Well-scale multiphase flow characterization and validation using distributed fiber-optic sensors for gas kick monitoring

Sharma, Jyotsna; Santos, Otto; Feo, Giuseppe; Ogunsanwo, Oloruntoba; Williams, Wesley C.

doi:10.1364/oe.404981

Cited by 26 publications

(8 citation statements)

References 8 publications

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“…The time instance when the pressure difference between these gauges first starts to drop ( t 1 ) indicates the gas–water interface first crossing that gauge location. Eventually, the pressure difference plateaus (at time t 2 ), which indicates that the gas–water mixture region has completely crossed the gauge at 3502 ft and is now between gauges at 2023 and 3205 ft. To estimate the height of the gas–water mixture region, we take the time difference ( t 2 minus t 1 ) and multiply that with the average gas rise velocity estimated experimentally and validated using numerical simulations by Sharma et al 24 for this test as 0.5 ft/s. This gives an average column height of 772 ft, which is in good agreement with the estimate using the VBE workflow.…”

Section: Applications Of Vbe Workflowmentioning

confidence: 99%

“…The DAS and DTS data were analyzed using a variety of signal processing techniques, including, frequency band energy (FBE) extraction, frequency-wavenumber (or F-K) transform, energy spectrums, and gradient plots. Some of these analysis and results pertaining to gas velocity calculation, flow rate measurement, and pressure estimation have been published in our earlier publications 21 – 24 . However, none of them included the estimation of gas void fraction and interface tracking, which is the focus of the current study.…”

Section: Introductionmentioning

confidence: 99%

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A novel velocity band energy workflow for fiber-optic DAS interpretation and multiphase flow characterization

Ekechukwu,

Sharma,

William

2023

Sci Rep

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Distributed fiber-optic sensing continues to gain widespread adoption in the energy industry because of the numerous benefits it offers for real-time surface and subsurface monitoring of pipelines, wellbores, reservoirs, and storage infrastructure. In this study, we introduce a novel workflow to analyze optical fiber-based distributed acoustic sensor (DAS) data, which takes into account the speed of sound for a certain phase to filter the acoustic energy or signal contributed by that phase. This information is then utilized for the characterization of multiphase flow. The application of the proposed velocity band energy (VBE) workflow is demonstrated using a dataset acquired in a 5163-ft-deep wellbore, for estimating gas void fraction and real-time gas–liquid interface tracking across the length of the well. The workflow utilizes a series of signal processing and conditioning steps that aim to reduce noise and enhance the signals of interest. The insights from the new methodology will further assist in validating DAS-based flow monitoring algorithms, leak detection and quantification, and reservoir characterization.

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Section: Applications Of Vbe Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel velocity band energy workflow for fiber-optic DAS interpretation and multiphase flow characterization

Ekechukwu,

Sharma,

William

2023

Sci Rep

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“…2 b). The objective of the experiments was to observe and characterize the gas rise in water using the fiber-optic sensors and downhole gauges as described in detail in the references 32 , 33 .…”

Section: Data Acquisitionmentioning

confidence: 99%

“…S1 and S2). A detailed interpretation of the gas signature can be found in the references 33 . Since the LFDAS data are sensitive to both dynamic strain and temperature changes, Band-LF (0 to 2 Hz) has both positive and negative numbers depending on whether the fiber section is experiencing compressive or tensile strain or heating or cooling phenomenon 32 .…”

Section: Data Acquisitionmentioning

confidence: 99%

Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS

Ekechukwu¹,

Sharma²

2021

Sci Rep

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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.

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“…They are lightweight, chemically passive, immune to electromagnetic influence, and do not require any electronics along the optical path [3]. However, owing to their high sensitivity, DAS signals are often overwhelmed by environmental noise inherent to the dynamic and harsh downhole conditions in a wellbore [4]. Thus, signal processing and denoising techniques are necessary for extracting the signatures of interest from the background noise.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction techniques for noisy distributed acoustic sensor data acquired in a wellbore

Tabjula¹,

Sharma²

2023

Appl. Opt.

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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.

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Well-scale multiphase flow characterization and validation using distributed fiber-optic sensors for gas kick monitoring

Cited by 26 publications

References 8 publications

A novel velocity band energy workflow for fiber-optic DAS interpretation and multiphase flow characterization

A novel velocity band energy workflow for fiber-optic DAS interpretation and multiphase flow characterization

Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS

Feature extraction techniques for noisy distributed acoustic sensor data acquired in a wellbore

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