Permanent Gauges are a remarkable source of information of both long term production data and the capture of occasional
buildups that may be described as ‘free well tests’. Data are acquired at high frequency and over a long duration. The down side is the large
number of data points gathered, which can amount to hundreds of millions per sensor which is far beyond the processing capability of today’s
fastest PC. There are a number of challenges: storing and accessing the raw data, filtering, transferring this to the relevant analysis module and
finally sharing both filtered data and analyses.
KAPPA-Server is a client-server solution for Permanent Gauges reservoir surveillance that addresses these issues in a shared environment. It permanently mirrors
raw data from any data historian, reduces the number of points with wavelet-based filtering, stores and shares the filtered data. Filtered data can
be exported to third party databases.
- Reduce Noise & Smart Filter
- Data Synchronization
- Export & Synchronize Data
High frequency data (Permanent Gauges)
Time-lapse data with measures recorded every 1 – 30 second intervals can be considered as high frequency data (pressure, temperature, flowmeter rates) and usually are stored in the company’s data historian. Using a preconfigured data link an user can browse the list of available tags (gauges, data sets) in the historian and select the required ones to be mirrored into KAPPA-Server.
Mirroring involves copying the data tag from the historian into a gauge item in KAPPA-Server. Once this is done the gauge item is kept updated by an internal process that checks for additional data and mirrors them.
Low Frequency Data (Permanent Gauges)
Time-lapse data with measures recorded with intervals greater than 15 minutes can be considered as low frequency data. Reallocated production data are a good example of such data. These data can be mirrored (copied) from production databases into a direct data type item in KAPPA-Server and kept automatically updated.
Unlike high frequency data that require filtering before it can be used, direct data are immediately available for analysis and calculations.
Data from Permanent Gauges are inherently noisy especially if acquired with high frequency. To reduce the noise without losing specific data characteristics required for PTA analysis KAPPA server applies the wavelets algorithm to process the raw gauge data.
Smart filtering reduces the volume of raw data by a processing called decimation. Sections of data exhibiting gradual changes are represented by fewer points while breakpoints or areas with fast changing trends are represented with a greater number of points. Using this approach an overall reduction can be up to 2-3 orders of magnitude.
Once the raw data have undergone smart filtering they can easily be transferred into KW modules for analysis
Checking filter parameters
It is important to verify that the chosen set of filtering parameters (wavelet denoising and smart filtering) are not altering the data to the extent that subsequent analysis are affected. Filtering quality control can be performed by outputting the actual raw data and using it as a benchmark on a log-log derivative plot in Saphir. The filter threshold values plot should also be checked regularly to ensure that the initially set noise threshold values are adequate to the current noise levels of raw data.
Users can select sections of data previously filtered and set different filtering criteria for them. This is especially helpful in cases with very noisy data that requires coarse filtering on production periods and fine filter settings on areas of PTA interest.
Build-up periods can be identified on pressure and temperature data. Identification can be by the user manually or automatically in the KAPPA Client user interface. Additionally, shut-in detection can also be run in a fully automated mode on the KAPPA-Server. For shut-in detection, only the pressure or the temperature data sets are used.
Using the shut-in detection information rates data can be automatically corrected and aligned with the pressure data. Rate correction algorithm can also account for the well uptime.
The quality of results from the automatic shut-in detection algorithm greatly depend on the data quality and sometimes the detection does not give accurate results. This may be due to the inherent noise in the data or the data shows a soft shut-in. As a best practice it is always recommended to visually inspect the results of the auto shut-in identification.
Transferring data to KAPPA-Workstation
Filtered data sets, direct data and derived channels can easily be transferred to the KW suite by drag and drop or using the KAPPA Send button.
A user can select a number of earlier identified build-ups and send them to Saphir for PTA analysis with the corrected rates all in one go. On receiving the data sets Saphir automatically plots the log-log derivatives of the selected build-ups.
Synchronising new data
Pressure and rate data that were earlier sent to KW can easily be updated. The user just needs to click on the update button from KW and the relevant new pressure and rate will be pulled from KAPPA-Server and appended to the existing set.
KS API is an interface provided by KAPPA-Server to allow 3rd party programs to access data stored in KAPPA-Server. Filtered data sets, data from derived channels as well as analysis results from KAPPA Wokstation documents (Saphir, Topaze) can be accessed via this interface.
The interface is an integration enabling tool for 3rd party programmers and vendors to develop code within their applications to communicate with KS API in order to send requests for and receive data from KAPPA-Server.
KS API supports SOAP and REST standards.
Derived channels & alarms
These are user defined and permit mathematical operations on data channels with a comprehensive formula package. The outcome may be another data set or a boolean vs time that may be used to create an alarm.
The purpose of the alarm is to display it in the KAPPA Client window, or the sending of an alarm E-mail.
Fully Automated Shut-in Detection
Shut-in detection is now fully automated and can be configured to run 24x7 on the KAPPA-Server. A user simply creates a Shut-in user task, specifies the shut-in pressure and duration criteria and KAPPA-Server runs a dedicated process that applies the detection algorithm on incoming pressure data. Once relevant shut-ins are identified, the user is notified by email with a link that can be used to quickly navigate to the relevant shut-in information.
Field objects repository
Field PVTs, relative permeability data as well as simple field map contours can be stored in KAPPA-Server field hierarchy. These objects can be stored at the field level or at a wells group level.
When a user requires to use any of these objects in KW analysis he simply drags and drops the relevant object onto the KW analysis document. This approach facilitates information share and effective reuse.