Continual Compositionality as a Scalable Ai Framework for Adaptive Oil and Gas Operations

Abstract

Abstract This paper proposes a continual compositionality framework for adaptive artificial intelligence in digital oilfield operations. Conventional AI solutions in the oil and gas industry rely on static, monolithic models that degrade over time due to non-stationary production behavior, evolving reservoir conditions, and operational changes. To address these limitations, the proposed framework enables AI systems to dynamically compose specialized agents and selectively update only relevant components in response to real-time conditions, eliminating the need for computationally expensive full model retraining. The framework is implemented for production instability management using streaming sensor data, where an orchestration layer activates anomaly detection, short-term forecasting, and advisory agents as required. Continual learning is achieved through Elastic Weight Consolidation and Learning without Forgetting, supported by an adaptive feedback loop that balances learning stability and plasticity to prevent model drift. Experimental validation on historical digital oilfield data demonstrates that the proposed approach achieves up to a 97% reduction in model drift compared to conventional retraining and fine-tuning strategies, while improving short-term production forecasting accuracy by 20–30% under instability scenarios. The results show that dynamic agent composition enables low latency anomaly detection, timely corrective recommendations, and improved operational resilience. The proposed framework provides a practical pathway toward autonomous, continuously adaptive digital oilfield systems capable of sustained deployment in dynamic industrial environments.