Danilo Colombo

Digital Twin technologies have seen growing adoption in the Oil and Gas industry, yet the term is applied to systems with markedly different levels of data integration, analytical capability, physical–digital interaction, and operational autonomy. Existing classifications distinguish broad categories such as Digital Models, Digital Shadows, and Digital Twins but provide limited guidance for the Oil and Gas context, as they do not explicitly differentiate behavioral representation, analytical sophistication, asset-to-model synchronization, digital-to-physical interaction, and decision-making autonomy. This limitation makes it difficult to compare implementations that share the same Digital Twin label but differ substantially in functional configuration and sophistication. To address this gap, this paper proposes a framework comprising seven capability-based categories: Static Digital Model, Dynamic Digital Model, Analytic Digital Model, Digital Shadow, Analytic Digital Shadow, Digital Twin, and Intelligent Digital Twin. The framework was developed through research synthesis and descriptive analysis and empirically assessed by classifying 92 industry-specific studies using a hybrid procedure that combined Generative AI (G-AI) assessments with researcher review. The results show that many systems labeled as Digital Twins in the reviewed literature are reported at intermediate levels, while incomplete reporting of synchronization, analytics, and decision autonomy contributes to classification ambiguity. The proposed framework clarifies the boundaries among Digital Twin configurations and supports the evaluation of existing applications, the identification of capability gaps, and the development of more integrated and autonomous solutions.

Well P&A constitutes a critical stage within the decommissioning process in oil exploration and production projects, reflecting the industry’s commitment to sustainability, environmental protection, and social responsibility. The correct and timely execution of permanent abandonment is essential to prevent adverse environmental impacts, such as hydrocarbon leaks and contamination of underground aquifers, thereby protecting marine and terrestrial ecosystems. According to the IOGP’s Decommissioning Insights Report 2023, well abandonment campaigns can represent 40% to 60% of the total budget for decommissioning programs. Established economic analysis concepts are applied to estimate the impacts of postponing well abandonment for up to five years relative to the schedule set out in Petrobras’ Strategic Planning (2025–2029). To this end, a comparative analysis of Total Economic Value (TEV) is performed across three scheduling scenarios, considering five cost components (all discounted to present value): (i) direct abandonment costs, (ii) regulatory costs associated with non-compliance with SGIP commitments, (iii) expected monetary value (EMV) of well integrity loss/blowout events, (iv) decommissioning obligations costs (non-COGO), and (v) opportunity costs related to revitalization projects. The methodology is applied to a portfolio of 150 interventions, selected based on the availability of well integrity failure probability functions required to compute EMV. The results indicate that performing abandonment at the appropriate time avoids costs significantly higher than the financial disbursement allocated in the Strategic Plan. In conclusion, beyond being environmentally responsible and a concession obligation, well abandonment is decisive for the sustainability of the oil exploration and production business.