Gas Injection EOR Optimization Using Fiber-Optic Logging with DTS and DAS for Remedial Work
Bibliographic record
Abstract
Abstract The evaluation of in-flow performance for all stages in pumping, multi-stage fractured horizontal (MSFHW) oil wells is challenging with conventional production logging tools. The successful deployment of fiber-optic sensing technology has been influential in diagnosing gas breakthrough in producing wells of a gas injection EOR project by providing real-time, full well in-flow profiling. The integrated operation of a multi-disciplined team and the outcomes will be described. An operator commenced a tertiary dry gas injection enhanced oil recovery (EOR) in the Bakken Formation of southeast Saskatchewan, Canada. The pilot project was designed as a 1:8 toe-heel injection pattern, with a one mile horizontal in-fill well supporting eight perpendicular producers. The offset producers are monitored using regular production data and gas breakthrough has been found in certain fracture stages that are in communication with the injector. A program was developed to use real-time fiber-optic DTS and DAS to identify the stages with gas channeling in the producer for corrective action. Distributed Temperature Sensing (DTS) utilizes the Joule-Thompson (J-T) cooling principle as the gas enters the frac port and expands into the liner, while Distributed Acoustic Sensing (DAS) captured the amplitude and frequency of acoustics from the fluid flow. Simultaneous measurements of DTS and DAS were taken using a hybrid fiber/electric wireline inside coiled tubing. The artificial pumping system was pulled out of the producer and coiled tubing was run to the bottom of the well. Real-time pressure and temperature sensors were run on the end-of the coil and a memory gauge at the bottom of the swab string. The well was stabilized overnight before swabbing and monitoring. A service rig was used to swab the well and establish a draw-down. Live down-hole DTS, DAS and pressure data were logged and monitored at the surface and remotely. Post-processed data interpretation from three wells in the EOR project will be discussed that shows DTS and DAS logging is a valuable technique in detecting injected gas channeling. The results were effective in making decisions about shutting off the problem stages. DTS was most effective since gas has a higher J-T coefficient compared to oil and water. The DAS data supplemented and confirmed the DTS data. Having real time downhole pressure measurements allowed for regulating the swabbing rates to obtain the desired drawdown pressure. Gas injection EOR projects can be optimized using fiber-optic sensing while swabbing. It provides a new and effective tool in identifying gas breakthrough for remedial work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".