Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria

2014 ◽  
Vol 115 ◽  
pp. 78-89 ◽  
Author(s):  
Ahmed Ali Zerrouki ◽  
Tahar Aïfa ◽  
Kamel Baddari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Field ◽  
Natural Fracture ◽  
Well Log ◽  
Fracture Porosity ◽  
Log Data ◽  
Fuzzy Ranking ◽  
Artificial Neural

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Tamer Moussa ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Well Log ◽  
Well Test ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Reservoir Permeability ◽  
Artificial Neural ◽  
Core Permeability

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

Artificial Neural Network for 4D Reservoir Modeling System Design

2012 ◽  
Vol 268-270 ◽  
pp. 1783-1789
Author(s):  
Liu Mei Zhang ◽  
Tian Shi Liu ◽  
Shao Wei Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Area ◽  
Oil Field ◽  
Reservoir Modeling ◽  
Static Structure ◽  
Neural Network Approach ◽  
Modeling Systems ◽  
Artificial Neural ◽  
New Research

In quantitative geological modeling with an artificial neural network approach, time information can be considered as input variable to better describe dynamic evolution patterns of reservoir parameters. Such approach requires independent programming to implements 4D reservoir modeling systems and thus introduces a new research area with great development potential. This paper is dedicated to implement a 4D reservoir modeling system by neural network. It explains the development process with detailed activities vary from determine the execution process and organization structure at one extreme to determine the sequence diagram at the other extreme. Between two extremes besides analyzing essential object class functions, UML modeling language has been used to define the use case model, static structure model and dynamic behavior model. It has practical meaning for further development of eastern oil field in China.

scholarly journals Using Artificial Neural Network to Predict Rate of Penetration from Dynamic Elastic Properties in Nasiriya Oil Field

2020 ◽  
Vol 21 (2) ◽  
pp. 7-14
Author(s):  
Yasser A. Khudhaier ◽  
Fadhil S. Kadhim ◽  
Yousif K. Yousif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Elastic Properties ◽  
Average Rate ◽  
Oil Field ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Dynamic Elastic Properties

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic properties for the studied wells was determined and listed with depth. Laboratory measurements were conducted on core samples selected from two wells from the studied wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. The reason behind that is to check the accuracy of the Greenberg-Castagna equation that was used to estimate the shear wave in order to calculate dynamic elastic properties. The model was built using Artificial Neural Network (ANN) to predict the rate of penetration in Mishrif formation in the Nasiriya oil field for the selected wells. The results obtained from the model were compared with the provided rate of penetration from the field and the Mean Square Error (MSE) of the model was 3.58 *10-5.

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