Research on AI-Assisted Dynamic Correction of Historical Exploration Data and Precise Oil and Gas Field Development Based on Closed-Loop Optimization

The reliability of historical exploration data is crucial for oil and gas field development and resource management decisions. However, factors such as instrumentation, calibration, or human errors often compromise data accuracy and completeness. Traditional data quality control relying on manual editing and seismic processing suffers from low efficiency, subjectivity, and time-consuming limitations in handling complex multidimensional data. This study develops a novel AI-assisted closed-loop optimisation framework for bidirectional calibration of historical exploration data. By integrating data correction, reservoir simulation, and economic assessment in a dynamic iterative workflow, the framework achieves global management of data quality. Expert domain knowledge and heuristic rules are fused to enhance the geological and physical consistency of restored datasets.