Fracture Characterization of Naturally Fractured Reservoirs

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1		Riyaz Kharrat	--	2095	2023-05-05 10:16:38	\|
2	format correct	Conner Chen	Meta information modification	2095	2023-05-08 03:37:24	\|

This entry is adapted from the peer-reviewed paper 10.3390/en16083437

Recent developments in fracture characterization, modeling, and the impact of fracture networks on oil recovery in naturally carbonate-fractured reservoirs have been reviewed. The pivotal role of fracture identification and characterization in understanding production mechanisms and developing realistic fracture modeling approaches has been highlighted. This concludes that improved fracture network modeling requires considering various factors, such as data collection, fracture characterization, reservoir simulation, and model updating based on newly acquired field data. Integrating multiple techniques and data sources is recommended for obtaining a reliable reservoir model to optimize primary and enhanced oil recovery methods.

fractured reservoirs fracture characterization fracture modeling

1. Fractures Characteristics, Parameters, and Fracture Types

Fracture characterization refers to identifying, measuring, and describing fractures’ physical and geometric properties in a rock formation. The fractures can be characterized using various methods, including; core analysis, well logging, seismic method, and tracer testing. Tracer testing is one of the powerful methods for characterizing fractures and their properties in the subsurface. Tracer tests involve injecting a tracer into a fracture or borehole and monitoring its movement and concentration over time ^[1]^[2]. Through the tracer test, one can gain valuable insights into the subsurface flow patterns and better understand the behavior of fractures in the subsurface.

Once a fracture is characterized, its properties can be used to determine its potential for fluid flow or as a pathway for fluid injection. In addition, fracture characterization can be used to identify the type of fracture, such as natural fractures, tectonic fractures, joints, and veins. It can also be used to determine the mechanical properties of fracture, such as fracture aperture, roughness, persistence, and conductivity. The mechanical and morphological characteristics of fractures can significantly impact fluid flow in a reservoir. For example, hydraulic fractures with a branched morphology tend to have higher permeability than those with a non-branched morphology, while tectonic fractures parallel to the direction of fluid flow tend to have higher permeability than those oriented perpendiculars to the flow direction. Understanding fractures’ genetic types and associated characteristics can help develop effective reservoir management strategies.

During the data acquisition, fractures are typically grouped into different discontinuity sets ^[3]. They can have similar characteristics in the same category, such as the mechanical properties and the origin of occurrence. Fractures in one discontinuity set are parallel, meaning they have a similar dip and dip direction in the case of the systematic fracture sets, making them relatively easy to distinguish ^[4]. An example of an analyzed Rose diagram showing different fracture sets is illustrated in Figure 1.

Figure 1. Rose diagrams showing different fracture sets.

Fracture sets recognition depends on pattern recognition expertise and capabilities. Minor variations in attributes are often disregarded to simplify the modeling process and decrease computational power needs ^[5].

The fracture length and aperture control the flow behavior of fracture networks. The fracture aperture and porosity are estimated by the fracture length ^[6]. From the fracture aperture, the permeability of the fracture can be estimated. Simple and complex correlations were proposed for fracture permeability. The basic relation is based on Darcy’s equation which resembles the cubic law ^[7].

The general technique to differentiate fracture types is separating them into open, partial, and closed fractures. Open fractures can be classified according to their aperture values since they are the main concern for fluid flow in porous media ^[8]. Based on FMI and EMI logs, fractures can be identified, and their aperture can be estimated using mathematical expression by converting measured logging parameters to aperture. For example, based on the FMI log shown in Figure 2, fractures appear as sinusoidal features with more dip than a structural dip. The blue triangles, circular, and square dips represent the minor, medium, and major open fractures. Based on the aperture size, different fracture types, namely major, medium, minor, and hairline fractures, can be identified if sufficient image logs are available. For example, the four mentioned types were determined in a giant carbonate fractured reservoir with more than 29 image logs ^[9]. In this classification, the major fractures had the highest continuity and aperture values (600 µm), while the hairline fracture type had the lowest values (10 µm). The medium and minor average apertures were 400 µm and 200 µm, respectively. The hairline fractures can be lumped with a minor fracture if their aperture size is too small.

Figure 2. FMI image and core of a carbonate formation: major open fractures blue square dips, medium open fractures (blue circular dips), and minor open fractures (blue triangle dips) ^[10].

2. Matrix–Fracture Interaction

Matrix–fracture interaction is a key aspect of subsurface flow and transport in rock formations. The matrix, which is the intact rock surrounding the fractures, and the fractures can possess distinct properties such as permeability and porosity that can influence the flow and movement of fluids. In the fluid transfer between fractures and matrix, non-dimensional numbers such as Capillary, Gravity, and Péclet number control the flow behavior ^[11]. Hence, the matrix–fracture interaction is governed by matrix and fracture properties. To understand and predict the behavior of subsurface fluids in fractured rocks, it is important to have a detailed understanding of the properties and behavior of the matrix and fractures and their interactions. This can be obtained through laboratory experiments, field measurements, and numerical modeling, such as rock mechanics tests, permeability, and porosity measurements, microscopic imaging, hydraulic tests, geophysical surveys, well logging, DFN modeling, and coupled fluid flow and geomechanical modeling.

In the water-wet rock reservoirs, the effective imbibition of water from the fractures is supported by a counter-current or co-current mechanism to displace oil from the matrix ^[12]. It is an effective recovery mechanism in water-wet fractured reservoirs, whereas less effective in a mixed-wettability or oil-wet system since a faster increase in the water cut usually happens. The interaction between the matrix blocks highly influences the gravity drainage mechanism, capillary continuity, and the liquid’s reinfiltration. For gas-based EOR methods, it is the main production mechanism, especially in the gas-invaded zone ^[13]^[14]^[15]^[16]. Due to the height and large density differences (oil and gas phases), pressure contrast between matrix blocks and surrounding fractures occurs, enhancing oil production from the matrix blocks ^[6]^[17].

3. Modeling and Workflow for Data Integration

Data integration in modeling naturally fractured reservoirs involves combining different data types to create a numerical model accurately representing the reservoir’s geological and hydraulic characteristics. The difficulty of this procedure stems from the intricacy of the fractures and the requirement for validation at every stage of the modeling and scaling process ^[18]. Usually, production data are used for tuning the model through history matching. This fact necessitates a precise static and dynamic modeling approach for the fracture networks due to their effects on overall reservoir performance. Fractures of the same type generated simultaneously are grouped into a fracture set, and each fracture network contains at least one fracture set or more. Wellbore data in the form of logs, cores, borehole images, or dynamic data provide information on the characteristics of smaller-scale fractures. In contrast, seismic data can provide the distribution parameters of large-scale discontinuities, e.g., major faults. Stochastic methods are used to populate the area between seismic-scale faults, but reliable statistics might result in high extrapolation uncertainties ^[19].

Statistical methods are commonly recommended for fractured network characterization. Fracture attributes such as the aperture, size distribution, and fracture density are implemented within the rock volume based on experimentally recognized statistical models ^[20]^[21]. The Poisson modeling technique was used in commercial software. However, the model tends to have some uncertainty owing to its simplified assumptions, mainly uniform spatial distribution, fracture shape simplification, and neglecting the relationships between geometrical properties and topological relations are the weakness of this approach ^[21]^[22]^[23].

The main techniques utilized for modeling fractured reservoirs, continuum and discrete fracture modeling (DFM), as well as their combination, are explored in the following sections.

3.1. Continuum Approach

In this approach, virtual fracture networks are generated as a second, independent continuum dividing the rock matrix, the first continuum in matrix blocks. Both continuums have their own storage, flow properties, and flow equations. A hydraulic coupling between the rock matrix and the fracture network is introduced by a transfer function. A transfer function defined in shape factor interactions with the two continua (fracture and matrix) and determines the fluid transfer between matrix and fractures. This function represents the difference in pressure between matrix blocks and the surrounding fractures ^[24]. Several transfer functions were proposed to improve the matrix–fracture interaction description and provide accurate modeling ^[5]^[25]^[26].

Two models were used for the continuum approach, namely the dual-porosity (DP) and the dual-permeability (DK) models ^[27]. The DP model is computationally efficient, but precision might be compromised due to its tendency to underestimate or overestimate the oil recovery during early or late times. The main difference between these two models is that the exchange of fluids is only allowed between the matrix block and the fracture in the dual-porosity model. This corresponds to a situation in which matrix blocks are essentially insulated by the fracture network. The DP model allows for inter blocks flow and directly connects the flow pathway with the wellbore and the inter-porosity flow between matrix and fracture systems. The modeling results might differ for different reservoirs; however, it is not completely proven to be one or the other. The continuum approach’s main benefit is simplifying the fracture networks’ complexity to be suitable for field-scale studies. However, this simplification might provide misleading simulation results ^[27]^[28].

3.2. Discrete Fractures Approach

The discrete fractures approach explicitly represents fractures as elements or control volumes as an alternative to the dual continuum approach. The approach involves dividing the reservoir into discrete fractures characterized by their geometric properties (length, width, and orientation) and flow properties (permeability and aperture). The fractures are then connected to the surrounding matrix, representing the non-fractured rock. The DFN is used to display the fracture network ^[29]. When the permeability of the reservoir matrix is low or negligible, fluid flow through the fractures can be modeled using discrete fracture network models. In comparison, other discrete fracture models need to be utilized in the case of fracture matrix interaction ^[28].

In the DFM models, the need for transfer functions is eliminated, but it could be used to estimate the properties of fractures that are not directly known ^[30]. The DFM conveniently describes the fractures’ structural features, such as fracture geometry. Fractures aligning with the internal boundaries of the matrix grids is a mandate in the DFM models. Therefore, unstructured grids are necessary for the fractures’ geometrical representation and the matrix domain’s decentralization. However, quality gridding might not be feasible when the distance between the fracture is trivial ^[31]. It is worth mentioning that DFM models are computationally intensive and fall outside the realm of traditional solution methodologies for field-scale applications ^[30].

The embedded discrete fracture model (EDFM) is an alternative modeling technique in the discrete fractures approach, with the advantage of using the non-conforming grid to discretize the matrix domain without considering the fracture’s locations. The approach defines the intersection between the fracture polygons and matrix grids after placing the fractures in the matrix grids. The matrix grid boundaries discretize the fractures, so only one fracture segment is created for each infiltrated matrix grid by a fracture ^[29]. Based on these measures, the model can be compatible with commercial simulators ^[28].

3.3. Hybrid Approach

The hybrid approach combines the continuum and the discrete fracture approaches. To simulate the overall behavior of the rock matrix and fluid flow through it, continuum elements such as finite difference or finite element methods are employed. The discrete elements, such as DFN, are used to model the fractures’ behavior, including their geometry, orientation, and aperture. The construction of the hybrid model has two essential processes: partitioning and lumping. The fracture network is separated into two sets based on a pre-determined cutoff in the partitioning process. The continuum approach implicitly represents the small fracture set, while the discrete fracture approach explicitly represents a large one. It should be noted that the implicitly represented fractures must be subdivided into fracture groups so they can be integrated into a continuum ^[28]. The hybrid approach is particularly useful for fractured reservoirs because it can accurately capture the complex behavior of the fractures and their interaction with the surrounding rock matrix and fluids, leading to more accurate predictions of fluid flow and production rates.

In summary, different simulation approaches were proposed for fractured reservoirs; however, the simulation outcome might differ depending on the reservoir and fracture properties. For example, water movement and distribution differ between DFN and non-DFN models for water flooding in a low permeability reservoir. The DFN model predicts more water spreading and reaching farther locations than the non-DFN model ^[32]. Hence, the simulation modeling approach is critical for the secondary oil recovery and EOR stages.

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Riyaz Kharrat

Holger Ott

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