An intelligent data-driven model for virtual flow meters in oil and gas development

Song, Shangfei; Wu, Mian; Qi, Jianing; Wu, Haihao; Kang, Qi; Shi, Bohui; Shen, Siheng; Li, Qingping; Yao, Haiyuan; Chen, Haihong; Gong, Jing

doi:10.1016/j.cherd.2022.08.016

Cited by 14 publications

(2 citation statements)

References 26 publications

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“…According to Song et al [27], neural networks' data learning and non-linear modelling abilities are factors for their consideration over other machine learning methods. Also, it is more easily represented mathematically than any other machine learning approach.…”

Section: Performance Of the Developed Neural Networkmentioning

confidence: 99%

Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Udoma,

Okon,

Udoeyop

et al. 2023

Int. J. Petrol. Technol.

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Methods to determine static bottom-hole pressure (BHP) from surface measurements include the average temperature and z-factor method, the Sukkar-Cornell method, the Cullender-Smith method, and the Poettmann method. Among these methods, the Poettmann method is preferable in the petroleum industry but with a concern for software developers, as the integral values to determine the static BHP are tabular. In this study, neural network-based models to predict the integral values using pseudo-reduced pressures and temperatures were developed. The 2-3-1, 2-4-1, and 2-5-1 neural-based models had overall correlation coefficients (R) of 0.9974, 0.99835, and 0.99745, respectively, for the maximum-minimum normalization method and R of 0.99745, 0.99805, and 0.9992 for the clip-scaling method. Comparing the models' predictions with the Lagrangian interpolated values resulted in R of 0.99895 and 0.9995 for the maximum-minimum and clip-scaling-based models. Thus, the developed models can predict Poettmann's integral values without table look-up to estimate static BHP in gas wells.

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Section: Performance Of the Developed Neural Networkmentioning

confidence: 99%

Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Udoma,

Okon,

Udoeyop

et al. 2023

Int. J. Petrol. Technol.

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show abstract

“…The method performed better than the ARIMA, Arps, and Duong methods. In another study, Song et al [11] used LSTM, a feature extraction and optimization model that incorporates feature engineering and parameter optimization and exhibits the lowest mean absolute error (MAE) value compared to other www.ijacsa.thesai.org models such as BPNN, LSTM, and random forest as reported by Dyer et al [12] One of the recent challenges in oil and gas is predicting crude oil prices. The demand for crude oil price prediction has increased due to crude oil's complex and highly unpredictable characteristics of crude oil [13].…”

Section: Introductionmentioning

confidence: 99%

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Yusoff,

Ehsan,

Sharif

et al. 2024

IJACSA

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Forecasting crude oil prices hold significant importance in finance, energy, and economics, given its extensive impact on worldwide markets and socio-economic equilibrium. Using Long Short-Term Memory (LSTM) neural networks has exhibited noteworthy achievements in time series forecasting, specifically in predicting crude oil prices. Nevertheless, LSTM models frequently depend on the manual adjustment of hyperparameters, a task that can be laborious and demanding. This study presents a novel methodology incorporating Particle Swarm Optimization (PSO) into LSTM networks to optimize the network architecture and minimize the error. This study employs historical data on crude oil prices to explore and identify optimal hyperparameters autonomously and embedded with the star and ring topology of PSO to address the local and global search capabilities. The findings demonstrate that LSTM+starPSO is superior to LSTM+ringPSO, previous hybrid LSTM-PSO, conventional LSTM networks, and statistical time series methods in its predictive accuracy. LSTM+starPSO model offers a better RMSE of about +0.16% and +22.82% for WTI and BRENT datasets, respectively. The results indicate that the LSTM model, when enhanced with PSO, demonstrates a better proficiency in capturing the patterns and inherent dynamics data changes of crude oil prices. The proposed model offers a dual benefit by alleviating the need for manual hyperparameter tuning and serving as a valuable resource for stakeholders in the energy and financial industries interested in obtaining dependable insights into fluctuations in crude oil prices.

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Two-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networks

Ruiz-Díaz,

Quispe-Suarez,

González-Estrada

2024

emergent mater.

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Accurate biphasic flow pattern recognition is essential in the design of coatings for the oil and gas sector because it enables engineers to create materials that are tailored to specific flow conditions. This results in enhanced corrosion protection, erosion resistance, flow efficiency, and overall performance of equipment and infrastructure in the challenging environments of the oil and gas industry. The development of flow maps has been based on empirical correlations that incorporate characteristics such as superficial velocities, volume fractions, and physical properties such as the density and viscosity of the analyzed substances. In addition, geometric parameters such as the inclination and the internal diameter of the pipes are considered. However, due to the difficult working conditions on offshore platforms and the limitations in monitoring internal flow patterns, technological advances have been implemented to improve this process using artificial intelligence techniques. In this context, this study proposes using a long-term memory (LSTM) recurrent neural network to predict the flow patterns generated in vertical pipes. This LSTM network was trained and validated using data obtained from a literature database. The results obtained showed that the model has a prediction error of less than 1%. These technological advances represent an important step towards optimizing the flow pattern identification process in the hydrocarbons industry. By leveraging the capabilities of artificial intelligence, more accurate and reliable forecasts can be obtained, enabling informed decisions and improving the efficiency and safety of operations.

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An intelligent data-driven model for virtual flow meters in oil and gas development

Cited by 14 publications

References 26 publications

Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Two-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networks

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