Projects

Hand-held data acquisition system delivers downhole data to maximise production and reduce shut-in wells

Access to well data is key for making informed integrity and abandonment decisions. With over 600 wells currently shut-in across the UKCS due to well management issues, this is a key challenge and opportunity for the mature North Sea basin.

SolaSense's hand-held technology harnesses similar technology deployed in the telecoms industry. The hand-held data acquisition system provides a portable, processing software and visualisation interface for delivering near real time interpretation of DAS/DTS data at the rig site. This allows well features to be readily recognised and evaluated, avoiding shut-in wells and minimising lost production.

Phase one of The OGTC's support of the project is to develop two key pieces of distributed fibre optic sensing (DFOS) software:
- DAS data decimator and processor
- Data visualisation and interpretation that will encompass both DTS and DAS information

Industry Value

With 43% of shut-in wells due to integrity issues, applying this technology could improve well surveillance by driving down the cost of fibre operations.

The value of this technology to the UKCS is estimated at >£135 million per year. This is based on three areas of application (source: OGA) covering:
- A 1% reduction in production losses associated with issues that can be detected and address with DFOS (£17 million per annum)
- Re-instatement of 1% of the current ~600 shut-in UKCS wells (£30 million per annum)
- An £88 million annual saving in plug and abandonment intervention costs through improved leak path identification and barrier location selection

Technology Organisation

SolaSense

SOLA Sense provides smart solutions for optimization and monitoring of solar power plants. Our solutions are based on smart embedded sensors, wireless communication, cloud services and data analysis.

ProjectSolaSense - Low cost real time DFOS interpretation system

Challenge

Wells

Vision

Fix Today

Entry TRL

3

Field Trial

Planned

Technology

Low cost real time DFOS interpretation system

Type

Develop

Achieved TRL

8

Technology Organisation

SolaSense

SOLA Sense provides smart solutions for optimization and monitoring of solar power plants. Our solutions are based on smart embedded sensors, wireless communication, cloud services and data analysis.

Key Results

Phase one of the this project has delivered the foundation software that addresses the industry challenge. This software comprises a flexible well data visualisation capability that can incorporate multiple types and formats of well information, which of course includes DFOS data. Importantly, it combines with a new type of DAS decimation, processing and visualisation capability that has been developed as part of the project.

Lessons Learned

Compatibility for integration with broader oilfield data collection and analytical/surveillance products and developments will be a key consideration for this software to ensure as broad a usage as possible. Also, consultations have been made to ensure compatibility of the data decimator and processor product with modern machine learning and pattern recognition techniques, which may form a further development stage of this product in the future.

Following the successful completion of phase one of the project, SolaSense were able to take their technology to a commercial stage without further field trials/development. A further phase may be presented to look at enhancing the softwares capability in delivering data analysis in real time as well as the application of machine learning algorithms.

Next Steps

The next stage will integrate the two software components developed in Phase one and take the integrated product through a series of workshop and field trials. These field trials will test and prove the existing functionality of the software and guide further development to ensure that the end product is aligned with industry needs and bring the product to commercialisation.
A phase two of the project will be developed to investigate a number of enhancements to the DAS processing component that have been identified will be explored, including real time measurement and interpretation which could lead to automated feature/event detection through machine learning.