Agglomerative clustering to improve the resolution of pseudo well stochastic seismic inversion: A case study

Yadav, Ashok; Nayak, Soumya Ranjan; Mondal, Samit

doi:10.1016/j.petrol.2021.109566

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…These procedures are based on optimization strategies and significantly depend on computational resources. Recently, additional efficient approaches based on stochastic Bayesian formulations employing Monte Carlo procedures have been suggested to estimate the posterior distribution of reservoir properties conditioned to geophysical data (De Figueiredo et al, 2017;Yadav et al, 2022;Yan et al, 2020). A pseudo-well problem has a complex and non-convex search space with several local and global optimum solutions.…”

Section: Introductionmentioning

confidence: 99%

“…AI, as a process of porosity and water saturation, is specified by a second-degree polynomial formula utilized as a forward model (Babasafari et al, 2021;Guo et al, 2020;Hosseini et al, 2023c;Hosseini et al, 2023a;Hosseini et al, 2023b;Kianoush et al, 2022b). Yadav et al (2022) applied the revised pseudo-well stochastic seismic inversion approach to seismic data from the deep-water Krishna-Godavari basin to better demonstrate the characterization of the reserve's thin shale layers. Sun et al (2023) and Kianoush et al (2023c) presented an intelligent AVA inversion technique utilizing a convolutional neural network trained by realistic pseudo-well logs; results can lessen interparameter crosstalk artifacts (Billi et al, 2023;Camacho et al, 2020;Cammarata et al, 2018;Cintorrino et al, 2019;Ferranti et al, 2014;Palano et al, 2023;Palano et al, 2015;Palano et al, 2013;Polcari et al, 2022;Secreti et al, 2022;Sparacino et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

Khah,

Salehi,

Kianoush

et al. 2024

Preprint

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The hydrate concentration model considerably affects elastic properties, including bulk and shear modulus. Defining seismic properties of sediments, such as compressional and shear wave velocity and density, provides valuable information to identify rock facies and fluid types. This information commonly results from pre-stack seismic inversion, while post-stack seismic information provides acoustic impedance as a layer-based property. Traditionally, seismic inversion requires well logs to produce an initial guess of inversion routines and provide a low-frequency part of the amplitude spectrum. Eventually, seismic inversion methods could not be performed in the areas without well-log data, such as deep sea areas. In such cases, pseudo-well logs derived from pre-stack seismic data are a solution. Pseudo-well generation is a title used to estimate the elastic parameters of sediments in areas, such as deep marine environments, where drilled wells are absent or sparse. Metaheuristic optimization algorithms are suitable tools for minimizing the cost function as they best match real and synthetic seismic data. In this study, the SEAM earth model has been used as a reference to investigate the quality of pseudo-well generation utilizing a simulated annealing (SA) algorithm as an optimization method of property model change, which minimizes the cost function of seismic inversion. As a result, considering an initial model type of the SEAM model, simultaneous seismic inversion introduced the best compressional and shear wave velocities and density logs, which provide the best real and synthetic seismic data match when synthetic data is calculated from the simplified Zoeppritz equation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

Khah,

Salehi,

Kianoush

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Non-hierarchical cluster analysis was used for assisting permeability prediction with transforming the well logs into electrofacies in dolomite and sandstone intervals in the Ogallah Field, USA [ 10 ], specifying the facies for a well in sandstone formation in West Africa before predicting the formation permeability [ 11 ], and the identification of heterogeneous carbonate reservoirs in a Southern Iraqi oilfield [ 12 ]. Other recent well log applications include improved electrofacies identification and lithology classification [ 13 , 14 ], assisting pseudo-well stochastic seismic inversion [ 15 ], automated layer-thickness determination for inversion procedures and estimation of typical log response values of hydrocarbon formations [ 16 ], clustering of incomplete core laboratory datasets [ 17 ], sweet spot identification and separation of different gas-bearing intervals in unconventional reservoirs [ [18] , [19] , [20] ]. As new alternative, machine learning tools can help to solve geophysical inverse problems.…”

Section: Introductionmentioning

confidence: 99%

Robust reservoir identification by multi-well cluster analysis of wireline logging data

Szabó

Kilik

Dobróka

2023

Heliyon

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“…These methods are based on optimization procedures and strongly depend on computational resources. More efficient techniques based on stochastic Bayesian formulations using Monte Carlo methods have been proposed in recent publications to estimate the posterior distribution of reservoir properties conditioned to geophysical data (de Figueiredo et al, 2017;Yadav et al, 2022). Simulated annealing can assemble to the global minimum, and because of so many perturbations during the optimization process, the starting point has a negligible effect on the final answer (Mahmoodpour and Masihi, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Acoustic impedance (AI), as a process of water saturation and porosity, is defined by a seconddegree polynomial equation used as a forward problem (Babasafari et al, 2021;Guo et al, 2020;Hosseini et al, 2023c;Hosseini et al, 2023a;Hosseini et al, 2023b;Kianoush et al, 2022b). Yadav et al (2022) Yadav et al (2022) applied the modified pseudo-well stochastic seismic inversion method on seismic data from the deep-water Krishna-Godavari basin to better demonstrate the characterization of the reserve's thin shale layers. Sun et al (2023) and Kianoush et al (2023c) presented an intelligent AVA inversion technique utilizing a convolutional neural network trained by realistic pseudo-well logs; results have the potential to reduce interparameter crosstalk artifacts.…”

Section: Introductionmentioning

confidence: 99%

Estimation of elastic properties of sediments utilizing simulated annealing algorithm – a pseudo-wells generation approach

Khah,

Salehi,

Kianoush

et al. 2023

Preprint

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The gas hydrate distribution model containing cement, or un-cement models, considerably affects elastic properties such as shear and bulk modulus. Thus, determining the distribution model in the hydrate-bearing sediments requires decreasing uncertainty in quantitative studies based on seismic velocities. Pre-stack seismic data include additional fluid information than post-stack seismic data, and the pre-stack inversion procedure has influential benefits such as stable outcomes, high resolution, and strong manageability. The amplitude-variation-with-angle (AVA) inversion along the bottom simulating reflector (BSR) utilizing compressional and Shear wave pre-stack analysis could be involved to evaluate hydrate and gas saturations at the pseudo well, especially in accretionary prism structures. It could change unconsolidated sediments' elastic properties and produce sharp bottom simulating. Pseudo-well generation is a title for estimating the elastic parameters of sediments in areas such as deep marine environments where drilled wells are absent (or sparse). This seismic angle-gather inversion problem has complex space and several local and global optimum solutions. In such cases, metaheuristic algorithms are a suitable tool for problem-solving. In this paper, the metaheuristic Simulated Annealing algorithm has been investigated, and the developed optimized algorithm has been presented. In order to analyze the performance of the algorithm, pre-stack seismic data is utilized as a benchmark. The conceptual model of this data includes the elastic parameter logs, which makes it an appropriate option for analyzing the performance and accuracy of the algorithm. According to the results, the generation of pseudo-wells with the algorithm mentioned earlier can be used to characterize hydrates-bearing sediments.

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Agglomerative clustering to improve the resolution of pseudo well stochastic seismic inversion: A case study

Cited by 5 publications

References 22 publications

Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method

Robust reservoir identification by multi-well cluster analysis of wireline logging data

Estimation of elastic properties of sediments utilizing simulated annealing algorithm – a pseudo-wells generation approach

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