Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

Yavari, Hossein; Khosravanian, Rasool; Wood, David A.; Aadnøy, Bernt S.

doi:10.46690/ager.2021.04.05

Cited by 14 publications

(3 citation statements)

References 50 publications

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“…With the development of the computational power, the image treatment driven by the artificial intelligence algorithm is adopted frequently in the geological and mining engineering [16][17][18][19][20][21][22][23][24][25][26][27]. For example, under different light source positions, Saricam and Ozturk [16] located the positions of the fractures in terms of the shadows, segmented the drilling core pieces, and calculated the RQD value.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Xiong,

Wang,

Zhang

et al. 2024

Advances in Civil Engineering

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In the construction of the civil engineering infrastructure, the noise and vibration are affected by the geological conditions, adopting specific construction techniques based on the geological conditions is of great significance in suppressing the noise and vibration caused by the construction. To classify and evaluate the rock mass quality, the rock quality designation (RQD) is adopted widely in the geological and mining engineering. Traditionally, to obtain RQD, lengths of drilling core pieces are measured and RQD is calculated manually, which is labor-expensive and time-consuming. With the development of the computational power, the image treatment driven by the computer vision creates a potential approach to obtain RQD automatically. In the present work, the image treatment process with the aid of the object detection and the image segmentation is adopted to obtain RQD automatically, based on the similarity of features such as color and texture, the segment anything model is adopted to detect the rock cores in the image, then, the YOLOv8 algorithm is adopted to train the model, and the gap features of the rock chip segments are extracted for segmentation of different rock core segments. To test the performance of the proposed approach, 10 boreholes from Shapingba Railway Comprehensive Reconstruction Project are adopted to conduct the case study. Compared to the traditional manual approach, RQD obtained by the proposed approach is relatively accurate and obviously efficient, namely, the average error is less than 5% and the time consumed is less than 70%.

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Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Xiong,

Wang,

Zhang

et al. 2024

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Moreover, multiple simulation runs are required to inverse problems, conduct sensitivity analyses, and optimize a project's design. Recently, surrogate models to traditional numerical models have been widely employed in the field of reservoir development, including reduced-order methods [2][3][4][5][6], deep learning methods [7][8][9][10][11][12][13][14][15], and Gaussian process [16,17], which provide a rapid numerical model approximator to efficiently solve inverse problems, optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Han,

Xue,

Jia

et al. 2024

CMES

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Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physicsinformed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different physical properties. In this work, we propose a criss-cross physics-informed convolutional neural network (CC-PINN) learning architecture, aiming to learn the solution of parametric PDEs with spatial heterogeneity of physical properties. To achieve the seamless enforcement of flux continuity and integration of physical meaning into CNN, a predefined 2D convolutional layer is proposed to accurately express transmissibility between adjacent cells. The efficacy of the proposed method was evaluated through predictions of several petroleum reservoir problems with spatial heterogeneity and compared against state-of-the-art (PINN) through numerical analysis as a benchmark, which demonstrated the superiority of the proposed method over the PINN.

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“…Xue et al combined the random forest and ensemble Kalman filter to forecast the dynamic transient pressure automatically. Yavari et al adopted the ANFIS model to estimate the pressure difference from the end point (toe) to the hell in the lateral section of the horizontal well. ANN models were also applied to the prediction of minimum CO 2 miscibility pressure. , Fan et al developed an autoregressive integrated moving average-long short-term memory (ARIMA-LSTM) hybrid model to forecast the well production.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

et al. 2022

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A well production rate is an essential parameter in oil and gas field development. Traditional models have limitations for the well production rate estimation, e.g., numerical simulations are computation-expensive, and empirical models are based on oversimplified assumptions. An artificial neural network (ANN) is an artificial intelligence method commonly used in regression problems. This work aims to apply an ANN model to estimate the oil production rate (OPR), water oil ratio (WOR), and gas oil ratio (GOR). Specifically, data analysis was first performed to select the appropriate well operation parameters for OPR, WOR, and GOR. Different ANN hyperparameters (network, training function, and transfer function) were then evaluated to determine the optimal ANN setting. Transfer function groups were further analyzed to determine the best combination of transfer functions in the hidden layers. In addition, this study adopted the relative root mean square error with the statistical parameters from a stochastic point of view to select the optimal transfer functions. The optimal ANN model’s average relative root mean square error reached 6.8% for OPR, 18.0% for WOR, and 1.98% for GOR, which indicated the effectiveness of the optimized ANN model for well production estimation. Furthermore, comparison with the empirical model and the inputs effect through a Monte Carlo simulation illustrated the strength and limitation of the ANN model.

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Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

Cited by 14 publications

References 50 publications

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

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