Clustering, Connectivity and Flow in Naturally Fractured Reservoir Analogs

2021 ◽  
Author(s):  
Ajay K. Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Oil And Gas ◽  
Flow Behavior ◽  
Oil And Gas Industry ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Fracture Networks ◽  
Gas Industry ◽  
Network Geometry

Abstract A previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.

scholarly journals Clustering, Connectivity and Flow Responses of Deterministic Fractal-Fracture Networks

2020 ◽  
Vol 54 ◽  
pp. 149-156
Author(s):  
Ajay K. Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Spatial Clustering ◽  
Flow Behavior ◽  
Flow Simulation ◽  
Fractal Dimensions ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Fracture Models ◽  
Fractal Fracture

Abstract. It is well known that fracture networks display self-similarity in many cases and the connectivity and flow behavior of such networks are influenced by their respective fractal dimensions. In the past, the concept of lacunarity, a parameter that quantifies spatial clustering, has been implemented by one of the authors in order to demonstrate that a set of seven nested natural fracture maps belonging to a single fractal system, but of different visual appearances, have different clustering attributes. Any scale-dependency in the clustering of fractures will also likely have significant implications for flow processes that depend on fracture connectivity. It is therefore important to address the question as to whether the fractal dimension alone serves as a reasonable proxy for the connectivity of a fractal-fracture network and hence, its flow response or, if it is the lacunarity, a measure of scale-dependent clustering, that may be used instead. The present study attempts to address this issue by exploring possible relationships between the fractal dimension, lacunarity and connectivity of fractal-fracture networks. It also endeavors to study the relationship between lacunarity and fluid flow in such fractal-fracture networks. A set of deterministic fractal-fracture models generated at different iterations and, that have the same theoretical fractal dimension are used for this purpose. The results indicate that such deterministic synthetic fractal-fracture networks with the same theoretical fractal dimension have differences in their connectivity and that the latter is fairly correlated with lacunarity. Additionally, the flow simulation results imply that lacunarity influences flow patterns in fracture networks. Therefore, it may be concluded that at least in synthetic fractal-fracture networks, rather than fractal dimension, it is the lacunarity or scale-dependent clustering attribute that controls the connectivity and hence the flow behavior.

Clustering and Connectivity of Fractal-Fracture Networks: Are they related?

2020 ◽  
Author(s):  
Ajay Kumar Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Spatial Clustering ◽  
Flow Behavior ◽  
Fractal Dimensions ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Fractal System ◽  
Clustering Behavior ◽  
Fractal Fracture

<p>It well known that fracture networks display self-similarity in many cases and the connectivity and flow behavior of such networks are influenced by their respective fractal dimensions. One of the authors have previously implemented the concept of lacunarity, a parameter that quantifies spatial clustering, to demonstrate that a set of 7 nested natural fracture maps belonging to a single fractal system, but different visual appearances have different clustering attributes. Any scale-dependency in the clustering of fractures will also likely have significant implications for flow processes that depend upon fracture connectivity. It is therefore important to address the question as to whether the fractal dimension serves as a reasonable proxy  for the connectivity of a fractal-fracture network or is it the lacunarity parameter that may be used instead. The present study attempts to address this issue by studying the clustering behavior (lacunarity) and connectivity of fractal-fracture patterns. We compare the set of 7 nested fracture maps mentioned earlier which belong to a single fractal system, in terms of their lacunarity and connectivity values. The results indicate that while the maps that have the same fractal dimension have almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering change systematically with the scale at which the networks are mapped. It is further noted that there appears to be an exact correlation between clustering and connectivity values. Therefore, it may be concluded that rather than fractal dimension, it is the lacunarity or scale-dependent clustering attribute that control connectivity in fracture networks.</p>

Scale discrepancy paradox between observation and modelling in fractured reservoir models in oil and gas industry.

2020 ◽  
Author(s):  
Pascal Richard ◽  
Loïc Bazalgette
Keyword(s):  
Oil And Gas ◽  
Dynamic Models ◽  
Fractured Reservoirs ◽  
Research Work ◽  
Oil And Gas Industry ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Dynamic Data ◽  
Gas Industry

<p>Naturally fractured reservoirs represent one of the most challenging resource in the oil and gas industry. The understanding based on centimeter scale observations is upscaled and modeled at 100-meter scale.</p><p>In this paper, we will illustrate with case study examples of conceptual fracture model elaborated using static and dynamic data, the disconnect between the scale of observation and the scale of modelling. We will also discuss the potential disconnect between the detail of fundamental, but necessary, research work in universities against the coarse resolution of the models built in the oil industry, and how we can benefit of the differences in scales and approaches.</p><p> </p><p>The appraisal and development of fractured reservoirs offer challenges due to the variations in reservoir quality and natural fracture distribution. Typically, the presence of open, connected fractures is one of the key elements to achieve a successful development. Fracture modelling studies are carried out routinely to support both appraisal and development strategies of these fractured reservoirs.</p><p>Overall fracture modelling workflow consists first of a fracture characterization phase concentrating on the understanding of the deformation history and the evaluation of the nature, type and distribution of the fractures; secondly of a fracture modelling part where fracture properties for the dynamic simulation are generated and calibrated against dynamic data. The pillar of the studies is the creation of 3D conceptual fracture diagrams/concepts which summarize both the understanding and the uncertainty of the fracture network of interest. These conceptual diagrams rely on detailed observations at the scale of the wellbore using core and borehole image data which are on contrasting scale compare to the 10’s of meters to 100’s of meter scale of the grid cells of the dynamic models used for the production history match and forecast. These contrasting scales will be the thread of the presentation.</p>

Scale discrepancy paradox between observation and modelling in fractured reservoir models in oil and gas industry.

2021 ◽  
Author(s):  
Pascal Richard ◽  
Loic Bazalgette
Keyword(s):  
Oil And Gas ◽  
Dynamic Models ◽  
Fractured Reservoirs ◽  
Research Work ◽  
Oil And Gas Industry ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Dynamic Data ◽  
Gas Industry

<p>Naturally fractured reservoirs represent one of the most challenging resource in the oil and gas industry. The understanding based on centimeter scale observations is upscaled and modeled at 100-meter scale.</p><p>In this paper, we will illustrate with case study examples of conceptual fracture model elaborated using static and dynamic data, the disconnect between the scale of observation and the scale of modelling. We will also discuss the potential disconnect between the detail of fundamental, but necessary, research work in universities against the coarse resolution of the models built in the oil industry, and how we can benefit of the differences in scales and approaches.</p><p> </p><p>The appraisal and development of fractured reservoirs offer challenges due to the variations in reservoir quality and natural fracture distribution. Typically, the presence of open, connected fractures is one of the key elements to achieve a successful development. Fracture modelling studies are carried out routinely to support both appraisal and development strategies of these fractured reservoirs.</p><p>Overall fracture modelling workflow consists first of a fracture characterization phase concentrating on the understanding of the deformation history and the evaluation of the nature, type and distribution of the fractures; secondly of a fracture modelling part where fracture properties for the dynamic simulation are generated and calibrated against dynamic data. The pillar of the studies is the creation of 3D conceptual fracture diagrams/concepts which summarize both the understanding and the uncertainty of the fracture network of interest. These conceptual diagrams rely on detailed observations at the scale of the wellbore using core and borehole image data which are on contrasting scale compare to the 10’s of meters to 100’s of meter scale of the grid cells of the dynamic models used for the production history match and forecast. These contrasting scales will be the thread of the presentation.</p>

Statistical Significance and Hypothesis for Friction Factor Correlations of Non-Newtonian Crude Oils in Pipelines

2015 ◽  
Author(s):  
Ahmed H. Kamel ◽  
Ali S. Shaqlaih ◽  
Essam A. Ibrahim
Keyword(s):  
Turbulent Flow ◽  
Friction Factor ◽  
Oil And Gas ◽  
Flow Behavior ◽  
Oil And Gas Industry ◽  
Newtonian Fluids ◽  
Data Set ◽  
Gas Industry ◽  
Pressure Losses ◽  
Wide Range

In pipelines, non-Newtonian fluids are generally pumped under turbulent flow conditions where frictional pressure losses are required for hydraulic design. The friction factor is a crucial parameter in calculating frictional pressure losses. However, determination of the friction factor is a decisive challenge, especially for turbulent flow of non-Newtonian fluids. This is mainly due to the large number of friction factor equations and the precision of each. The main objective of the present paper is to evaluate the published friction factor correlations for non-Newtonian fluids over a wide range of friction factor data to select the most accurate one. An analytical comparative study adopting the recently introduced Akaike information criterion (AIC) and the traditional coefficient of determination (R2) is conducted. Data reported by several researchers are used individually and collectively. The results show that each model exhibits accuracy when examined with a specific data set while El-Emam et al. model proves its superiority to other models when examining the data mutually. In addition to its simple and explicit form, it covers a wide range of flow behavior indices and generalized Reynolds numbers. It is also shown that the traditional belief that a higher R2 corresponds to better models may be misleading. AIC overcomes the shortcomings of R2 as it employs the parsimonious principle to trade between the complexity of the model and its accuracy not only to find the best approximating model but also to develop statistical inference based on the data. Although it has not yet been used in oil and gas industry, the authors present the AIC to initiate an innovative strategy that has been demonstrated in other disciplines to help alleviate several challenges faced by professionals in the oil and gas industry. Finally, a detailed discussion and models’ ranking according to AIC and R2 is presented showing the numerous advantages of AIC.

Deployment of Digital NDT Solutions in the Oil and Gas Industry

2020 ◽  
Vol 78 (7) ◽  
pp. 861-868
Author(s):  
Casper Wassink ◽  
Marc Grenier ◽  
Oliver Roy ◽  
Neil Pearson
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry

What Does Rent Give to Federal Budget (on the Russia's Budget Dependence upon "Oil Dollars")

2004 ◽  
pp. 51-69 ◽  
Author(s):  
E. Sharipova ◽  
I. Tcherkashin
Keyword(s):  
Oil And Gas ◽  
Strong Dependence ◽  
Oil Prices ◽  
Federal Budget ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Resource Rent ◽  
Tax Legislation ◽  
Russian Economy ◽  
The Government

Federal tax revenues from the main sectors of the Russian economy after the 1998 crisis are examined in the article. Authors present the structure of revenues from these sectors by main taxes for 1999-2003 and prospects for 2004. Emphasis is given to an increasing dependence of budget on revenues from oil and gas industries. The share of proceeds from these sectors has reached 1/3 of total federal revenues. To explain this fact world oil prices dynamics and changes in tax legislation in Russia are considered. Empirical results show strong dependence of budget revenues on oil prices. The analysis of changes in tax legislation in oil and gas industry shows that the government has managed to redistribute resource rent in favor of the state.

The Political Economy of Power: Approaches to the Analysis of Relationships between the State and Business in Russia

2011 ◽  
pp. 19-33
Author(s):  
A. Oleinik
Keyword(s):  
Oil And Gas ◽  
Road Construction ◽  
Oil And Gas Industry ◽  
Retail Trade ◽  
Business Community ◽  
The Political ◽  
Economic Power ◽  
Gas Industry ◽  
The Road ◽  
Ruling Elites

The article deals with the issues of political and economic power as well as their constellation on the market. The theory of public choice and the theory of public contract are confronted with an approach centered on the power triad. If structured in the power triad, interactions among states representatives, businesses with structural advantages and businesses without structural advantages allow capturing administrative rents. The political power of the ruling elites coexists with economic power of certain members of the business community. The situation in the oil and gas industry, the retail trade and the road construction and operation industry in Russia illustrates key moments in the proposed analysis.

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