• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Universitetet i Stavanger
  • Faculty of Science and Technology
  • Department of Electrical and Computer Engineering (TN-IDE)
  • Studentoppgaver (TN-IDE)
  • View Item
  •   Home
  • Universitetet i Stavanger
  • Faculty of Science and Technology
  • Department of Electrical and Computer Engineering (TN-IDE)
  • Studentoppgaver (TN-IDE)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Signal-noise analysis of PTA data from modern well surveillance system

Ambjørnrud, Even.; Magnus, Wersland.
Master thesis
Thumbnail
URI
https://hdl.handle.net/11250/3010016
Date
2022
Metadata
Show full item record
Collections
  • Studentoppgaver (TN-IDE) [1048]
Description
Full text not available
Abstract
In industries such as oil and gas, geothermal and carbon storage most

wells are equipped with permanent downhole gauges(PDGs). These gauges

are used to monitor the well pressure in real time. The pressure read-

ings are used to interpret and calculate information about the wells and

underground flows governing injection, production and overall process effi-

ciencies.

The process of interpreting the data is labour intensive and requires exten-

sive experience in the field. This thesis focuses on automating parts of the

process to speed up the interpretation, and minimise the labour-intensive

work.

The automation process includes removing outliers, filtering and prepar-

ing the data for a pressure transient analyis (PTA). Real-world raw data

presents some problems, such as the well operations and rate fluctuations,

which can complicate the interpretation.

The paper examines different filtering methods, such as mean filter, low pass

filter, various types of regression, Fourier transformations and wavelets.

The paper also looks at combining different filters to enhance the best

parts from both filters.

Our thesis showed the importance of resampling a signal before processing

it. The best results came from combining a moving average filter with

LOWESS. This combination eliminated almost all noise, while still giving

a fairly good representation of the transients.

The paper tested with a synthetic dataset to calculate a score for how good

each filter performed. This dataset is not a perfect representation of real

world conditions, but the result still concluded that a LOWESS filter is the

best choice.
 
In industries such as oil and gas, geothermal and carbon storage most

wells are equipped with permanent downhole gauges(PDGs). These gauges

are used to monitor the well pressure in real time. The pressure read-

ings are used to interpret and calculate information about the wells and

underground flows governing injection, production and overall process effi-

ciencies.

The process of interpreting the data is labour intensive and requires exten-

sive experience in the field. This thesis focuses on automating parts of the

process to speed up the interpretation, and minimise the labour-intensive

work.

The automation process includes removing outliers, filtering and prepar-

ing the data for a pressure transient analyis (PTA). Real-world raw data

presents some problems, such as the well operations and rate fluctuations,

which can complicate the interpretation.

The paper examines different filtering methods, such as mean filter, low pass

filter, various types of regression, Fourier transformations and wavelets.

The paper also looks at combining different filters to enhance the best

parts from both filters.

Our thesis showed the importance of resampling a signal before processing

it. The best results came from combining a moving average filter with

LOWESS. This combination eliminated almost all noise, while still giving

a fairly good representation of the transients.

The paper tested with a synthetic dataset to calculate a score for how good

each filter performed. This dataset is not a perfect representation of real

world conditions, but the result still concluded that a LOWESS filter is the

best choice.
 
Publisher
uis

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit