logging tools, and real-time pressure sensing. Particular emphasis is placed on the role of digital technologies—including artificial intelligence, machine learning, and digital twin systems—in enabling predictive maintenance, early failure detection, and lifecycle risk management. The novelty of this review lies in its integrated, cross-domain perspective and its emphasis on digital twin applications for continuous, adaptive well integrity surveillance. It identifies critical knowledge gaps in modeling, materials qualification, and data integration—especially in the context of long-term CO₂ and H₂ storage—and advocates for a proactive, digitally enabled approach to lifecycle well integrity.

Ensuring long-term wellbore integrity is critical for CO₂ injection and storage operations. Conventional cement degrades in CO₂-rich environments, compromising zonal isolation and increasing leakage risks. This study presents a novel self-healing cement formulation incorporating Barite, Pozzolan, and Chalcedony, optimized using a Design of Experiment (DOE) approach. Geochemical simulations were conducted using PHREEQC and Python to evaluate porosity evolution, mineral stability, and self-sealing efficiency under CO₂ exposure. The results demonstrate that the optimized formulations significantly reduce porosity (within 7–14 days) through the formation of calcium silicate hydrate (C-S-H) gels, enhancing crack sealing and mechanical resilience. Saturation index and phase volume analyses confirm the long-term stability of ECSH2 and Calcite, reinforcing the cement matrix. Compared to conventional cement, the self-healing formulations exhibit improved durability, lower permeability, and superior resistance to CO₂-induced degradation. These findings support the use of self-healing cement in carbon capture and storage (CCS), geothermal energy, and deep-well applications, offering a cost-effective and durable solution for long-term wellbore integrity. However, further experimental validation and field-scale evaluation are needed to confirm the practical performance of these formulations under real-world reservoir conditions.

Orphaned and abandoned wells in the United States pose significant environmental risks, including methane emissions, groundwater contamination, and ecosystem degradation. These wells also threaten the integrity of carbon capture and storage (CCS) projects by providing potential leakage pathways for stored CO₂, particularly if they lack proper plugging and well integrity. Although the exact number of orphaned and abandoned wells is uncertain due to poor historical documentation, recent estimates suggest there could be as many as 3.9 million such wells nationwide, emitting approximately 3.2 Teragram (Tg) of methane annually. This study investigates the distribution of orphaned and abandoned wells across the United States, presenting new estimates of documented wells and exploring their methane emissions. Through state-level data analysis, the number of documented orphan wells is estimated to be significantly higher than previously reported. A machine learning model, specifically a RandomForestRegressor, was employed to predict the locations of potential orphan wells, enhancing the ability to target monitoring and remediation efforts.