Reservoir characterization in under-balanced drilling with nonlinear Moving Horizon Estimation with manual and automatic control conditions

Nikoofard, Amirhossein; Johansen, Tor Arne; Molaei, Amirmasoud

doi:10.1016/j.petrol.2020.107248

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…It is reported in [57], [68], [69] that there are two main types of parameter estimation approaches using family of KFs, these are dual and joint filtering. In joint filtering, the parameters are concatenated to state vector and they are estimated by just one filter, whereas in dual filtering, two filters are separately used for the state and parameter estimation.…”

Section: Respiratory Rate Estimation Using Modjukfmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the timevarying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method. INDEX TERMS Unscented Kalman filter, joint unscented Kalman filter, respiratory rate tracking, devicefree, vital signs, health monitoring, radio signals.

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Section: Respiratory Rate Estimation Using Modjukfmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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Robust monitoring of solvent based carbon capture process using deep learning network based moving horizon estimation

Wang¹,

Cheng²,

Wu³

et al. 2022

Fuel

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Intelligent Inversion Analysis of Drilling Gas Kick Characteristic Parameters and Advanced Predictions of Development Trends

Yin,

Li,

Xiao

et al. 2024

SPE Journal

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Summary Gas kicks are a common and complex occurrence in oil and gas exploration and development. Obtaining the characteristic parameters promptly and accurately after a gas kick occurs is of utmost importance. The kick characteristic parameters are essential foundational data for accurately understanding the multiphase flow state within the wellbore, analyzing kick development trends, and formulating well-killing design. Traditional methods for determining gas kick characteristic parameters often involve trial estimation, a process that can introduce significant arbitrariness. Alternatively, using specialized equipment for measurement and analysis is an option, but it can be time-consuming and limited by technology and cost constraints. This paper proposes an intelligent inversion method for kick characteristic parameters based on surface logging data, including standpipe pressure and mud pit volume. First, a gas kick simulation model is developed based on wellbore multiphase flow theory. This model can accurately simulate the changes in surface logging parameters that occur when a gas kick occurs. Next, the particle swarm optimization algorithm is used to optimize the kick parameters, such as the gas-influx index and pore pressure, in conjunction with the gas kick simulation model. The optimization evaluation criterion is the Fréchet distance, which is used to identify the calculated curve that is most similar to the actual logging parameter change curve. Subsequently, kick trends can be predicted using the simulation model based on these kick parameters. The case analysis demonstrates that the method proposed in this paper can rapidly acquire the gas kick characteristic parameters based on the early change characteristics of logging parameters. It effectively simulates and reproduces the true distribution of wellbore fluids, enabling advanced prediction of kick development. This approach helps in preparing preventive measures in advance, reducing the risk of accidents, and minimizing financial losses.

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Reservoir characterization in under-balanced drilling with nonlinear Moving Horizon Estimation with manual and automatic control conditions

Cited by 3 publications

References 44 publications

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

Robust monitoring of solvent based carbon capture process using deep learning network based moving horizon estimation

Intelligent Inversion Analysis of Drilling Gas Kick Characteristic Parameters and Advanced Predictions of Development Trends

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