The Technique of Seismic Inversion and Use of the Relation Between Inversion Results and Porosity Log for Predicting Porosity of a Carbonate Reservoir in a South Iranian Oil Field

Jalalalhosseini, S. M.; Eskandari, Somayeh; Mortezazadeh, Emad

doi:10.1080/15567036.2011.580326

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Then, the acoustic impedance values are gradually modified until the synthetic seismic trace approaches the original seismic trace, i.e., within the acceptable limits set by the user. The model-based inversion is widely used, and several recent articles have been published for characterization of carbonate reservoirs ( Jalalalhosseini et al 2015, Al-Rahim and Hashem 2016, Pramudito et al 2017, Ferreira and Lupinacci 2018, as well as for siliciclastic ones (Maurya andSingh 2015, Karin et al 2016).…”

Section: Methodsmentioning

confidence: 99%

An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

et al. 2020

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The main reservoirs in the Linguado Field are grainstone and packstone carbonates composed of oolites, oncolites, pelloids, and rare bioclasts, of the Quissamã Formation (Albian-age) and coquinas of the Coqueiros Formation (Aptian-age). Within Quissamã Formation, facies have high porosity variations and their 3D characterization is of utter importance to allow for a better location of the wells. The objective of this study was a volumetric characterization of Quissamã Formation by means of an adapted classical workflow composed of well-seismic tie, seismic preconditioning, seismic interpretation, seismic inversion, petrophysical properties estimation, and porosity geostatistical modeling. Seismic inversion results allowed the interpretation of a horizon to separate the carbonate platform in an upper zone, with low acoustic impedance, from a lower zone, with high acoustic impedance. Finally, behavioral knowledge of acoustic impedance and the porosity distribution within the Albian carbonate platform helped to better characterize the distribution of carbonaceous facies allowing the confirmation that commercial wells were drilled in low acoustic impedance and high porosity regions. It was also possible to confirm that a well, declared as noncommercial, was drilled in a poor reservoir region with high acoustic impedance and low porosity and identified other locations that were not yet drilled but could represent potential targets.

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Section: Methodsmentioning

confidence: 99%

An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

et al. 2020

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“…Modern three-dimensional (3D) seismic data assists not only in delineating reservoir geometry but also in predicting petrophysical properties (thickness, porosity, and lithology) with variations away from those of the well control [1]. The geologic model derived from combining the 3D seismic survey with available well data provides a degree of detail about the depositional environments of the producing reservoir that cannot be obtained from well data alone [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Depositional Environments of Mishrif Formation from Seismic Isopach Map in the Dujaila Oil Field, Southeast-Iraq

Khawaja

Thabit

2021

eijs

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In this paper, we attempt to predict the depositional environments with associated lithofacies of the main reservoir of the late Cretaceous Mishrif carbonate Formation, depending on the analysis of the created seismic isopach map by integrating seismic and well data. The isopach map was created from a 3D-seismic reflection survey carried out at the Dujaila oil field in southeastern Iraq, which is of an area of 602.26 Km2, and integrated with the data of the two explored wells. Based on the interpretation of the seismic isopach map, the diagram of the 3D-depositional environment model of Mishrif Formation was constructed. It showed three distinguished depositional environments, which were graduated from a back reef lithofacies of a shallow open marine (shelf) environment in the west and NW, to a shoal environment of isolated Rudist reefal buildup in the middle, and a fore reef lithofacies of the deep open marine basin environment in the SE of the field. A 3D-instantaneous frequency model was generated to verify the capability of the seismic isopach map of predicting the depositional environments, which in turn showed that the low frequency was restricted in the region of the high thickness of Rudist reefal buildups (porous reservoir facies) in the vicinity of the productive well Dujaila-1.

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“…Traditional methods use rock physics and geostatistics to achieve reservoir characterization and prediction (Jalalalhosseini et al 2014(Jalalalhosseini et al , 2015Liu et al 2018). Nowadays, machine learning has attracted wide attention in geoscience because of its advantages in addressing big data issues (e.g., Huang et al 2016;Chen 2017Chen , 2018Chen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Lithofacies identification using support vector machine based on local deep multi-kernel learning

et al. 2020

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Lithofacies identification is a crucial work in reservoir characterization and modeling. The vast inter-well area can be supplemented by facies identification of seismic data. However, the relationship between lithofacies and seismic information that is affected by many factors is complicated. Machine learning has received extensive attention in recent years, among which support vector machine (SVM) is a potential method for lithofacies classification. Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem, which needs to be solved by means of the kernel function. Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification. However, it is very difficult to determine the kernel function and the parameters, which is restricted by human factors. Besides, its computational efficiency is low. A lithofacies classification method based on local deep multi-kernel learning support vector machine (LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed. The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information. The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification. Both the model data test results and the field data application results certify advantages of the method. This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.

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The Technique of Seismic Inversion and Use of the Relation Between Inversion Results and Porosity Log for Predicting Porosity of a Carbonate Reservoir in a South Iranian Oil Field

Cited by 8 publications

References 9 publications

An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

Predicting the Depositional Environments of Mishrif Formation from Seismic Isopach Map in the Dujaila Oil Field, Southeast-Iraq

Lithofacies identification using support vector machine based on local deep multi-kernel learning

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