Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods

Tian, Jianwei; Qi, Chongchong; Sun, Yingfeng; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Pham, Binh Thai

doi:10.1007/s00366-020-01012-z

Cited by 88 publications

(36 citation statements)

References 79 publications

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“…For future research, the permeability and saturation index should be quantified using machine learning models, which could remarkably contribute to this research domain through new soft computing technology (Tian et al, 2020a(Tian et al, , 2020bYaseen et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Effect of Water Flooding on Oil Reservoir Permeability: Saturation Index Prediction Model for Giant Oil Reservoirs, Southern Iraq

Awadh

Al-Mimar

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2021

Nat Resour Res

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This research was conducted on five oilfields in the Mishrif reservoir, southern Iraq, to illustrate the effects of permeability on the damage caused by the injection of river water into the oilfield. Oilfield flooding has dramatically changed the pH and brine chemistry of the reservoir and resulted in the deposition of carbonates and native sulfur. The air permeability test and scanning electron microscope (SEM) analysis revealed how the precipitated minerals and materials (e.g., residual heavy oil, asphaltene, wax, native sulfur and authigenic and diagenetic clay minerals) reduced the permeability by lining the pore necks. The PHREEQC software and saturation index (SI) model revealed several types of porelining scales formed in a porous matrix. The SI model indicates that barite, celestite, calcite and pyrite are common scales lining the pore spaces, which precipitated in response to the injection of sulfate-and carbonate-rich river water into an oil well. This research generated useful findings that can be taken into consideration for future oil production worldwide. Additionally, the permeability values were used as evidence for discrimination of sedimentary environment, particularly reef and non-reef facies.

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Section: Discussionmentioning

confidence: 99%

Effect of Water Flooding on Oil Reservoir Permeability: Saturation Index Prediction Model for Giant Oil Reservoirs, Southern Iraq

Awadh

Al-Mimar

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2021

Nat Resour Res

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“…The fiber volume fraction in this work possessed a 0.20-4.50 (%) range, compared with 0.25-3.00 (%) in [164] and 0.25-2.00 (%) in [43]. Once the database contained the relevant information on the constituent materials, the prediction ML tool had the ability to estimate other experiments using the values from the training database used to construct the ML model [166]. Again, a reliable dataset that covers a wide range of input values is crucial for the development of ML models.…”

Section: Dataset Used For ML Modelingmentioning

confidence: 99%

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

et al. 2020

Sustainability

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Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

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“…Moreover, it can also be extended to solve analogous theoretical problems in other unconventional reservoirs (Cortes and Vapnik, 1995;Anifowose et al, 2014;Nwachukwu et al, 2018;Yu et al, 2020). For example, classification of lithofacies, prediction of permeability and porosity, identification of water saturation using well logging data in reservoirs, and so on (Zhang et al, 2018;Wood, 2019;Zhang et al, 2020a;Tian et al, 2020;Sun et al, 2021;Zhang et al, 2021). The machine learning method is more and more being widely used in reservoir engineering (Gholami et al, 2014;Wang et al, 2014;Li et al, 2020;Silva et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification for Inter-well Nonlinear Permeability Configuration in Low Permeability Reservoirs Utilizing Machine Learning Methods

Liu

2022

Front. Earth Sci.

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Machine learning methods have become the leading research algorithm enjoying popularity for reservoir engineering evaluation. In this paper, one machine learning method is selected and optimized for the recognition and classification of inter-well nonlinear permeability configurations between injection and production wells in the low permeability reservoir. The above configurations are divided into four classes, i.e., homogeneous, linear increment, convexity increasing (logarithmic function), and convex downward increasing (exponential function). According to four kinds of nonlinear permeability distributions in low permeability reservoirs and the increased effect of threshold pressure gradient, the productivity formula is established. Then the decision tree, neural networks (NN) and support vector machines (SVM) are utilized for training dynamic data under the influence of the training model, i.e., the configuration in low-permeability reservoirs. The data set is formed with dynamic production data under different configuration permeability, well spacing, thickness, pressure, and production. The recognition and classification of the permeability configuration are performed using different machine learning models. The results show that compared with NN and decision tree, SVM presents better performance in the accuracy of verification, true positive rate (TPR), false-negative rate (FNR) and receiver operating characteristic (ROC). Moreover, SVM verification results are placed on the brink of the training methods. This paper provides new insights and methods for the recognition and classification of inter-well nonlinear permeability configuration in low permeability reservoirs. Additionally, the research method can also apply to solve similar theoretical problems in other unconventional reservoirs.

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Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods

Cited by 88 publications

References 79 publications

Effect of Water Flooding on Oil Reservoir Permeability: Saturation Index Prediction Model for Giant Oil Reservoirs, Southern Iraq

Effect of Water Flooding on Oil Reservoir Permeability: Saturation Index Prediction Model for Giant Oil Reservoirs, Southern Iraq

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Recognition and Classification for Inter-well Nonlinear Permeability Configuration in Low Permeability Reservoirs Utilizing Machine Learning Methods

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