Improving porosity and gamma ray prediction for the Middle Jurassic Hugin sandstones in southern Norwegian North Sea with the application of deep neural networks

2021 ◽  
pp. 1-46
Author(s):  
Satinder Chopra ◽  
Ritesh Sharma ◽  
Kurt J. Marfurt ◽  
Rongfeng Zhang ◽  
Renjun Wen
Keyword(s):  
Neural Network ◽  
North Sea ◽  
Seismic Data ◽  
Deep Neural Network ◽  
Gamma Ray ◽  
Accurate Determination ◽  
Empirical Relation ◽  
Reservoir Property ◽  
Well Data

The complete characterization of a reservoir requires accurate determination of properties such as porosity, gamma ray and density, amongst others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. Generally, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray suggesting large uncertainty in the determined relationship. Although for many rocks there is a well established petrophysical model correlating P-impedance to porosity, there is not a comparable model correlating P-impedance to gamma ray. To address this issue, interpreters can use crossplots to graphically correlate two seismically derived variables to well measurements plotted in color. When there are more than two seismically derived variables, the interpreter can use multilinear regression or artificial neural network (ANN) analysis that uses a percentage of the upscaled well data for training to establish an empirical relation with the input seismic data and then uses the remaining well data to validate the relationship. Once validated at the wells, this relationship can then be used to predict the desired reservoir property volumetrically. We describe the application of deep neural network (DNN) analysis for the determination of porosity and gamma ray over the Volve Field in the southern Norwegian North Sea. After employing several quality-control steps in the deep neural network workflow and observing encouraging results, we validate the final prediction of both porosity and gamma ray properties using blind well correlation. The application of this workflow promises significant improvement to the reservoir property determination for fields that have good well control and exhibit lateral variations in the sought properties.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals A Comparison of Machine Learning Approaches for Prediction of Permeability using Well Log Data in the Hydrocarbon Reservoirs

2021 ◽  
Vol 2 (2) ◽  
pp. 82-99
Author(s):  
Mohsen Talebkeikhah ◽  
Zahra Sadeghtabaghi ◽  
Mehdi Shabani
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Gamma Ray ◽  
Accurate Determination ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Learning Approaches ◽  
Statistical Parameters ◽  
Southwest Of Iran ◽  
Gamma Ray Log

Permeability is a vital parameter in reservoir engineering that affects production directly. Since this parameter's significance is obvious, finding a way for accurate determination of permeability is essential as well. In this paper, the permeability of two notable carbonate reservoirs (Ilam and Sarvak) in the southwest of Iran was predicted by several different methods, and the level of accuracy in all models was compared. For this purpose, Multi-Layer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBF), Support Vector Regression (SVR), decision tree (DT), and random forest (RF) methods were chosen. The full set of real well-logging data was investigated by random forest, and five of them were selected as the potent variables. Depth, Computed gamma-ray log (CGR), Spectral gamma-ray log (SGR), Neutron porosity log (NPHI), and density log (RHOB) were considered efficacious variables and used as input data, while permeability was considered output. It should be noted that permeability values are derived from core analysis. Statistical parameters like the coefficient of determination ( ), root mean square error (RMSE) and standard deviation (SD) were determined for the train, test, and total sets. Based on statistical and graphical results, the SVM and DT models perform more accurately than others. RMSE, SD and R2values of SVM and DT models are 0.38, 1.63, 0.97 and 0.44, 2.89, and 0.96 respectively. The results of the best-proposed models of this paper were then compared with the outcome of the empirical equation for permeability prediction. The comparison indicates that artificial intelligence methods perform more accurately than traditional methods for permeability estimation, such as proposed equations. Doi: 10.28991/HEF-2021-02-02-01 Full Text: PDF

Extensive, Gas-charged Quaternary Sand Accumulations of the Northern North Sea and North Sea Fan

2020 ◽  
Author(s):  
Benjamin Bellwald ◽  
Sverre Planke ◽  
Sunil Vadakkepuliyambatta ◽  
Stefan Buenz ◽  
Christine Batchelor ◽  
...  
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Data Sets ◽  
3D Seismic ◽  
Fine Grained ◽  
The North ◽  
Northern North Sea ◽  
Well Data ◽  
Sea Fan ◽  
3D Seismic Data

<p>Sediments deposited by marine-based ice sheets are dominantly fine-grained glacial muds, which are commonly known for their sealing properties for migrating fluids. However, the Peon and Aviat hydrocarbon discoveries in the North Sea show that coarse-grained glacial sands can occur over large areas in formerly glaciated continental shelves. In this study, we use conventional and high-resolution 2D and 3D seismic data combined with well information to present new models for large-scale fluid accumulations within the shallow subsurface of the Norwegian Continental Shelf. The data include 48,000 km<sup>2</sup> of high-quality 3D seismic data and 150 km<sup>2</sup> of high-resolution P-Cable 3D seismic data, with a vertical resolution of 2 m and a horizontal resolution of 6 to 10 m in these data sets. We conducted horizon picking, gridding and attribute extractions as well as seismic geomorphological interpretation, and integrated the results obtained from the seismic interpretation with existing well data.</p><p>The thicknesses of the Quaternary deposits vary from hundreds of meters of subglacial till in the Northern North Sea to several kilometers of glacigenic sediments in the North Sea Fan. Gas-charged, sandy accumulations are characterized by phase-reserved reflections with anomalously high amplitudes in the seismic data as well as density and velocity decreases in the well data. Extensive (>10 km<sup>2</sup>) Quaternary sand accumulations within this package include (i) glacial sands in an ice-marginal outwash fan, sealed by stiff glacial tills deposited by repeated glaciations (the Peon discovery in the Northern North Sea), (ii) sandy channel-levee systems sealed by fine-grained mud within sequences of glacigenic debris flows, formed during shelf-edge glaciations, (iii) fine-grained glacimarine sands of contouritic origin sealed by gas hydrates, and (iv) remobilized oozes above large evacuation craters and sealed by megaslides and glacial muds. The development of the Fennoscandian Ice Sheet resulted in a rich variety of depositional environments with frequently changing types and patterns of glacial sedimentation. Extensive new 3D seismic data sets are crucial to correctly interpret glacial processes and to analyze the grain sizes of the related deposits. Furthermore, these data sets allow the identification of localized extensive fluid accumulations within the Quaternary succession and distinguish stratigraphic levels favorable for fluid accumulations from layers acting as fluid barriers.</p>

High-resolution reservoir characterization by an acoustic impedance inversion of a Tertiary deltaic clinoform system in the North Sea

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. O57-O67 ◽  
Author(s):  
Daria Tetyukhina ◽  
Lucas J. van Vliet ◽  
Stefan M. Luthi ◽  
Kees Wapenaar
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Sampling Rate ◽  
Seismic Inversion ◽  
Acoustic Properties ◽  
The North ◽  
The North Sea ◽  
Well Data

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface, and potential reservoir units are often close to or below seismic resolution. For seismic inversion, it is therefore important to integrate the seismic data with higher resolution constraints obtained from well logs, whereby not only the acoustic properties are used but also the detailed layering characteristics. We have applied two inversion approaches for poststack, time-migrated seismic data to a clinoform sequence in the North Sea. Both methods are recursive trace-based techniques that use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and uses horizontal and vertical regularization parameters for lateral propagation. The first method has a low level of parameterization embedded in a geologic framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geologic settings of the reservoir; however, there is no explicit geologic significance and the method is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data.

Sign in / Sign up

Export Citation Format

Share Document