Comparison of machine learning techniques for predicting porosity of chalk

Nourani, Meysam; Alali, Najeh; Samadianfard, Saeed; Band, Shahab S.; Chau, Kwok-wing; Shu, Chi‐Min

doi:10.1016/j.petrol.2021.109853

Cited by 12 publications

(5 citation statements)

References 65 publications

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“…Researchers have commonly used ANN to predict porosity in various formations [39], [57], [66], [80]- [84]. Using a backpropagation ANN, Helle et al [39] predicted porosity and permeability in the North Sea.…”

Section: B Porositymentioning

confidence: 99%

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Machine Learning in Oil and Gas Exploration: A Review

Lawal,

Yang,

et al. 2024

IEEE Access

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A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning's involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field's dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into lesscharted territories in this evolving field, offering valuable insights and guidance for future research.INDEX TERMS Oil and gas exploration, machine learning, petrophysical properties prediction, facies and lithofacies classification, seismic data processing.

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Section: B Porositymentioning

confidence: 99%

“…However, the authors noted that ANN provides better accuracy. Conversely, Nourani et al [84] utilized Hand-held X-ray fluorescence (HH-XRF) as input for porosity prediction in a chalk reservoir. The authors relied on the speed and accuracy provided by the HH-XRF approach for geochemical characterization.…”

Section: B Porositymentioning

confidence: 99%

Machine Learning in Oil and Gas Exploration: A Review

Lawal,

Yang,

et al. 2024

IEEE Access

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“…Hybrid forms of MLP and LSSVM algorithms with PSO and GA optimizers have presented themselves as promising hybrid methods for diverse prediction purposes in different engineering sections, particularly in the oil and gas industry. For instance, these hybrid methods have shown significant accuracy in the prediction of different parameters, such as shear wave velocity (Ghorbani et al, 2021;Miah, 2021;Rajabi et al, 2022a), viscosity of waxy crude oils (Madani et al, 2021), estimating formation pore pressure (Rajabi et al, 2022b), safe mud window (Beheshtian et al, 2022), gas flow rate (Abad et al, 2022), casing collapse (Mohamadian et al, 2021), rock porosity and permeability (Nourani et al, 2022), two-phase flow pressure drop modelling (Faraji et al, 2022), rate of penetration in drilling (Hashemizadeh et al, 2022), gas condensate viscosity (Abad et al, 2021a), prediction based on biodiesel distillation (Vera-Rozo et al, 2022), oil holdup (Zhang et al, 2011). The promising accuracy achieved by the optimized LSSVM and MLP models for different prediction purposes trigged the idea to establish solid hybrid methods based on these algorithms and evaluate their performance in the FVDC prediction using conventional well logs.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In recent years, artificial intelligence has grown dramatically, and many people have used it to predict important parameters in various fields (Ahmadi et al, 2020;Nabipour et al, 2020b;Nourani et al, 2022). Previous research has made some…”

Section: Introductionmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

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One of the challenges that reservoir engineers, drilling engineers, and geoscientists face in the oil and gas industry is determining the fracture density (FVDC) of reservoir rock. This critical parameter is valuable because its presence in oil and gas reservoirs boosts productivity and is pivotal for reservoir management, operation, and ultimately energy management. This valuable parameter is determined by some expensive operations such as FMI logs and core analysis techniques. As a result, this paper attempts to predict this important parameter using petrophysics logs routinely collected at oil and gas wells and by applying four robust computational algorithms and artificial intelligence hybrids. A total of 6067 data points were collected from three gas wells (#W1, #W2, and #W3) in one gas reservoir in Southwest Asia. Following feature selection, the input variables include spectral gamma ray (SGR); sonic porosity (PHIS); potassium (POTA); photoelectric absorption factor (PEF); neutron porosity (NPHI); sonic transition time (DT); bulk density (RHOB); and corrected gamma ray (CGR). In this study, four hybrids of two networks were used, including least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) with two optimizers particle swarm optimizer (PSO) and genetic algorithm (GA). Four robust hybrid machine learning models were applied, and these are LSSVM-PSO/GA and MLP-PSO/GA, which had not previously used for prediction of FVDC. In addition, the k-fold cross validation method with k equal to 8 was used in this article. When the performance accuracy of the hybrid algorithms for the FVDC prediction is compared, the revealed result is LSSVM-PSO > LSSVM-GA > MLP-PSO > MLP-GA. The study revealed that the best algorithm for predicting FVDC among the four algorithms is LSSVM-PSO (for total dataset RMSE = 0.0463 1/m; R2 = 0.9995). This algorithm has several advantages, including: 1) lower adjustment parameters, 2) high search efficiency, 3) fast convergence speed, 4) increased global search capability, and 5) preventing the local optimum from falling. When compared to other models, this model has the lowest error.

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“…Using post-stack 3D seismic amplitude data, Leite and Vidal (2011) made porosity maps that consider constraints from borehole log density and acoustic data. The impact of DL on predicting rock properties has been a subject of study in several works, comparing various algorithms (Al-Anazi & Gates, 2012;Nourani et al, 2022;Singh et al, 2024;Verma et al, 2024). Specifically, Al-Anazi and Gates (2012) compared SVR and MLP for evaluating porosity and permeability in heterogeneous sandstone reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ntibahanana,

Jianguo,

Luemba

et al. 2024

Earth and Space Science

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Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

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Comparison of machine learning techniques for predicting porosity of chalk

Cited by 12 publications

References 65 publications

Machine Learning in Oil and Gas Exploration: A Review

Machine Learning in Oil and Gas Exploration: A Review

Prediction of fracture density in a gas reservoir using robust computational approaches

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

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