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

Validated artificial neural networks in determining petrophysical properties: A case study from Colombia

2018 ◽  
Vol 6 (4) ◽  
pp. T1067-T1080 ◽  
Author(s):  
Ursula Iturrarán-Viveros ◽  
Andrés M. Muñoz-García ◽  
Jorge O. Parra ◽  
Josué Tago
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seismic Attributes ◽  
P Wave ◽  
Well Log ◽  
Seismic Line ◽  
Facies Classification ◽  
Artificial Neural ◽  
Cubic Polynomials ◽  
Seismic Scale

We have applied instantaneous seismic attributes to a stacked P-wave reflected seismic section in the Tenerife field located in the Middle Magdalena Valley Basin in Colombia to estimate the volume of clay [Formula: see text] and the density [Formula: see text] at seismic scale. The well logs and the seismic attributes associated to the seismic trace closer to one of the available wells (Tenerife-2) is the information used to train some multilayered artificial neural networks (ANN). We perform data analysis via the gamma test, a mathematically nonparametric nonlinear smooth modeling tool, to choose the best input combination of seismic attributes to train ANNs to estimate [Formula: see text] and [Formula: see text]. Once the ANNs are trained, they are applied to predict these parameters along the seismic line. From the continuous estimations of [Formula: see text], we distinguish two facies: sands for [Formula: see text] and shales when [Formula: see text]. These estimations confirm the production of the Mugrosa C-Sands zone, and we draw the brown shale that correlates with the high-amplitude attributes and the yellow sand that correlates with the low-amplitude attributes. Using the well-log information for [Formula: see text] and the facies classification (also in the well log), two cubic polynomials that depend on time (or depth) are obtained, one for sands and the other for shales, to fit the [Formula: see text]. These two cubic polynomials and the facies classification obtained from the [Formula: see text] at the seismic scale enable us to estimate [Formula: see text] at the seismic scale. To validate the 2D [Formula: see text] and [Formula: see text] predicted data, a forward-modeling software (the Kennett reflectivity algorithm) is used. This model calculates synthetic seismograms that are compared with the real seismograms. This comparison indicates a small misfit that suggests that the [Formula: see text] and [Formula: see text] images are representing the reservoir description characteristics and the ANN method is accurate to map these parameters.

scholarly journals Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

2021 ◽  
Vol 44 ◽  
Author(s):  
Guilherme Loriato Potratz ◽  
Smith Washington Arauco Canchumuni ◽  
Jose David Bermudez Castro ◽  
Júlia Potratz ◽  
Marco Aurélio C. Pacheco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Unsupervised Classification ◽  
Machine Learning Algorithms ◽  
Hydrocarbon Exploration ◽  
Data Set ◽  
Log Data ◽  
Hidden Patterns ◽  
Artificial Neural

One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

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