Bridging the Air Gap: A Comprehensive Workflow for Integrating Drone Data Into the Digital Oilfield (DOF)
エアギャップを埋める：ドローンデータをデジタル油田（DOF）に統合する包括的ワークフロー (AI 翻訳)
A. Hassan, Sami Alnuaim
🤖 gxceed AI 要約
日本語
この論文は、ドローンによって収集された多様なデータをデジタル油田インフラに統合するための標準化されたワークフローを提案する。5段階のプロセス（データ取得、前処理、管理・統合、分析、アプリケーション）を定義し、WITSMLやOSDUなどの業界標準とNIST準拠のサイバーセキュリティ対策を組み合わせている。ADNOCでのメタン検出ケーススタディを通じて、環境コンプライアンスとESGへの貢献を示す。
English
This paper proposes a standardized workflow for integrating diverse drone-collected data into Digital Oilfield (DOF) infrastructures. A five-stage process (data acquisition, pre-processing, management & integration, analytics, application enablement) is defined, combining industry standards like WITSML and OSDU with NIST-aligned cybersecurity. A case study from ADNOC on methane detection demonstrates scalability and ESG impact.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の石油・ガス業界でもデジタル油田化が進む中、ドローンデータ統合の標準化は重要。特にSSBJ対応やGHG排出量報告の観点から、メタン漏洩検出の自動化は実務上有用。ただし、本論文の事例はADNOC（UAE）であり、日本の規制環境との直接的な連携は限られる。
In the global GX context
Globally, this workflow addresses the integration gap for drone data in oil and gas digitalization, aligning with industry standards (WITSML, OSDU) and cybersecurity best practices. The methane LDAR case study demonstrates how drone-to-DOF integration can enhance environmental compliance and support ESG reporting, relevant for companies facing methane regulations (e.g., EU, US).
👥 読者別の含意
🔬研究者:Provides a structured framework for integrating drone data into DOF, useful for researchers in digital oilfield and environmental monitoring.
🏢実務担当者:Offers a vendor-agnostic, standards-aligned workflow that can be adopted by oil and gas companies to improve operational efficiency and environmental compliance.
🏛政策担当者:Illustrates how technology can enable methane emissions monitoring, potentially informing regulatory frameworks for emissions reporting.
📄 Abstract（原文）
This paper addresses a critical industry gap: the absence of a standardized, comprehensive framework for integrating diverse drone-collected data into Digital Oilfield (DOF) infrastructures. A detailed, end-to-end workflow is presented to transform raw data from unmanned aerial vehicles (UAVs) into actionable intelligence across onshore and offshore operations while maintaining alignment with industry standards and cybersecurity requirements. The proposed five-stage workflow covering data acquisition, pre-processing, data management and integration, analytics, and application enablement, combines established industry data standards such as WITSML, PRODML, and OSDU with modern drone technologies, cloud platforms, and advanced analytics. Each stage outlines technical specifications, best practices, and NIST-aligned cybersecurity measures to ensure reliability and data integrity across the enterprise. The framework mitigates integration risk through standardized data mapping and quality assurance, supports real-time fusion of multi-modal sensors (visual, thermal, LiDAR, and gas), and enables advanced DOF applications including predictive integrity, anomaly detection, and environmental compliance (methane LDAR). Key integration challenges ranging from cybersecurity and interoperability to data governance are addressed with best-in-class, standards-aligned solutions, enabling safe, secure, and efficient drone-to-DOF operations. Finally, A practical case from ADNOC, integrating drone-based methane detection with DOF workflows, demonstrates the approach's scalability and ESG impact. The result is a vendor-agnostic, secure, and operationally proven pathway for safe, cost-effective, and sustainability-driven digital oilfield operations.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.2523/iptc-25169-msfirst seen 2026-07-16 06:40:26
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