A Comprehensive Review of Fracture Characterization and Its Impact on Oil Production in Naturally Fractured Reservoirs

Kharrat, Riyaz; Ott, Holger

doi:10.3390/en16083437

Cited by 8 publications

(3 citation statements)

References 73 publications

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“…Analyzing fractured reservoirs, especially in the context of hydrocarbon production mechanisms, is particularly difficult [1,2]. The type and orientation of fractures notably impact enhanced oil recovery methods, especially in gas and thermal applications, significantly influencing production mechanisms [3][4][5]. A significant complicating factor arises from the fact that the permeability of carbonates is influenced either by secondary dissolution porosity or by fractures that serve as pathways for fluid flow.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field

Kharrat,

Kadkhodaie,

Azizmohammadi

et al. 2024

Processes

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This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production history, including carbonates from the Asmari and Bangestan Formations. A fracture intensity map was generated based on the interpretation of image logs from 28 wells drilled within the field. Mud loss data were collected and mapped based on the geostatistical Gaussian Random Function Simulation (GRFS) algorithm. Maximum curvature maps were generated based on Asmari structural surface maps. Comparing the results shows a good agreement between the curvature map, fault distribution model, mud loss map, fracture intensity map, and productivity index. The results of image log interpretations led to the identification of four classes of open fractures, including major open fractures, medium open fractures, minor open fractures, and hairline fractures. Using the azimuth and dip data of the four fracture sets mentioned above, the fracture intensity log was generated as a continuous log for each well with available image log data. For this purpose, the fracture intensity log and a continuous fracture network (CFN) model were generated. The continuous fracture network model was used to generate a 3D discrete fracture network (DFN) for the Asmari Formation. Finally, a 3D upscaled model of fracture dip and azimuth, fracture porosity, fracture permeability, fracture length, fracture aperture, and the sigma parameter (the connectivity index between matrix and fracture) were obtained. The results of this study can illuminate the modeling of intricate reservoirs and the associated production challenges, providing insights not only during the initial production phase but also in the application of advanced oil recovery methods, such as thermal recovery.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field

Kharrat,

Kadkhodaie,

Azizmohammadi

et al. 2024

Processes

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“…In contrast, the rock matrix serves as the hydrocarbon source [2]. These reservoirs are well known for having poorer recoveries than their counterpart clastic reservoirs due to their production characteristics and geological heterogeneities [3][4][5][6][7][8]. Furthermore, the channeling and fingering of the injected fluid may be due to the complexity of the natural fracture networks [9].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Influence of Fracture Networks on Gas-Based Enhanced Oil Recovery Methods

Kharrat,

Alalim,

Ott

2023

Energies

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Numerous reservoirs that play a significant role in worldwide petroleum production and reserves contain fractures. Typically, the fractures must form a connected network for a reservoir to be classified as naturally fractured. Characterizing the reservoir with a focus on its fracture network is crucial for modeling and predicting production performance. To simplify the solution, dual-continuum modeling techniques are commonly employed. However, to use continuum-scale approaches, properties such as the average aperture, permeability, and matrix fracture interaction parameters must be assigned, making it necessary to improve the fracture depiction and modeling methods. This study investigated a fractured reservoir with a low matrix permeability and a well-connected fracture network. The focus was on the impact of the hierarchical fracture network on the production performance of gas-based enhanced oil recovery methods. The discrete fracture network (DFN) model was utilized to create comprehensive two-dimensional models for three processes: gas injection (GI), water alternating gas (WAG), and foam-assisted water alternating gas (FAWAG). Moreover, dimensionless numbers were employed to establish connections between properties across the entire fracture hierarchy, spanning from minor to major fractures and encompassing the fracture intensity. The results indicate that the FAWAG process was more sensitive to fracture types and networks than the WAG and GI processes. Hence, the sensitivity of the individual EOR method to the fracture network requires a respective depth of description of the fracture network. However, other factors, such as reservoir fluid properties and fracture properties, might influence the recovery when the minor fracture networks are excluded. This study determined that among the enhanced oil recovery (EOR) techniques examined, the significance of the hierarchical depth of fracture networks diminished as the ratio of major (primary fracture) aperture to the aperture of medium and minor fractures increased. Additionally, the impact of the assisted-gravity drainage method was greater with increased reservoir height; however, as the intensity ratio increased, the relative importance of the medium and minor fracture networks decreased.

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“…Significant hydrocarbon resources may be derived from naturally fractured reservoirs such as fractured shale gas reservoirs, fractured tight sandstone and limestone reservoirs, and basement rocks [1][2][3][4][5]. Fracture characterization plays a critical role in the quantitative evaluation and effective management of these complex reservoirs [6]. Fracture characterizations include quantitative information on fracture density, orientation, and porosity, among others [7].…”

Section: Introductionmentioning

confidence: 99%

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Ifrene,

Irofti,

et al. 2023

Fuels

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Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

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A Comprehensive Review of Fracture Characterization and Its Impact on Oil Production in Naturally Fractured Reservoirs

Cited by 8 publications

References 73 publications

A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field

A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field

Assessing the Influence of Fracture Networks on Gas-Based Enhanced Oil Recovery Methods

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

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