Distributed acoustic sensor (DAS) is a promising technology for real-time monitoring of wellbores and other infrastructures. However, the desired signals are often overwhelmed by background and environmental noise inherent in field applications. We present a suite of computationally inexpensive techniques for the real-time extraction of gas signatures from noisy DAS data acquired in a 5163-ft-deep wellbore. The techniques are implemented on three well-scale DAS datasets, each representing multiphase flow conditions with different gas injection volumes, fluid circulation rates, and injection methods. The proposed denoising techniques not only helped in optimizing the gas slug signature despite the high background noise, but also reduced the DAS data size without compromising the signal quality.

No abstract

Riser gas events during offshore drilling operations are hazardous and challenging to control. Therefore, knowledge of the gas influx sizes and distributions in a marine drilling riser is critical for the selection of riser gas handling methods and the estimation of risks of riser gas unloading. An Extended-Kalman-Filter-based data assimilation method is developed and evaluated for the real-time estimation of gas influx rates and void fraction distributions in a riser during riser gas handling. Full-scale experiments were conducted in this study for the evaluation of the proposed data assimilation method. An offshore well, which consists of a marine drilling riser and a wellbore below the subsea blowout preventer, was simulated by a 1,572-meter-deep experimental well. Real-time measurement data, including surface and downhole pressures, pump rates, and liquid outflow rates, were used to estimate the downhole gas influx rates using the Kalman Filter. An online calibrated Drift Flux Model based on data assimilation is used to estimate the distributions of void fractions in the riser over time. The measurement data from a gas flowmeter and the Distributed Fiber Optic Sensing were used to validate the estimation results, and satisfying performance was seen from the presented method. This study proposed a novel data assimilation-based state estimation method by maximizing the use of measurement data of different types from the available managed pressure drilling systems. This method enables the more accurate estimation and prediction of gas behaviors in a riser and helps to facilitate real-time decision-making during riser gas handling.

Distributed acoustic sensor (DAS) is a promising technology for real-time monitoring of wellbores and other infrastructures. However, the desired signals are often overwhelmed by background and environmental noise inherent in field applications. We present a suite of computationally inexpensive techniques for the real-time extraction of gas signatures from noisy DAS data acquired in a 5163-ft-deep wellbore. The techniques are implemented on three well-scale DAS datasets, each representing multiphase flow conditions with different gas injection volumes, fluid circulation rates, and injection methods. The proposed denoising techniques not only helped in optimizing the gas slug signature despite the high background noise but also reduced the DAS data size without compromising the signal quality.

Distributed acoustic sensor (DAS) is a promising technology for real-time monitoring of wellbores and other infrastructures. However, the desired signals are often overwhelmed by background and environmental noise inherent in field applications. We present a suite of computationally inexpensive techniques for the real-time extraction of gas signatures from noisy DAS data acquired in a 5163-ft-deep wellbore. The techniques are implemented on three well-scale DAS datasets, each representing multiphase flow conditions with different gas injection volumes, fluid circulation rates, and injection methods. The proposed denoising techniques not only helped in optimizing the gas slug signature despite the high background noise but also reduced the DAS data size without compromising the signal quality.