Subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network

Patent metadata

Field	Value
Publication number	US-11768976-B2
Application number	US-202117453741-A
Country	US
Kind code	B2
Filing date	Nov 5, 2021
Priority date	Jul 13, 2021
Publication date	Sep 26, 2023
Grant date	Sep 26, 2023

Abstract

The present disclosure belongs to the field of petroleum engineering, and specifically relates to a subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network. The subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network includes three steps: digital twin model establishment, degradation process re-prediction model establishment, and remaining useful life calculation model establishment. The subsea Christmas tree re-prediction system integrating Kalman filter and Bayesian network includes a subsea distribution unit information acquisition subsystem mounted on an subsea distribution unit, a subsea control module information acquisition subsystem mounted on a subsea control module, a subsea valve bank information acquisition subsystem mounted on a subsea valve bank, a wellhead mechanical module information acquisition subsystem mounted on a wellhead mechanical module, a subsea environmental information acquisition module mounted on a subsea control module, and a subsea Christmas tree digital twin subsystem mounted in an overwater control station.

First claim

What is claimed is:

1. A subsea Christmas tree re-prediction method integrating Kalman filter and Bayesian network, comprising three steps:
digital twin model establishment,
degradation process re-prediction model establishment, and
remaining useful life calculation model establishment,
wherein the specific steps for digital twin model establishment are as follows:
for a physical structure of a subsea Christmas tree, establishing a digital twin geometric size model, wherein the digital twin geometric size model comprises an electronic structure geometric size model, a hydraulic structure geometric size model, a mechanical structure geometric size model, and the three structure geometric size models reflect a geometric size and an assembly relationship of a physical system;
for a marine environment of the subsea Christmas tree, establishing a digital twin production environment model, wherein the digital twin production environment model comprises real-time dynamic data composed of marine environmental monitoring data comprising typhoon, internal wave current, sea water temperature and pressure;
for a process parameter of the subsea Christmas tree, establishing a digital twin production process model, wherein the digital twin production process model comprises oil and gas production process data comprising conventional oil recovery, chemical injection, and paraffin removal;
for a monitoring parameter of the subsea Christmas tree, establishing a digital twin production state model, wherein the digital twin production state model comprises multi-source sensor system state data of a mechanical structure, a hydraulic structure, and an electronic structure;
the specific steps for degradation process re-prediction model establishment are as follows:
reading system state data of the subsea Christmas tree, calculating degradation amount of each assembly over time, wherein voltage information of the electronic structure, pressure of the hydraulic structure, flow information and stress-strain information of the mechanical structure of a historical process of the subsea Christmas tree are read, and historical degradation amount is determined by using a subsea Christmas tree failure mode;
estimating a Wiener process parameter based on degradation data, wherein for the subsea Christmas tree, a degradation model of each structure conforms to a Wiener process:
X ( t )= X (0)+λ t+σ B B ( t ),

where λ is a drift coefficient, σ B is a diffusion coefficient, and B(t) is a standard Brownian motion, and t is a sampling time and n represents a number of sets of degradation data and i represents a number of monitoring points in each set of degradation data;
for n sets of degradation data, each set of degradation data has i monitoring points, the degradation amount is recorded as X, the time is recorded as T, and the Wiener process parameter is estimated by using a maximum likelihood estimation method:

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Assignees

• Univ China Petroleum East China
• Ocean Univ China
• Univ Beihang

Inventors

• Cai Baoping
• Liu Yonghong
• Shao Xiaoyan
• Liu Guijie
• Feng Qiang
• Wu Qilin
• Ge Weifeng
• Li Xincheng
• Wu Qibing
• Wu Xiaolu
• He Rui
• Zou Zhexian
• Wang Honghui
• Fan Hongyan

Classifications

• G06F30/17 Primary

  Mechanical parametric or variational design · CPC title

• E21B33/035 Primary

  specially adapted for underwater installations (E21B33/043, E21B33/064, E21B33/076 take precedence) · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• E21B2200/20

  Computer models or simulations, e.g. for reservoirs under production, drill bits · CPC title

• G06F2111/08

  Probabilistic or stochastic CAD · CPC title