AI-Assisted Digitization of Handwritten Drilling Reports with Human-in-the-Loop Validation

Daily Drilling Reports (DDRs) have long been recorded manually by rig personnel, containing vital operational details such as hourly information of the type of operation, along with critical information on depth progression, mud properties, bit performance, and field observations. However, these handwritten reports are typically archived as scanned documents, making data extraction labor-intensive and error prone. Variations in handwriting, inconsistent layouts, use of abbreviations, and lack of standardized formats further hinder reliable digitization using conventional Optical Character Recognition (OCR) methods.

This study presents an AI-assisted solution to automate the digitization and data extraction from handwritten DDRs. The approach employs a custom document understanding model trained to recognize report-specific patterns, capture tabular relationships, and interpret handwritten fields with high accuracy. A human-in-the-loop review mechanism is incorporated to validate and refine the outputs extracted by the model, ensuring data quality and trustworthiness.

The proposed workflow enables the transformation of historical, unstructured drilling records into searchable, machine-readable datasets. This not only reduces manual transcription efforts but also establishes a scalable digitalization framework for knowledge management and analytics across multiple rigs and wells.