Oil and Gas Field Digital Twin Full Lifecycle Management Platform and Application Based on Meta-Universe Technology
Authors: Zhang Y. , Song Z. , Lu X. , Chen B. , Zhang Q. , Su G. 
Published: November 2024

Abstract
Oil and gas fields are often characterized by wide distribution of facilities and equipment, harsh natural environments, difficulties in engineering construction and production operation and maintenance management, and increasingly severe risks of violence and terrorism. There is an urgent need to build a unified management platform covering the engineering construction, production and operation phases, and to carry out unified control over the whole life cycle process of oilfields. This paper takes meta-universe technology as the core to construct a virtual reality environment for oil and gas field production and office facilities. It integrates the static data in the engineering construction process, such as engineering design, procurement, construction, commissioning, and startup, with the dynamic data and business processes in the production and operation process, such as Internet of Things (IOT) production, sales and procurement, and comprehensive office, to build a digital twin full life cycle management platform for oil and gas fields, and applies it to an oil field in West Africa. Aiming at the problem of poor network transmission conditions, this paper researches the adaptive dimensionality reduction algorithm for digital twin high-dimensional animation data, improved Forward Error Correction coding (FEC) and Automatic Retransmission Request (ARQ) algorithms, and adaptive transmission algorithms based on active sampling of the "protagonist aura" model. This ensures stable operation of the twin platform and its applications in restricted and highly unstable network environments. In terms of equipment dynamic fault identification and predictive maintenance, this paper adopts high-fidelity modeling and multi-case simulation technology to realize the modeling and simulation of reciprocating compressors, and establishes a fault diagnosis model based on machine learning and deep learning, which is capable of accurately identifying and classifying different types of faults and providing early warning, with a fault identification accuracy rate of up to 95% and an early warning accuracy rate of up to 90%. In general, the working mode of the oilfield is transformed from "labor-intensive, stationed on duty, regular inspection" to "fewer or unmanned, centralized monitoring, on-demand inspection". The management mode changes from"manual judgment + experience analysis" to "automatic warning + intelligent analysis". The abnormality handling mode is transformed from "real-time alarm and post-disposal" to "pre-warning and mid-control". Through the construction of the platform, it improves the company's data governance capability, optimizes the operation and management process, improves the production operation efficiency, directly reduces the labor cost and operation cost by about 37%, improves the efficiency of production operation fault disposal to 75%, reduces the number of production emergencies by more than 30%, and realizes the full life-cycle management of the oilfield.