The oil prospecting using seismic attributes as predictors of reservoir properties and fluid saturation

1997 ◽  
Author(s):  
Miron B. Rapoport ◽  
Valery I. Ryjkov ◽  
Larisa I. Rapoport ◽  
Vladislav E. Parnikel ◽  
Valentin A. Kateli ◽  
...  
Keyword(s):  
Seismic Attributes ◽  
Reservoir Properties ◽  
Fluid Saturation

Optimal dynamic rock-fluid physics template validated by petroelastic reservoir modeling

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. O45-O58 ◽  
Author(s):  
Alireza Shahin ◽  
Robert Tatham ◽  
Paul Stoffa ◽  
Kyle Spikes
Keyword(s):  
Sensitivity Analysis ◽  
Acoustic Impedance ◽  
Velocity Ratio ◽  
Rapid Change ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Reservoir Modeling ◽  
Reservoir Model ◽  
Reservoir Properties ◽  
Fluid Saturation

Separation of fluid pore pressure and saturation using inverted time-lapse seismic attributes is a mandatory task for field development. Multiple pairs of inversion-derived attributes can be used in a crossplot domain. We performed a sensitivity analysis to determine an optimal crossplot, and the validity of the separation is tested with a comprehensive petroelastic reservoir model. We simulated a poorly consolidated shaly sandstone reservoir based on a prograding near-shore depositional environment. A model of effective porosity is first simulated by Gaussian geostatistics. Well-known theoretical and experimental petrophysical correlations were then efficiently combined to consistently simulate reservoir properties. Next, the reservoir model was subjected to numerical simulation of multiphase fluid flow to predict the spatial distributions of fluid saturation and pressure. A geologically consistent rock physics model was then used to simulate the inverted seismic attributes. Finally, we conducted a sensitivity analysis of seismic attributes and their crossplots as a tool to discriminate the effect of pressure and saturation. The sensitivity analysis demonstrates that crossplotting of acoustic impedance versus shear impedance should be the most stable way to separate saturation and pressure changes compared to other crossplots (e.g., velocity ratio versus acoustic impedance). We also demonstrated that the saturation and pressure patterns were detected in most of the time-lapse scenarios; however, the saturation pattern is more likely detectable because the percentage in pressure change is often lower than that of the saturation change. Imperfections in saturation and pressure patterns exist in various forms, and they can be explained by the interaction of saturation and pressure, the diffusive nature of pressure, and rapid change in pressure due to production operations.

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.

Experimental Determination of the Shale Wettability

2020 ◽  
Author(s):  
Aliya Mukhametdinova ◽  
Natalia Bogdanovich ◽  
Alexey Cheremisin ◽  
Svetlana Rudakovskaya
Keyword(s):  
Contact Angle ◽  
Organic Content ◽  
Wetting Angle ◽  
Reservoir Rock ◽  
Initial Assessment ◽  
Reservoir Properties ◽  
Laboratory Methods ◽  
Thin Sections ◽  
Mean Values ◽  
Fluid Saturation

<p>In recent years, the share of unconventional reserves in global oil production has grown. Exploration and development of unconventional resources require novel effective laboratory methods for characterizing the reservoir properties. The study and analysis of local shale deposits such as Bazhenov Formation (BF) in Western Siberia is a priority among non-traditional reservoirs. Wettability of the reservoir rock is one of the most important factors affecting the residual saturation and filtration properties in the formation. However, as multiple petrophysical studies show, conventional laboratory methods for characterizing the wettability are not applicable for this type of formations.</p><p>In this work, the fluid saturation and wettability of BF rock samples were studied utilizing a nuclear magnetic resonance (NMR) and the method of determining the wetting contact angle by a surface drop. We have provided the petrographic description of rocks using ultrathin sections for grouping the samples. In addition, we used data on the organic content (TOC) obtained by the Rock-Eval method on a HAWK RW instrument (Wildcat Technologies) and the results of lithological typing on thin sections using an Axio Imager A2m polarizing microscope (Carl Zeiss) for detailed analysis of NMR and contact angle methods results.</p><p>To assess wettability by NMR, T2 relaxation curves were constructed for extracted (cleaned), kerosene-saturated and water-saturated samples. A comparison of the relaxation spectra for kerosene and water enabled evaluation of the wettability for each by T2 log mean values. The calculation of the wetting angle was carried out for samples before and after the extraction, which revealed minor changes in the nature of the rock wettability because of cleaning. Thus, for this type of rock, the drop method for determining wettability turned out to be significantly sensitive to the shape of the OM distribution in the rock. Correlations built on wettability (by NMR results and calculated wetting angle) vs. TOC and lithotyping illustrated the dependence of rock wettability behavior on both the lithotype and the TOC content.</p><p>The calculation of the wetting angle provided an initial assessment of the surface wettability of the rock and made it possible to establish the relationship between the wetting angle and the content of organic carbon (TOC), which is relevant for BF rocks. The lithological description of thin sections was used to highlight groups with a similar wettability of the rock. For the integral characteristics of the samples wettability, the NMR relaxometry method was proposed.</p>

scholarly journals Biot's equations-based reservoir parameter inversion using deep neural networks

2021 ◽  
Vol 18 (6) ◽  
pp. 862-874
Author(s):  
Fansheng Xiong ◽  
Heng Yong ◽  
Hua Chen ◽  
Han Wang ◽  
Weidong Shen
Keyword(s):  
High Efficiency ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Attributes ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Shear Moduli ◽  
Parameter Inversion ◽  
Reservoir Parameter ◽  
Biot’S Equations

Abstract Reservoir parameter inversion from seismic data is an important issue in rock physics. The traditional optimisation-based inversion method requires high computational expense, and the process exhibits subjectivity due to the nonuniqueness of generated solutions. This study proposes a deep neural network (DNN)-based approach as a new means to analyse the sensitivity of seismic attributes to basic rock-physics parameters and then realise fast parameter inversion. First, synthetic data of inputs (reservoir properties) and outputs (seismic attributes) are generated using Biot's equations. Then, a forward DNN model is trained to carry out a sensitivity analysis. One can in turn investigate the influence of each rock-physics parameter on the seismic attributes calculated by Biot's equations, and the method can also be used to estimate and evaluate the accuracy of parameter inversion. Finally, DNNs are applied to parameter inversion. Different scenarios are designed to study the inversion accuracy of porosity, bulk and shear moduli of a rock matrix considering that the input quantities are different. It is found that the inversion of porosity is relatively easy and accurate, while more information is needed to make the inversion more accurate for bulk and shear moduli. From the presented results, the new approach makes it possible to realise accurate and pointwise inverse modelling with high efficiency for actual data interpretation and analysis.

Improving the translation of 4D seismic attributes into reservoir properties

2007 ◽  
Author(s):  
Cassiane Maria Ferreira Nunes* ◽  
Lúcia Duarte Dillon e Guenther Schwedersky Neto
Keyword(s):  
Seismic Attributes ◽  
Reservoir Properties ◽  
4D Seismic

Estimation of Reservoir Properties from Seismic Attributes and Well Log Data Using Artificial Neural Networks

2010 ◽  
Author(s):  
Mohamed Sitouah ◽  
Gabor Korvin ◽  
Abdulatif Al-Shuhail ◽  
Osman MAbdullatif ◽  
Abdulazeez Abdulraheem ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seismic Attributes ◽  
Well Log ◽  
Reservoir Properties ◽  
Log Data ◽  
Artificial Neural

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

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