Reservoir modeling. Automatic well log data correlation using "AutoCorr" software

2006 ◽  
Author(s):  
Igor S. Gutman ◽  
Olga Kuznetsova ◽  
Ivan Bulaban ◽  
Vladimir Stazovezov
Keyword(s):  
Reservoir Modeling ◽  
Well Log ◽  
Data Correlation ◽  
Log Data

Reservoir Modeling – Automatic Well Log Data Correlation Using "AutoCorr" Software (Russian)

2006 ◽  
Author(s):  
Igor Gutman ◽  
Ivan Balaban ◽  
Galina Kuznetsova ◽  
Vladimir Staroverov
Keyword(s):  
Reservoir Modeling ◽  
Well Log ◽  
Data Correlation ◽  
Log Data

A new approach to improve reservoir modeling via machine learning

2020 ◽  
Vol 39 (3) ◽  
pp. 170-175
Author(s):  
Islam A. Mohamed ◽  
Adel Othman ◽  
Mohamed Fathy
Keyword(s):  
Nile Delta ◽  
Modeling Method ◽  
Reservoir Modeling ◽  
Early Model ◽  
Well Log ◽  
Well Test ◽  
Reservoir Properties ◽  
New Approach ◽  
Log Data ◽  
Simulation Process

In highly heterogeneous basins with complex subsurface geology, such as the Nile Delta Basin, accurate prediction of reservoir modeling has been a challenge. Reservoir modeling is a continuous process that begins with field discovery and ends with the last phases of production and abandonment. Currently, the stochastic reservoir modeling method is widely used instead of the traditional deterministic modeling method to consider spatial statistics and uncertainties. However, the modeling workflow is demanding and slow, typically requiring months from the initial model concept to flow simulation. In addition, errors from early model stages become cumulative and are difficult to change retroactively. To overcome these limitations, a new workflow is proposed that implements probabilistic neural network inversion to predict reservoir properties. First, well-log data were conditioned properly to match the seismic data scale. Then, the networks were trained and validated, using the conditioned well-log data and seismic internal/external attributes, to predict water saturation and effective porosity 3D volumes. The resulting volumes were sampled in simulation 3D grids and tested using a blind well test. Subsequently, the permeability was calculated from a porosity-permeability relationship inside the reservoir. Finally, a dynamic simulation project of the field was performed in which the historical field production and pressures were compared to the predicted values. One of the Pliocene deepwater turbidite reservoirs in the offshore Nile Delta was used to demonstrate the proposed approach. The results proved the accuracy of the model in predicting the reservoir properties and honoring the heterogeneity of the reservoir. The new approach represents a shortcut for the seismic-to-simulation process, providing a reliable and fast way of constructing a reservoir model and making the seismic-to-simulation process easier.

Static reservoir modeling of El Wastani formation, for justifying development plans, using 2D seismic and well log data in Scarab field, offshore Nile Delta, Egypt

2019 ◽  
Vol 158 ◽  
pp. 103546
Author(s):  
Esam A. Abd El-Gawad ◽  
Mohammad A. Abdelwahhab ◽  
Mahmoud H. Bekiet ◽  
Ahmed Z. Noah ◽  
Nahla A. ElSayed ◽  
...  
Keyword(s):  
Nile Delta ◽  
Reservoir Modeling ◽  
Well Log ◽  
Log Data ◽  
Development Plans

scholarly journals Wave Propagation Characteristics in Gas Hydrate-Bearing Sediments and Estimation of Hydrate Saturation

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 804
Author(s):  
Lin Liu ◽  
Xiumei Zhang ◽  
Xiuming Wang
Keyword(s):  
Gas Hydrate ◽  
Clay Content ◽  
Clean Energy ◽  
Compressional Wave ◽  
Physical Parameters ◽  
Well Log ◽  
Log Data ◽  
Phase Velocities ◽  
Hydrate Saturation ◽  
Hydrate Bearing Sediments

Natural gas hydrate is a new clean energy source in the 21st century, which has become a research point of the exploration and development technology. Acoustic well logs are one of the most important assets in gas hydrate studies. In this paper, an improved Carcione–Leclaire model is proposed by introducing the expressions of frame bulk modulus, shear modulus and friction coefficient between solid phases. On this basis, the sensitivities of the velocities and attenuations of the first kind of compressional (P1) and shear (S1) waves to relevant physical parameters are explored. In particular, we perform numerical modeling to investigate the effects of frequency, gas hydrate saturation and clay on the phase velocities and attenuations of the above five waves. The analyses demonstrate that, the velocities and attenuations of P1 and S1 are more sensitive to gas hydrate saturation than other parameters. The larger the gas hydrate saturation, the more reliable P1 velocity. Besides, the attenuations of P1 and S1 are more sensitive than velocity to gas hydrate saturation. Further, P1 and S1 are almost nondispersive while their phase velocities increase with the increase of gas hydrate saturation. The second compressional (P2) and shear (S2) waves and the third kind of compressional wave (P3) are dispersive in the seismic band, and the attenuations of them are significant. Moreover, in the case of clay in the solid grain frame, gas hydrate-bearing sediments exhibit lower P1 and S1 velocities. Clay decreases the attenuation of P1, and the attenuations of S1, P2, S2 and P3 exhibit little effect on clay content. We compared the velocity of P1 predicted by the model with the well log data from the Ocean Drilling Program (ODP) Leg 164 Site 995B to verify the applicability of the model. The results of the model agree well with the well log data. Finally, we estimate the hydrate layer at ODP Leg 204 Site 1247B is about 100–130 m below the seafloor, the saturation is between 0–27%, and the average saturation is 7.2%.

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