Research on Abnormal Detection and Quality Control Method for Oilfield Logging Data Based on Big Data Analysis

Well logging data represents the core data asset in oilfield exploration and development, with its quality directly impacting the accuracy of reserve estimation, productivity prediction, and development plan formulation. To address current challenges such as large data volumes, heterogeneous formats, decentralized storage, and inefficient anomaly detection in well logging data, this paper proposes an intelligent quality control method based on big data analytics. The research methodology leverages big data technology to construct a multi-source well logging data fusion analysis model, breaking through traditional data silos and enabling structured integration and correlation analysis of geological, engineering, and production data across multiple dimensions. A standardized data architecture system covering the entire chain of acquisition, transmission, and storage is established, with unified management of data assets through a metadata dictionary to form a standardized “acquisition-storage-processing-utilization” data flow. A knowledge graph-driven data lineage tracking mechanism is introduced to establish a full-process closed-loop management system from raw data ingestion to quality assessment [1], realizing dynamic self-optimization of anomaly detection rule libraries. The study highlights that the key to well logging data quality control lies in the integration of standardized data architectures with intelligent analysis models, while big data applications significantly enhance the granularity of data management. This method efficiently identifies anomalous data and substantially improves data quality validation accuracy. The innovation of this research resides in the construction of a standardized well logging data governance system, enabling unified storage and efficient management of multi-source data, along with the development of an intelligent data quality assessment and repair system to form a closed-loop management process. This novel approach provides more reliable data support for oilfield exploration and development, facilitates precise decision-making, drives the digital transformation of well logging data management, enhances data utilization efficiency, and holds significant theoretical value and practical guidance for the intelligent development of oilfields.