Optimization of Data Insight Tool Based on Engineering Technology Data Governance Project in Ultra-deep Oil & Gas Fields

Based on the engineering technology data in the ultra-deep oil & gas fields, this paper utilizes data insight tool to identify and extract information from various types of data stored in documents with text or tables, which meets the needs of data governance project. If the document information is about text content, the natural language processing (NLP) method is directly selected for recognition; If the document information is a table, it is necessary to convert the table into a heterogeneous data table with Date-Frame format first by Python language, and then recognize and extract it. These two processing methods can successfully convert unstructured data to structured data, solving the problem of low accuracy and low timeliness of extracting information from different documents. The NumPy & Pandas learning with Python language and other algorithms/functions play an important role in building metadata models, labeling fields, and training backend algorithms of data insight tool structure. The target trained extraction model is very crucial to the identification and extraction of various information. Relying on this and later, the qualified data generated after steps of extraction of target documents, selection of matching data for review and multi-level audit evaluation will be marked with “EDG”, which is the main data source of various professional databases of Tari Oilfield and the guarantee of the capacity and quality of the data lake. Examples show that the data insight tool has strong adaptability, obvious optimization effects, and superior performance compared to other extraction tools. The development and application of data insight tool have significantly improved the identification and extraction ability for engineering data of ultra-deep oil & gas fields, improved the identification accuracy and extraction speed, and met the needs of data governance.