Implementation of New Seismic Attributes to Improve Reservoir Properties Prediction Using Probability Neural Network

2016 ◽  
Author(s):  
Maman Hermana ◽  
D. P. Ghosh ◽  
C. W. Sum ◽  
A. M. A. Salim
Keyword(s):  
Neural Network ◽  
Seismic Attributes ◽  
Reservoir Properties ◽  
Probability Neural Network

Comparison of linear regression and a probabilistic neural network to predict porosity from 3‐D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1349-1358 ◽  
Author(s):  
Daniel J. Leiphart ◽  
Bruce S. Hart
Keyword(s):  
Neural Network ◽  
New Mexico ◽  
Linear Regression ◽  
Regression Model ◽  
Seismic Data ◽  
Time Window ◽  
Probabilistic Neural Network ◽  
Seismic Attributes ◽  
Oil Field ◽  
Reservoir Properties

The Lower Brushy Canyon Formation of the Delaware Basin, New Mexico, consists of a series of overlying sand‐filled channels and associated fans separated by laterally extensive organic siltstone and carbonate interbeds. This laterally and vertically complex geology creates the need for precise interwell estimation of reservoir properties. In this paper we integrate wireline log and 3‐D seismic data to directly predict porosity in the area of an existing oil field in southeast New Mexico. The 3‐D seismic data were used to interpret the location of major stratigraphic markers between wells, and these seismic horizons were used to constrain a time window for a volume‐based attribute analysis. Stepwise regression and crossvalidation were used to combine seismic attributes to predict porosity in wells where the porosity was known from the well logs. The results of a linear regression porosity model showed good correlation [Formula: see text] between seven seismic attributes and the observed porosity logs at 11 wells in the study area, but the porosity volume created from the regression model did not display the known geologic features. A probabilistic neural network was then trained to look for a nonlinear relationship between the input data (the seven attributes) and the observed porosity at the 11 wells. The correlation was better [Formula: see text], but the biggest improvement over the linear regression model came in the more geologically realistic predicted porosity distribution.

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.

A Proxy Flow Modelling Workflow to Estimate Gridded Dynamic Properties and Well Production Rates by Deep Learning Algorithms

2021 ◽  
Author(s):  
Soumi Chaki ◽  
Yevgeniy Zagayevskiy ◽  
Terry Wong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dynamic Properties ◽  
Reservoir Management ◽  
Flow Modeling ◽  
Static Properties ◽  
Production Rates ◽  
Reservoir Properties ◽  
Well Production ◽  
Reservoir Simulator

Abstract This paper proposes a deep learning-based framework for proxy flow modeling to predict gridded dynamic petroleum reservoir properties (like pressure and saturation) and production rates for wells in a single framework. It approximates the solution of a full physics-based numerical reservoir simulator, but runs much more rapidly, allowing users to generate results for a much wider range of scenarios in a given time than could be done with a full physics simulator. The proxy can be used for reservoir management tasks like history matching, uncertainty quantification, and field development optimization. A deep-learning based methodology for accurate proxy-flow modeling is presented which combines U-Net (a variant of convolutional neural network) to predict gridded dynamic properties and deep neural network (DNN) models to forecast well production rates. First, gridded dynamic properties, such as reservoir pressure and phase saturations, are predicted from static properties like reservoir rock porosity and absolute permeability using a U-Net. Then, the static properties and the dynamic properties predicted by the U-Net are input to a DNN to predict production rates at the well perforations. The inclusion of U-net predicted pressure and saturations improves the quality of the well rate predictions. The proposed methodology is presented with the synthetic Brugge reservoir discretized into grid blocks. The U-Net input consists of three properties: dynamic gridded reservoir properties (such as pressure or fluid saturation) at the current state, static gridded porosity, and static gridded permeability. The U-Net has only one output property, the target gridded property (such as pressure or saturation) at the next time step. Training and testing datasets are generated by running 13 full physics flow simulations and dividing them in a 12:1 ratio. Nine U-Net models are calibrated to predict pressures/saturations, one for each of the nine grid layers present in the Brugge model. These outputs are then concatenated to obtain the complete pressure/saturation model for all nine layers. The constructed U-Net models match the distributions of generated pressures/saturations of the numerical reservoir simulator with a correlation coefficient value of approximately 0.99 and above 95% accuracy. The DNN models approximate well production rates accurately from U-Net predicted pressures and saturations along with static properties like transmissibility and horizontal permeability. For each well and each well perforation, the production rate is predicted with the DNN model. The use of the constructed proxy flow model generates reservoir predictions within a few minutes compared to the hours or days typically taken by a full physics flow simulator. The direct connection that is established between the gridded static and dynamic properties of the reservoir and well production rates using U-Net and DNN models has not been presented previously. Using only a small number of runs for its training, the workflow matches the numerical reservoir simulator results with reduced computational effort. This helps reservoir engineers make informed decisions more quickly, resulting in more efficient reservoir management.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

Lateral prediction of reservoir properties using seismic attributes with well log data

1992 ◽  
Author(s):  
S. Ronen ◽  
M. Hattori ◽  
S. Geoltrain ◽  
P. Schultz
Keyword(s):  
Seismic Attributes ◽  
Well Log ◽  
Reservoir Properties ◽  
Log Data

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
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