Over the past 20 years, distributed fiber-optic sensing (DFOS) has become a powerful tool in many applications including those in the oil and gas industry. Subject-matter experts from industry and academia discussed the current status of DFOS technologies and their implementation in flow measurement during a special session at the 2024 SPE Annual Technical Conference and Exhibition in New Orleans.
Panelists
- Mahmoud Farhadiroushan (Silixa)
- Yilin Fan (Colorado School of Mines)
- Mikko Jaaskelainen (Halliburton)
- Jyotsna Sharma (Louisiana State University)
- Haldun Unalmis (Weatherford) (Moderator)
Panel Summary
Haldun Unalmis, Weatherford, was the organizer and moderator of the panel session. He started the session with a brief introduction of downhole flow measurement techniques and how the measurements from DFOS technologies are used to solve single-phase and multiphase flows. He also pointed out some of the outstanding topics in flow measurement using DFOS technologies and future expectations.
Following his introduction, each of the panelists gave 10-minute presentations to discuss their relevant work. In the final stage, the panelists discussed moderator-led questions on 1) DFOS education, both in academia and industry; 2) flow measurement accuracy of DFOS systems and impacting factors; 3) current status of DFOS technologies in real-time monitoring capabilities; 4) challenges in field implementation; and 5) cost reduction of DFOS systems going forward. The panelists also responded to questions from the audience in the Q&A session.
Mahmoud Farhadiroushan, Silixa, gave a short presentation on engineered distributed optical fiber sensing solution for quantitative flow measurement. He described the recent advances in distributed acoustic sensors that utilize a sensing fiber with precision scatter centers to achieve a high sensitivity with a fine-gauge-length down to 25 cm. The new sensing techniques enable a dense array of sensors to be coupled along a pipe section and quantify the fluid composition and velocity by measuring the propagation speed of sound (SoS) and vortex velocity.
He presented some initial flow-loop test results for a two-phase flow (water/air) over an 8 m of a pipe section where the SoS in water dropped from about 1450 m/s to below 200 m/s when a small amount of air was injected into water, and then the SoS returned back to 1450 m/s after the air injection stopped while the vortex velocity remained constant.
The engineered distributed fiber sensor provided direct quantitative flow measurement that would allow auto labelling of the flow characteristics in downhole environments with low latency that can be used for production control and optimization using a physical production model as well as machine learning techniques. The SoS measurement is also of a great interest for direct monitoring and controlling of the dynamic fluid phase in carbon capture, transportation, and sequestration applications.
Yilin Fan, Colorado School of Mines, presented her recent studies on slug flow characterization and monitoring using distributed acoustic sensing (DAS). Her experimental research demonstrated DAS's ability to measure critical slug flow characteristic parameters such as frequency, translational velocity, and length, highlighting its potential for multiphase flow metering in individual wells and/or production allocation.
Her study also showed DAS's effectiveness in monitoring slug flow variations along pipelines, capturing the processes of slug dissipation and initiation over long distances. These insights offer valuable contributions to flow assurance, benefiting predictions and management strategies for issues like corrosion, pipe fatigue, gas hydrate, etc.
Additionally, downhole slug behavior data provided by DAS can help optimize pump schedules, particularly for pumps that are highly impacted by the slugging behaviors. Fan also discussed the cable sensitivity to multiphase flow behavior using various deployment methods.
Multiphase-flow metering and monitoring are highly valuable for production design and optimization, yet significant challenges remain in achieving accurate measurements with DFOS for multiphase flow. These challenges stem from factors like transient multiphase flow behaviors, varying flow patterns, the similar properties of oil and water, and the complex, often unpredictable nature of oil/water flow patterns.
Her research group is actively exploring solutions to address these issues and enhance measurement accuracy.
Mikko Jaaskelainen, Halliburton, presented a data-driven approach to generate a customized application-specific data-driven flow model. Flow regimes and fluid mix may significantly change over the life of a well, so a flow-monitoring system must be able to accurately monitor flow across a wide range of conditions. Completion components like interval control valves (ICVs) may be customized for specific intelligent wells thus resulting in different acoustic flow signatures; isolation packers may be spaced at different reservoir intervals; the location and number of perforations often vary depending on the reservoir conditions and depletion strategy.
The downhole well environment is highly complex with different acoustic characteristics from well to well. The approach to solve this problem is to generate a data set that can be used to build a machine learning model. In many cases, it is possible to measure and control surface flow rates, surface pressure, and downhole ICV settings. This data can be augmented with distributed and point downhole sensors like DAS, distributed temperature sensing (DTS), point pressure (P) and temperature (T) sensors, and injection/production logging runs. Proper design of experiment and execution of a controlled data collection phase will generate a data volume that can be used to build a supervised machine learning model. The process can be repeated over the life of the well as flow conditions change.
A general workflow for the approach was shared including a recent field implementation with real-time edge data collection, data decimation/processing, and real-time flow results.
Jyotsna Sharma, Louisiana State University, gave a speech on advancing the current state of the art of distributed fiber-optic sensing. She presented several advanced fiber- optic technologies currently being developed and demonstrated at Louisiana State University, including a groundbreaking technique that integrates quantum light states with optical fibers for ultrasensitive detection. She also introduced a novel algorithm for distributed pressure estimation, using data from DAS and DTS, which has been successfully demonstrated at well scale.
Another major development was the use of distributed chemical sensing for CO2 monitoring along the entire fiber, achieved by coating the fiber with nanoporous materials. Sharma explained the analytical model created to understand light modulation in the presence of reactive coatings, enabling the optimization of fiber and nanomaterial properties for enhanced chemical detection sensitivity across various operating conditions. In addition, she discussed innovative methods for detecting microleaks with flow rates below 0.1%, achieved by fusing DAS and DTS data.
By using advanced denoising techniques and machine learning, this system overcomes the challenge of detecting small leaks, which often go unnoticed due to low signal-to-noise ratios. The fusion of sensor data with these technologies enables the reliable detection of such leaks, helping to prevent significant damage over time.