The efficient development and operation of offshore oil and gas fields rely heavily on the availability of comprehensive and high-quality geological and technological data. However, due to various limitations such as technical constraints, environmental conditions, data acquisition challenges, and economic factors, significant data gaps frequently arise. These missing data points can hinder accurate reservoir modeling, production forecasting, and decision-making in offshore field operations. Therefore, the reconstruction and completion of incomplete geological and technological data play a crucial role in ensuring the reliability of field development strategies. This study investigates different methodologies for filling in missing data in offshore oil and gas fields, including interpolation techniques, statistical modeling, and machine learning approaches. Traditional methods such as kriging, inverse distance weighting, and regression analysis are compared with modern artificial intelligence-based techniques, including neural networks and deep learning algorithms. The advantages and limitations of each method are evaluated based on data accuracy, computational efficiency, and applicability to various offshore conditions. Moreover, this research emphasizes the importance of selecting appropriate data completion methods depending on the specific geological characteristics of the field, the extent of data gaps, and the available dataset. Case studies from real offshore field operations are analyzed to illustrate how different approaches perform in practice and their impact on reservoir characterization and production optimization. The findings of this study provide valuable insights for improving offshore field data management, enhancing predictive modeling capabilities, and optimizing production strategies. By implementing advanced data completion techniques, operators can mitigate uncertainties, improve reservoir performance assessments, and ensure more efficient exploitation of offshore oil and gas resources. Without a complete and accurate dataset, evaluating the productivity and development potential of offshore oil and gas fields becomes increasingly challenging. Data gaps can significantly impact the construction of geological models and hinder the optimization of production processes. Therefore, the reconstruction of missing data is not merely a technical task but a critical component of strategic decision-making. The integration of data completion techniques into reservoir engineering workflows can lead to more robust simulations and planning scenarios. Ultimately, bridging data gaps supports safer, more costeffective, and environmentally responsible offshore field development.