Content area

Abstract

To address the challenges of data fragmentation, inconsistent standards, and weak interactivity in oil and gas field surface engineering, this study proposes an intelligent delivery system integrated with three-dimensional dynamic modeling. Utilizing a layered collaborative framework, the system combines optimization algorithms and anomaly detection methods during data processing to enhance the relevance and reliability of high-dimensional data. The model construction adopts a structured data architecture and dynamic governance strategies, supporting multi-project secure collaboration and full lifecycle data management. At the application level, it integrates three-dimensional visualization and semantic parsing capabilities to achieve interactive display and intelligent analysis of cross-modal data. Validated through practical engineering cases, the platform enables real-time linkage of equipment parameters, documentation, and three-dimensional models, significantly improving data integration efficiency and decision-making capabilities. This advancement drives the transformation of oil and gas field engineering toward intelligent and knowledge-driven practices.

Details

1009240
Business indexing term
Title
Multi-Source Heterogeneous Data-Driven Digital Delivery System for Oil and Gas Surface Engineering
Author
Zhang, Wei 1 ; Dai Zhixiang 1 ; Xia Taiwu 1 ; Chen Gangping 1 ; Zhang, Yihua 2 ; Zhou, Jun 3 ; Liu, Cui 3 

 Natural Gas Gathering and Transmission Engineering Technology Research Institute, PetroChina Southwest Oil and Gas Field Company, Chengdu 610041, China; [email protected] (Z.D.); [email protected] (T.X.); [email protected] (G.C.) 
 Infrastructure Construction Engineering Department, PetroChina Southwest Oil and Gas Field Company, Chengdu 610031, China; [email protected] 
 Petroleum Engineering School, Southwest Petroleum University, Chengdu 610500, China; [email protected] 
Publication title
Systems; Basel
Volume
13
Issue
6
First page
447
Number of pages
17
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20798954
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-06-06
Milestone dates
2025-04-22 (Received); 2025-06-04 (Accepted)
Publication history
 
 
   First posting date
06 Jun 2025
ProQuest document ID
3223941801
Document URL
https://www.proquest.com/scholarly-journals/multi-source-heterogeneous-data-driven-digital/docview/3223941801/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-06-25
Database
ProQuest One Academic