Porosity prediction from pre-stack seismic data via a data-driven approach

Yang, Naxia; Li, Guofa; Zhao, Pingqi; Zhang, Jialiang; Zhao, Dongyuan

doi:10.1016/j.jappgeo.2023.104947

Cited by 9 publications

(8 citation statements)

References 56 publications

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“…Moreover, it is worth noting that the coefficients of functions (B 1 to B 15 ) are nothing but the optimized weights and biases of the in-house MLP-ANN model presented in Table 6. Furthermore, it should be noted that Equation (17) provides the normalized porosity value (φ n ); therefore, in order to obtain the true porosity value (φ), Equation ( 19) must be used to de-normalize the result as follows:…”

Section: Development Of An Explicit Ann-based Porosity Formulamentioning

confidence: 99%

“…In addition, Figure 6B shows a cross-plot of estimated p Equation ( 17)) versus actual values, and it can be seen that most of the data c Furthermore, an independent dataset of 205 well-log records from well X, which was not used in any phase of this study (i.e., training, cross-validation, or testing), was utilized to further assess the accuracy of the developed equation (Equation ( 17)). Figure 6A presents the reservoir's actual porosity profile of well X against the porosity profile predicted by Equation (17) for the independent dataset. It can be seen from Figure 6A that the developed equation (Equation ( 17)) produces a porosity profile that closely matches the true porosity profile.…”

Section: Development Of An Explicit Ann-based Porosity Formulamentioning

confidence: 99%

“…In this section, we present a brief demonstration of the use of the proposed ANN-based porosity correlation (Equation (17) in the main text).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…By utilizing large volumes of well-log data and core samples, these techniques can provide more accurate porosity estimates, even in complex reservoirs with significant heterogeneity [13][14][15]. Furthermore, machine learning models can also incorporate data from multiple sources, such as seismic features and well-log data, to provide a more comprehensive understanding of the reservoir properties and improve the porosity estimation accuracy [16,17]. In the literature, artificial neural networks [18][19][20][21][22][23][24], support vector machines [25][26][27], fuzzy logic [28][29][30], and neuro-fuzzy [31][32][33] are all examples of machine learning techniques that are often used for reservoir characterization, including porosity estimation.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

Alatefi

Azim²,

Alkouh

et al. 2023

Processes

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The accurate estimation of reservoir porosity plays a vital role in estimating the amount of hydrocarbon reserves and evaluating the economic potential of a reservoir. It also aids decision making during the exploration and development phases of oil and gas fields. This study evaluates the integration of artificial intelligence techniques, conventional well logs, and core analysis for the accurate prediction of porosity in carbonate reservoirs. In general, carbonate reservoirs are characterized by their complex pore systems, with the wide spatial variation and highly nonlinear nature of their petrophysical properties. Therefore, they require detailed well-log interpretations to accurately estimate their properties, making them good candidates for the application of machine learning techniques. Accordingly, a large database of (2100) well-log records and core-porosity measurements were integrated with four state-of-the-art machine learning techniques (multilayer perceptron artificial neural network, MLP-ANN; Gaussian process regression, GPR; least squares gradient boosting ensemble, LS-Boost; and radial basis function neural network, RBF-NN) for the prediction of reservoir porosity. The well-log data used in this study include sonic acoustic travel time, Gamma-ray, and bulk density log records, which were carefully collected from five wells in a carbonate reservoir. This study revealed that all the artificial intelligence models achieved high accuracy, with R-squared values exceeding 90% during both the training and blind-testing phases. Among the AI models examined, the GPR model outperformed the others in terms of the R-squared values, root-mean-square error (RMSE), and coefficient of variation of the root-mean-square error (CVRMSE). Furthermore, this study introduces an artificially intelligent AI-based correlation for the estimation of reservoir porosity from well-log data; this correlation was developed using an in-house, Fortran-coded MLP-ANN model presented herein. This AI-based correlation gave a promising level of accuracy, with R-squared values of 92% and 90% for the training and blind-testing datasets, respectively. This correlation can serve as an accurate and easy-to-use tool for porosity prediction without any prior experience in utilizing or implementing machine learning models.

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Section: Development Of An Explicit Ann-based Porosity Formulamentioning

confidence: 99%

Section: Development Of An Explicit Ann-based Porosity Formulamentioning

confidence: 99%

“…In this section, we present a brief demonstration of the use of the proposed ANN-based porosity correlation (Equation (17) in the main text).…”

Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

Alatefi

Azim²,

Alkouh

et al. 2023

Processes

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show abstract

“…Yang, et al (2023) [10] presented two neural network models, the PorNet model and the BlstmNet model, to predict porosity directly from pre-stack seismic data. They tested these NN models on synthetic seismic gathers and compared them to the test results.…”

Section: Introductionmentioning

confidence: 99%

Lithology and Porosity Distribution of High-Porosity Sandstone Reservoir in North Adriatic Using Machine Learning Synthetic Well Catalogue

Vukadin¹,

Čogelja²,

Vidaček³

et al. 2023

Applied Sciences

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Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.

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Prestack Porosity Direct Inversion Based on Logging Constraints

Yan,

Zhou,

et al. 2024

Springer Series in Geomechanics and Geoengineering

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Porosity prediction from pre-stack seismic data via a data-driven approach

Cited by 9 publications

References 56 publications

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

Integration of Multiple Bayesian Optimized Machine Learning Techniques and Conventional Well Logs for Accurate Prediction of Porosity in Carbonate Reservoirs

Lithology and Porosity Distribution of High-Porosity Sandstone Reservoir in North Adriatic Using Machine Learning Synthetic Well Catalogue

Prestack Porosity Direct Inversion Based on Logging Constraints

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