Permeability prediction for carbonate reservoir using a data-driven model comprising deep learning network, particle swarm optimization, and support vector regression: a case study of the LULA oilfield

Gu, Yufeng; Bao, Zhidong; Xiao, Song; Wei, Mingyang; Zang, Dongsheng; Niu, Bo; Kai, Lu

doi:10.1007/s12517-019-4804-3

Cited by 12 publications

(7 citation statements)

References 60 publications

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“…42 Moreover, a support vector machine is an effective AI tool that utilizes kernel functions during the training stage. 43 The SVM showed a lower computational load compared to other AI methods. 44 Similar to the ANN and FLS, the SVM has been used widely for classification and prediction purposes.…”

Section: ■ Methodologymentioning

confidence: 94%

“…In the petroleum industry, fuzzy logic is proven to be an effective tool in many fields such as drilling optimization, well stimulation, and rock mechanics and permeability estimation . Moreover, a support vector machine is an effective AI tool that utilizes kernel functions during the training stage . The SVM showed a lower computational load compared to other AI methods .…”

Section: Methodsmentioning

confidence: 99%

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An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

2021

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Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young’s modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

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Section: ■ Methodologymentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

2021

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“…The long-short memory neural network (LSTM) was introduced by Zhang et al [ 8 ] into the logging curve synthesis, and the results of real logging data verification revealed that the synthesized logging curve of the LSTM neural network is more accurate than that of the fully connected neural network (FCNN), which more suitable for solving complex problems. A hybrid data-driven model consisting of a PSO, SVR, and deep learning network was built by Gu et al [ 9 ] to produce more accurate predictions. Reda Abdel Azim et al [ 10 ] propose an artificial neural network (ANN) model based on the back propagation learning algorithm to predict formation permeability from well logs, using a weight visualization curve technique to optimize the number of hidden neurons and layers.…”

Section: Introductionmentioning

confidence: 99%

Reservoir parameters prediction based on spatially transferred long short-term memory network

Huang,

Tian

2024

PLoS ONE

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Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually shifting from previous petrophysical models to deep learning models, which bring about obvious improvements in terms of accuracy and efficiency. However, it is difficult to achieve large amount of data acquisition required for deep learning due to the cost of detection, technical difficulties, and the limitations of complex geological parameters. To address the data shortage problem, a transfer learning prediction model based on long short-term memory neural networks has been proposed, and the model structure has been determined by parameter search and optimization methods in this paper. The proposed approach transfers knowledge from historical data to enhance new well prediction by sharing some parameters in the neural network structure. Moreover, the practicality and effectiveness of this method was tested by comparison based on two block datasets. The results showed that this method could significantly improve the prediction accuracy of the reservoir parameters in the event of data shortage.

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“…In the last decade, machine learning has emerged as a promising avenue within petrophysics to predict reservoir properties . Several studies evaluated machine learning algorithms to predict porosity and permeability in carbonate reservoirs using conventional well logs as input to supervised models (Anifowose et al 2014;Ao et al, 2018;Gu et al, 2019;Al Khalifah et al, 2019). Despite some success, these models may not perform well in complex porous systems.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, SVR is equipped to ignore outliers that could cause prediction failure (Bagheri & Rezaei, 2019). On the other hand, SVR may have problems with collinearity between input variables and initialization of calculation parameters, which require mapped variables in the hyperspace to be linear (Gu et al, 2019).…”

Section: Support Vector Regressionmentioning

confidence: 99%

Carbonate reservoir characterization with integrated rock, special well logs, and machine learning methods

Wamzer Jeiss

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Permeability prediction for carbonate reservoir using a data-driven model comprising deep learning network, particle swarm optimization, and support vector regression: a case study of the LULA oilfield

Cited by 12 publications

References 60 publications

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

Reservoir parameters prediction based on spatially transferred long short-term memory network

Carbonate reservoir characterization with integrated rock, special well logs, and machine learning methods

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