An unexpected and unwanted influx of gas or “kick” into the wellbore during hydrocarbon drilling can cause catastrophic blowout incidents, resulting in human casualties, ecological damage, and asset losses. The ability of the oil and gas industry to control gas kick depends on our ability to accurately detect and monitor gas migration in a borehole in real-time. This study demonstrates the application of optical fiber-based Distributed Acoustic Sensors (DAS) for early detection and monitoring of gas in wellbore. Multiphase flow experiments conducted in a 5000 ft. deep test-well are analyzed for different injection, circulation, and pressure conditions. In each case, the low-frequency component of DAS demonstrates a superior capability to detect gas signatures both inside the tubing and the annulus of the well, even at small gas volumes. In comparison, the high-frequency DAS data seems limited in detail. The gas influx velocity was calculated using the frequency-wavenumber analysis of the gradient of the low-frequency DAS phase with respect to time, which shows good agreement with theoretical velocity estimates using flow models and surface gauge measurements. This study demonstrates a novel workflow to analyze low-frequency DAS to qualitatively and quantitatively map gas influx in a wellbore.

Small leaks, many of which often go undetected using conventional gauges, remain an urgent problem for an aging pipeline infrastructure. This paper proposes a method that allows for an automated and robust leak detection and localization system by fusing Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) data for machine learning. Distributed fiber-optic sensing creates an advantage over conventional gauges by providing real-time continuous measurements along the entire length of the fiber-instrumented pipeline at a high spatiotemporal resolution and sensitivity that can detect small leaks, as well as the leak location. High sensitivity, however, can create noisy data. Thus, machine learning is applied for a robust method of distinguishing non-leak data from leak signatures for accurate leak detection and localization. The workflow is demonstrated on an experimental pipeline setup exposed to environmental noise. The results illustrate reliable detection of small leaks between 0.04 L/s to 0.30 L/s with F1 scores over 0.9, on a range of DAS frequency bands combined with DTS data. The fusion of two different types of distributed fiber-optic sensors not only increases the likelihood of small leaks being detected but decreases the number of false alarms by filtering noise that only appears in one sensor domain. Furthermore, the machine learning approach utilizes segmentation, allowing for precise localization and quick investigation, as well as AI explainability of the sensor data deemed a leak signature.

Effective well control depends on the drilling teams’ knowledge of wellbore flow dynamics and their ability to predict and control influx. Unfortunately, detection of a gas influx in an offshore environment is particularly challenging, and there are no existing datasets that have been verified and validated for gas kick migration at full-scale annular conditions. This study bridges this gap and presents pioneering research in the application of fiber optic sensing for monitoring gas in riser. The proposed sensing paradigm was validated through well-scale experiments conducted at Petroleum Engineering Research & Technology Transfer lab (PERTT) facility at Louisiana State University (LSU), simulating an offshore marine riser environment with its larger than average annular space and mud circulation capability. The experimental setup instrumented with distributed fiber optic sensors and pressure/temperature gauges provides a physical model to study the dynamic gas migration in full-scale annular conditions. Current kick detection methods primarily utilize surface measurements and do not always reliably detect a gas influx. The proposed application of distributed fiber optic sensing overcomes this key limitation of conventional kick detection methods, by providing real-time distributed downhole data for accurate and reliable monitoring. The two-phase flow experiments conducted in this research provide critical insights for understanding the flow dynamics in offshore drilling riser conditions, and the results provide an indication of how quickly gas can migrate in a marine riser scenario, warranting further investigation for the sake of effective well control.

In this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. A machine learning workflow was developed and demonstrated using experimental datasets from gas–water flow tests conducted in a 5163-ft deep well instrumented with DAS, DTS, and four downhole pressure gauges. The workflow is successfully demonstrated on two experimental datasets, corresponding to different gas injection volumes, backpressure, injection methods, and water circulation rates. The workflow utilizes the random forest algorithm and involves a two-step process for distributed pressure prediction. In the first step, single-depth predictive modeling is performed to explore the underlying relationship between the DAS (in seven different frequency bands), DTS, and the gauge pressures at the four downhole locations. The single-depth analysis showed that the low-frequency components (< 2 Hz) of the DAS data, when combined with DTS, consistently demonstrate a superior capability in predicting pressure as compared to the higher frequency bands for both the datasets achieving an average coefficient of determination (or R²) of 0.96. This can be explained by the unique characteristic of low-frequency DAS which is sensitive to both the strain and temperature perturbations. In the second step, the DTS and the low-frequency DAS data from two gauge locations were used to predict pressures at different depths. The distributed pressure modeling achieved an average R² of 0.95 and an average root mean squared error (RMSE) of 24 psi for the two datasets across the depths analyzed, demonstrating the distributed pressure measurement capability using the proposed workflow. A majority of the current DAS applications rely on the higher frequency components. This study presents a novel application of the low-frequency DAS combined with DTS for distributed pressure measurement.