Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Grana, Dario; Paparozzi, E.; Mancini, Silvia; Tarchiani, C.

doi:10.1111/j.1365-2478.2012.01115.x

Cited by 37 publications

(6 citation statements)

References 31 publications

(62 reference statements)

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“…Different approaches to seismic data utilization for lithofacies propagation in the reservoir model are available. Grana et al (2013) [33] and Babasafari et al (2020) [34] documented good examples of facies probability prediction based on seismic inversion. Interdependence between Lithology classes and Zp here has been examined by facies probability analysis.…”

Section: Seismic Data Integration In the Static Reservoir Modelmentioning

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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Section: Seismic Data Integration In the Static Reservoir Modelmentioning

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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show abstract

“…Some researchers can reduce the multi-solution of geostatistics by adding certain prior constraints to narrow the range of reservoir parameter solution space (Pacheco et al, 2022;Yu et al, 2020). Geostatistical seismic inversion uses the constraints of seismic data to control the solution space of reservoir parameters within the structural characteristics of seismic data, which can effectively reduce the multi-solution of geostatistics Grana et al, 2013). Lang and Grana (2017), Fjeldstad and Grana (2018) and de Figueiredo et al (2019) proposed a multimodal distribution assumption, combining lithology division with inversion, constructing a Gaussian mixture posterior probability distribution and reducing calculation costs.…”

Section: Introductionmentioning

confidence: 99%

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Su,

Xu,

Chen

et al. 2024

Geophysical Prospecting

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Geostatistical seismic inversion is an important method for establishing high‐resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi‐scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies‐constrained geostatistical inversion method based on multi‐scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi‐scale sedimentary structural features of reservoirs are learned from well‐logging data. Seismic facies and multi‐scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi‐scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.

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“…The core problem of reservoir modelling is reservoir prediction between wells. Compared with other geological bodies, oil and gas reservoirs are controlled by a variety of parameters, such as complex rock structures, spatial configurations and spatial changes in reservoir parameters, which leads to multiple solutions of stochastic modelling [25][26][27][28]. The accuracy of reservoir stochastic modelling depends on the number of well points involved in interpolation.…”

Section: Introductionmentioning

confidence: 99%

Relationship between well pattern density and variation function of stochastic modelling and database establishment

Wang

Zhao

Yan³

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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For 3D geological modelling of oil and gas reservoirs, well pattern density is directly related to the number of samples involved in the calculation, which determines the variation function of stochastic modelling and has great impacts on the results of reservoir modelling. This paper focuses on the relationship between well pattern density and the variogram of stochastic modelling, selects the large Sulige gas field with many well pattern types as the research object, and establishes a variogram database of stochastic models for different well pattern densities. First, the well pattern in the study area is divided into three different types (well patterns A, B, and C) according to well and row space. Several different small blocks (model samples) are selected from each type of well pattern to establish the model, and their reasonable variogram values (major range, minor range and vertical range) are obtained. Then, the variogram values of all model samples with similar well pattern densities are analysed and counted, and the variogram database corresponding to each type of well pattern is established. Finally, the statistical results are applied to the modelling process of other blocks with similar well pattern density to test their accuracy. The results show that the reservoir model established by using the variation function provided in this paper agrees well with the actual geological conditions and that the random model has a high degree of convergence. This database has high adaptability, and the model established is reliable.

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Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Cited by 37 publications

References 31 publications

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

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

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Relationship between well pattern density and variation function of stochastic modelling and database establishment

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