Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a
Case Study
- URL: http://arxiv.org/abs/2002.02737v3
- Date: Mon, 26 Oct 2020 08:16:15 GMT
- Title: Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a
Case Study
- Authors: Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
- Abstract summary: This research investigates a hybrid modeling approach, utilizing techniques from both the aforementioned areas of expertise, to model a well production choke.
The choke is represented with a simplified set of first-principle equations and a neural network to estimate the valve flow coefficient.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Virtual flow meters, mathematical models predicting production flow rates in
petroleum assets, are useful aids in production monitoring and optimization.
Mechanistic models based on first-principles are most common, however,
data-driven models exploiting patterns in measurements are gaining popularity.
This research investigates a hybrid modeling approach, utilizing techniques
from both the aforementioned areas of expertise, to model a well production
choke. The choke is represented with a simplified set of first-principle
equations and a neural network to estimate the valve flow coefficient.
Historical production data from the petroleum platform Edvard Grieg is used for
model validation. Additionally, a mechanistic and a data-driven model are
constructed for comparison of performance. A practical framework for
development of models with varying degree of hybridity and stochastic
optimization of its parameters is established. Results of the hybrid model
performance are promising albeit with considerable room for improvements.
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