Enhanced production surveillance using probabilistic dynamic models

Ashutosh Tewari, Stijn de Waele, and Niranjan Subrahmanya
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_18_019.pdf1.03 MBMay 6, 2018 - 6:00pm

Production surveillance is the task of monitoring oil and gas
production from every well in a hydrocarbon field. A key opportunity
in this domain is to improve the accuracy of flow
measurements per phase (oil, water, gas) from a multi-phase
flow. Multi-phase flow sensors are costly and therefore not instrumented
for every production well. Instead, several low fidelity
surrogate measurements are performed that capture different
aspects of the flow. These measurements are then reconciled
to obtain per-phase rate estimates. Current practices
may not appropriately account for the production dynamics
and the sensor issues, thus, fall far short in terms of achieving
a desired surveillance accuracy. To improve surveillance accuracy,
we pose rate reconciliation as a state estimation problem.
We begin with hypothesizing a model that describes the
dynamics of production rates and their relationship with the
field measurements. The model appropriately accounts for
the uncertainties in field conditions and measurements. We
then develop robust probabilistic estimators for reconciliation
to yield the production estimates and the uncertainties therein.
We highlight recent advancements in the area of probabilistic
programming that can go a long way in improving the performance
and the portability of such estimators. The exposition
of our methods is accompanied by experiments in a simulation
environment to illustrate improved surveillance accuracy
achieved in different production scenarios.

Publication Year: 
2018
Publication Volume: 
9
Publication Control Number: 
019
Page Count: 
12
Submission Keywords: 
production surveillance
probabilistic rate reconciliation
probabilistic programming
Submission Topic Areas: 
Industrial applications
Uncertainty Quantification and Management in PHM
Submitted by: 
  
 
 
 

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