A Novel Approach to Enriching Well Master Data, Leveraging a Composable Modular Services Architecture Underpinned by AI/ML and the OSDU Data Platform
Authors: Wong Lee Hin , Ghani Normanisah Bt Mat , Shahibullah Anis Shahida , Hartono Sugiarto , Ping Lyu , Maxime M. , Prashanth P. 
Published: February 2025

Abstract
PETRONAS Upstream wellbore data, encompassing more than 150,000 documents from over 7,000 wellbores, represents a valuable asset for operational decision-making. The volume and complexity of this data, with its various formats and templates of well reports—including end-of-well reports, final well reports, well completion reports, and other documents from operators and partners—create significant challenges for efficient data management. Traditional manual processes for data extraction, classification, and loading, which are often prone to human error, no longer meet operational and regulatory requirements. In response, Upstream data management has launched an initiative to modernize its wellbore master data management by leveraging advanced technologies.
This paper describes a novel approach to enriching Well Master Data, leveraging a composable modular services architecture underpinned by Artificial Intelligence (AI) / Machine Learning (ML) and the OSDU Data Platform. The approach was conceived with the goal of improving Well Master Data quality and its objective, problem statement, solution architecture, challenges and impact on sustaining Upstream data quality are outlined in this paper.
Master data, or golden records such as Well Master Data, are managed and governed through well-defined, highly-controlled processes supported by the OSDU data platform. These records serve as the single source of truth across Upstream operations. This robust management ensures consistency, accuracy, and quality across all Upstream business systems. Well Master Data enables functions such as exploration, development, production, decarbonization, regulatory and other supporting Upstream activities to utilize, share, and contextualize well-related information based on the same curated dataset, avoiding discrepancies.
Ongoing reconciliation and maintenance of Well Master Data continue to uncover gaps and opportunities to further improve its quality. Low data quality scores for Well Master Data highlight inconsistencies that can lead to incorrect decisions, missed opportunities, inefficiencies, and increased costs related to data sharing, integration, and interoperability. As part of its continuous effort to sustain and enhance data quality, Upstream Data has launched an initiative to significantly enrich Well Master Data through a step-change approach. The solution conceived from the initiative includes a powerful data enrichment process that utilizes cutting-edge technologies, such as advanced Fact Extraction services, Artificial Intelligence (AI), Machine Learning (ML), and modern Optical Character Recognition (OCR), supported by a modular microservices architecture and leveraging the comprehensive data management capabilities (WKS and WKE) of the OSDU data platform.
The solution has enabled automated wellbore information extraction from multiple well reports available as an OSDU Kind record. Well information is reliably extracted through using the modern Optical Character Recognition technology. Appropriate machine Learning (ML) models were developed, trained, and underwent a series of validation and testing iterations to identify and extract 19 critical wellbore master data attributes, including: CountryBlockRegionFieldWellbore NameOperatorPhaseElevationElevation PointReference DatumReference PointSpud DateStart DateCompletion DateTVDDFTVDSSTotal DepthTrajectory ShapeWater Depth
The above well summary attributes were sourced from a variety of document types that were generated from well campaigns. The report types include: Final Well ReportEnd of Well ReportWell Completion ReportPost Drilling Report
The output of the Machine Learning (ML) model execution is ingested into OSDU raw kinds, which are then used to enrich the Well Golden Record via the standard OSDU Well Known Schema (WKS) and Well Known Entity (WKE) services. This automated workflow runs daily, processing any new documents ingested into the OSDU Data Platform, thereby contributing to sustainability efforts to ensure the integrity of upstream data operations. The Machine Learning model has been successfully tested and has generated results for more than 5,000 wellbores by processing over 150,000 documents, extracting approximately 50,000 master well data attributes, and improving data quality completeness with around 4,500 new data attributes.
These innovative Master Well Data Enrichment processes have improved the process cycle time (PCE) of manual data entry through machine-driven automation, eliminating mundane and error-prone manual tasks while enhancing data quality. This allows the business to make better and faster decisions. A key characteristic of this solution is the ease and speed with which these processes were modeled, built, and deployed, made possible by a modular services architecture—a core design philosophy of the solution. The modular services architecture is implemented through the Dataiku Data Science system, within which the Master Well Data Enrichment processes are encapsulated and deployed. In summary, these processes are as follows: Data Preparation and Exploration: The process discovers well reports from OSDU Document Kind, OCR storage, and wellbore WKE using elastic-search and document classification metadata. The exploratory screening analyzes report contents, annotates and labels relevant data, and generates statistical distributions and rankings of the relevant content.Machine Learning Model: The model was developed through a train, validate, and test process. As a rule of thumb, 80% of well report samples with sufficient variation were added to the corpus for training, 10% for testing, and 10% for validation. A validation algorithm was established to compare the extracted data with the source data and Well-Known Entity (WKE) data.Golden Record: The next process involves ingesting the extracted data into OSDU Raw Kind, followed by its maturation into the OSDU Wellbore Well-Known Schema (WKS) before being merged into the Well-Known Entity (WKE) as a golden record.
All these processes are scripted to automatically detect new well reports and extract Master Well Data as soon as a new report is ingested. Another key advantage of the modular services architecture is its ability to support process deployment at scale. This means that as new data enrichment approaches are developed, they can be seamlessly translated into executable processes.
The details of the scope, methodology, technology used, challenges encountered, and results of this novel approach will be explained in the subsequent sections.