Prediction of dynamic liquid level in water-producing shale gas wells based on liquid film model

Nie, Jie; Qiao, Lingxi; Wang, BenQiang; Wang, Weiyang; Li, Mingzhong; Zhou, Chenru

doi:10.3389/feart.2023.1230470

Cited by 3 publications

(2 citation statements)

References 48 publications

(23 reference statements)

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“…The coupling wave is formed by a small portion of the reflected wave generated when the acoustic wave encounters the reflective interface created by the oil tube coupling. Assuming that the length of the oil tube is

, and the travel path difference for the coupling wave echo is 2

, with the frequency of the coupling wave obtained through the Fourier transform being

, the formula for the calculation of the propagation speed of sound waves in the oil well is as follows [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ].

…”

Section: Principles and Methodsmentioning

confidence: 99%

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Liu,

Fan,

Liu

et al. 2024

Sensors

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Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells’ dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield’s central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

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, and the travel path difference for the coupling wave echo is 2

, with the frequency of the coupling wave obtained through the Fourier transform being

, the formula for the calculation of the propagation speed of sound waves in the oil well is as follows [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ].

…”

Section: Principles and Methodsmentioning

confidence: 99%

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Liu,

Fan,

Liu

et al. 2024

Sensors

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show abstract

“…In practical applications, it is necessary to select a suitable model for analysis based on the characteristics of the wellbore (Nie et al, 2023). After comparing the critical liquid loading models under different conditions, it can be more targeted in predicting the timing of gas well liquid loading in field applications in order to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Review on critical liquid loading models and their application in deep unconventional gas reservoirs

He,

Huang,

Yang

et al. 2024

Front. Energy Res.

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The exploitation of deep unconventional gas resources has gradually become more significant attributing to their huge reserves and the severe depletion of convention gas resources in the world. The proportion of deep unconventional gas reservoirs in the total gas resources cannot be underestimated, including shale gas, tight gas, and gas of coal seam. Due to the low permeability and porosity, hydraulic fracturing technology is still an important means to develop deep unconventional gas resources. However, the presence of fracturing fluids and water accumulation at the bottom of the wellbore significantly reduce gas production. The liquid loading model can be used to determine when the gas well begins to load the liquid. In this work, different types of liquid loading models are classified, and the applicability of different models is analyzed. At present, the existing critical liquid carrying models can be divided into mechanism models and semi-empirical models. The model established by Turner is a typical mechanism model. There are great differences in the application of a critical liquid loading model between vertical and horizontal wells. The field cases of a liquid loading model in different gas fields are provided and discussed. The mechanism of liquid loading models in recent years is introduced and analyzed. The physical simulations and experimental work therein are described and discussed to clarify the feasibility of the modeling mechanism. This article also presents the limitation and future work for improving the liquid loading models.

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Prediction of Liquid Accumulation Height in Gas Well Tubing Using Integration of Crayfish Optimization Algorithm and XGBoost

Xia,

Liu,

Xiang

2024

Processes

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The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells.

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Prediction of dynamic liquid level in water-producing shale gas wells based on liquid film model

Cited by 3 publications

References 48 publications

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level

Review on critical liquid loading models and their application in deep unconventional gas reservoirs

Prediction of Liquid Accumulation Height in Gas Well Tubing Using Integration of Crayfish Optimization Algorithm and XGBoost

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