Electrical resistance tomography-based multi-modality sensor and drift flux model for measurement of oil–gas–water flow

Rashed, Sarah; Faraj, Yousef; Wang, Mi; Wilkinson, Stephen J.

doi:10.1088/1361-6501/ac74a1

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…The only undetermined variable in the definition of flux equations ( 1) and ( 2) is the average solid velocity in the upper layer v u . Zhang et al 9 used the drift-flux model [35][36][37][38] to calculate the velocity of cuttings in a solid-liquid mixed layer, and the calculated results were in good agreement with the experimental results.…”

Section: Drift-flux Modelmentioning

confidence: 82%

Transient cutting transport model for horizontal wells with a slim hole

Huang

et al. 2022

Energy Science & Engineering

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Compared with conventional drilling methods, the method of drilling horizontal wells with a slim hole (HW‐SH) has advantages, such as high productivity, environment‐friendliness, and low costs. However, during the drilling process, the narrow annulus may become blocked, which may lead to an increase in the annulus pressure if the wellbore is not clean. This often leads to serious drilling accidents, such as sticking and leaking formations. Many models have been used to study the cutting transport law in an annulus, but the influence of drill‐pipe rotation on cutting transport is often ignored and weakened in these models. None of the existing models can effectively simulate the cutting transport law of an entire well section. Therefore, using these models to predict the cutting transport law in the annulus of the HW‐SH will cause significant errors. In this study, the authors first establish a pipeline model of the same size as the on‐site annulus using computational fluid dynamics (CFD). Subsequently, a one‐dimensional (1D) two‐layer model that uses three correction factors to express the influence of drill‐pipe rotation on cutting transport and annulus pressure loss is developed. Finally, the parameters in the 1D model are calibrated using the CFD simulation results to accurately predict the cutting transport behavior and annular pressure loss. From the calculation results of the annulus cutting quality, annulus section‐cutting concentration, and annulus pressure drop, it was observed that the two algorithms proposed in this study have a high degree of coincidence, thereby confirming the reliability of the model. The 1D model provides close to three‐dimensional (3D) accuracy at a much shorter central processing unit time than the 3D CFD models. In addition, the accuracy of the developed model was verified using the results of an indoor pipeline two‐phase flow experiment conducted at the University of Tulsa, without considering the rotation of the drill pipe.

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Section: Drift-flux Modelmentioning

confidence: 82%

Transient cutting transport model for horizontal wells with a slim hole

Huang

et al. 2022

Energy Science & Engineering

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“…Historically, the field of multiphase flow measurement has seen the combination of Electrical Resistance Tomography (ERT) with DFM as a prevalent approach [15], [16]. This method in studies aiming to accurately measure and analyze flows involving multiple phases, such as gas, liquid, and solids.…”

Section: Background a Electrical Resistance Tomography With Drift Flu...mentioning

confidence: 99%

“…In the current study, the DFM is employed to elucidate the interplay between water flow velocity, average gas concentration, and gas flow velocity in gas-water two-phase flow scenarios [16]. This relationship in two-phase gas-water flow is summarized in (3) and (4).…”

Section: Background a Electrical Resistance Tomography With Drift Flu...mentioning

confidence: 99%

Enhancing Accuracy in Gas-Water Two-Phase Flow Sensor Systems Through Deep-Learning-based Computational Framework

Bao,

Wu,

Wang

et al. 2024

Preprint

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Multiphase flow is a critical component in contemporary industrial operations, yet the accurate quantification of multiphase parameters presents a substantial obstacle. This study enhances gas-water two-phase flow measurement accuracy via a deep learning framework, leveraging a multi-sensor array in a lab-simulated dual-layer pipeline. Employing electrical resistance tomography, electromagnetic flow meters, and temperature and pressure sensors, it captures real-time data for a deep learning model integrating a classical drift flux model for a non-intrusive, comprehensive measurement system. Two models, One Dimensional Convolutional Bidirectional Long Short-Term Memory Neural Network (1D CNN-BiLSTM) and Multiphase Flow Estimation Neural Network (MFENet)-featuring positional encoding, multi-attention mechanisms, and a sliding window-were developed. Testing across 185 different flow conditions demonstrated superior precision of MFENet in flow predictions with average relative errors of 2.45% for gas volumetric flow rate and 1.38% for water volumetric flow rate, outperforming 1D CNN-BiLSTM. This emphasizes the capability of deep learning to improve the accuracy of multiphase flow measurement techniques.

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“…Sara Rashed et al established a new approach to measure each component in oil-gas-water mixtures by deploying devices such as biplane electrical resistance tomography sensors, gradient pressure gauges, and flow density meters, so as to estimate fluid volumetric flow rates. They further deduce the water content and gas volume fraction based on the method [12]. With cameras, Wu Zhou et al obtained the size and position of each imaged droplet using the depth-from-focus method and introduced a simple algorithm to estimate the size and position from the twocamera images.…”

Section: Introductionmentioning

confidence: 99%

Blowout Fluid Velocity Measurement Method based on High-speed Sequence Images

Teng,

Zhang

2024

FCIS

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Given the high-temperature and high-pressure environment after the blowout, the staff usually cannot calculate and analyze various blowout parameters at close range and thus take effective measures to control accidents quickly. In this paper, a blowout fluid velocity measurement method is proposed based on high-speed sequence images, and simulation experiments are conducted. The scene conditions of blowout accidents are simulated and analyzed. On this basis, cameras are adopted to shoot a large number of simulated blowout fluid videos from different angles under outdoor conditions. Afterward, the video is converted into a multi-frame image, and the blowout fluid image feature point detection and extraction model are established based on the orb (oriented fast and rotated brief) algorithm. On the feature points extracted by the model, the gms algorithm is used to match the features of the blowout fluid images in the front and back frames. Additionally, the wrong matching points are eliminated using the ransac (random sample consensus) algorithm, and the obtained feature point pairs are employed into the flow velocity calculation model for flow velocity calculation. The results demonstrate that the measured fluid velocity is more accurate. The method proposed in this study can provide reliable data support for early blowout emergency rescue.

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Electrical resistance tomography-based multi-modality sensor and drift flux model for measurement of oil–gas–water flow

Cited by 7 publications

References 18 publications

Transient cutting transport model for horizontal wells with a slim hole

Transient cutting transport model for horizontal wells with a slim hole

Enhancing Accuracy in Gas-Water Two-Phase Flow Sensor Systems Through Deep-Learning-based Computational Framework

Blowout Fluid Velocity Measurement Method based on High-speed Sequence Images

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