A rocky marriage: Carbonate reservoir quality prediction by combining 3D seismic acoustic impedance and diagenetic facies maps in Ghawar Field

2004 ◽  
Author(s):  
Shiv N. Dasgupta ◽  
Rami A. Kamal
Keyword(s):  
Acoustic Impedance ◽  
Carbonate Reservoir ◽  
Reservoir Quality ◽  
Quality Prediction ◽  
3D Seismic ◽  
Diagenetic Facies ◽  
Ghawar Field

Dolomitization geometry and reservoir quality from supervised Bayesian classification and probabilistic neural networks: Midland Basin Leonardian Wichita and Clear Fork Formations

2021 ◽  
Vol 9 (2) ◽  
pp. T585-T598
Author(s):  
Abidin B. Caf ◽  
John D. Pigott
Keyword(s):  
Neural Networks ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
A Priori ◽  
Bayesian Classification ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Probabilistic Neural Networks ◽  
Neutron Porosity ◽  
Seismic Scale

Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian Basin. Early workers have found that the platform and shelf-top carbonates were dolomitized, whereas slope and basinal carbonates remained calcitic, proposing a reflux dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and although exhibiting high resolution vertically, such determinations are laterally sparse. We have used supervised Bayesian classification and probabilistic neural networks (PNN) on a 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity, we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well-log crossplots of neutron porosity and acoustic impedance to determine the a priori proportions of the lithology and the probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of the acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. The acoustic impedance volume was obtained by model-based poststack inversion, and the neutron porosity volume was obtained by the PNN. Our results best supported a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while the dolomitization is decreasing, but with sea-level forcing. With this study, we determined that diagenesis and the corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

scholarly journals Pore Type and Porosity Distribution of Carbonate Reservoir Based on 3D Seismic Inversion in “P” Field Salawati Basin

2019 ◽  
Vol 125 ◽  
pp. 15002
Author(s):  
Avishena Prananda ◽  
Mohammad Syamsu Rosid ◽  
Robet Wahyu Widodo
Keyword(s):  
Acoustic Impedance ◽  
Carbonate Rock ◽  
Sedimentary Rock ◽  
Seismic Inversion ◽  
Carbonate Reservoir ◽  
High Porosity ◽  
3D Seismic ◽  
Impedance Distribution ◽  
Pore Types ◽  
Type Classification

Overall, carbonate rock has complex and more heterogeneous physical characteristic, compared to siliciclastic sedimentary rock. One parameter, which distinguishes carbonate rock and siliciclastic is pore structure/pore type. The heterogeneity and complexity of carbonate reservoir pore type are affected by the sedimentation process and the diagenesis process. Pore type classification is divided into three: interparticle, stiff, and crack. Therefore, carbonate pore type determination becomes important to enhance drilling success. This paper explains pore types prediction, porosity, and acoustic impedance on carbonate reservoir. The Differential Effective Medium (DEM) method to analyze carbonate reservoir pore type has been applied. DEM method generates bulk and shear modulus parameters to create carbonate Vp and Vs model based on pore type. We also do a 3D seismic inversion to create acoustic impedance distribution, porosity, and pore type. Afterward, we made cube porosity and pore type cube by using geostatistics method to provide a better result. Moreover, this study shows low impedance value correlates with high porosity value and enhancement of porosity value correlates with crack and interparticle pore type on “P” field, Salawati Basin.

Identifying Deep Sweetspots: Seismically Constrained Reservoir Quality Prediction Offshore Brunei

2016 ◽  
Author(s):  
Stephan Gelinsky ◽  
Sze-Fong Kho ◽  
Irene Espejo ◽  
Matthias Keym ◽  
Jochen Näth ◽  
...  
Keyword(s):  
Reservoir Quality ◽  
Quality Prediction

3D Seismic attribute optimization technology and application for dissolution caved carbonate reservoir prediction

2011 ◽  
Author(s):  
Lifeng Liu ◽  
Sam Zandong Sun ◽  
Haiyang Wang ◽  
Haijun Yang ◽  
Jianfa Han ◽  
...  
Keyword(s):  
Carbonate Reservoir ◽  
3D Seismic ◽  
Reservoir Prediction ◽  
Seismic Attribute

Formation Evaluation While Drilling Of a Complex Khuff-C Carbonate Reservoir in Ghawar Field, Saudi Arabia

2007 ◽  
Author(s):  
Abdullah Abdulrahman Al-Fawwaz ◽  
Nedhal Mohamed Al-Musharfi ◽  
Parvez Jamil Butt ◽  
Abdul Fareed
Keyword(s):  
Saudi Arabia ◽  
Carbonate Reservoir ◽  
Formation Evaluation ◽  
Ghawar Field

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

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