The effect of fracturing on permeability in carbonate reservoir rocks

Rashid, F.; Hussein, D.; Lorinczi, Piroska; Glover, Paul

doi:10.1016/j.marpetgeo.2023.106240

Cited by 11 publications

(4 citation statements)

References 71 publications

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“…They studied the effects of the number of prefabricated fractures (e.g., single fracture, double fractures, multiple fractures) and the fracture morphology (e.g., angle, length, spacing, and filling materials of the fractures) on the mechanical properties and the deformation-cracking response of the rock samples using uniaxial compression test, conventional triaxial compression test, Brazilian disk test, cyclic loading test, etc. (Haeri et al, 2014;Yang, 2015;Zhuang and Zhou, 2020;Jacobsson et al, 2021;Liang et al, 2021;Rashid et al, 2023). The results confirmed that the initial detects reduced the strength of rock samples, and the weakening degree was affected by the geometrical parameters of the fractures.…”

Section: Introductionsupporting

confidence: 65%

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Song

Tian

et al. 2023

Adv. Geo-Energy Res.

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The analysis of mechanical response and deformation-cracking behavior contributes to the high-efficiency extraction of geo-energy and long-term safety of underground engineering structures. Compared to natural cores, the mechanical properties of 3D-printed samples made from quartz sand as raw material are relatively homogeneous, and can be used for quantitative studies on the influence of natural defects on the mechanical properties of rocks. In this work, 3D-printed samples with single fractures of different crack angles, lengths and widths were fabricated and used for uniaxial compression tests. Adopting the digital image correlation method, the stress-strain distribution during uniaxial compression tests were visualized, and the influence of prefabricated fracture characteristics (dip angle, length, and width) on the deformation-failure process were studied. An extended finite element method subroutine for ABAQUS ® software was modeled and used for the uniaxial compression simulation, which was validated by experiments. Then, the influence of mechanical parameters (Young's modulus, Poisson's ratio, cohesion, and internal friction angle) on the deformation-cracking mechanics were simulated and studied. The results indicate that, compared to the intact sample, fractures reduce the sample strength. With the extension of fracture length and width, or the decline of fracture angle, both the peak strain and strength of the 3D-printed samples decrease. The splitting tensile failure, or shear failure, or both were determined for the 3D-printed samples with different fracture angles. For the same axial strain, the extension length of the new crack increases linearly with rising Young's modulus and decreases linearly with increasing Poisson's ratio. The initial strain of new cracks decreases linearly with increasing Young's modulus, while little variations are found in samples with different Poisson's ratio. For the same axial displacement load, the peak stress increases linearly with growing internal friction angle and cohesion.

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Section: Introductionsupporting

confidence: 65%

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Song

Tian

et al. 2023

Adv. Geo-Energy Res.

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“…The three proposed algorithms are all variants of neural network architectures used for sequence modeling and processing. While all three are based on the same fundamental concept of processing sequential data, they differ in their internal mechanisms and the way they handle long-term dependencies 2 . These models are actually an extension of each other, the most basic of which is RNN.…”

Section: Methodsmentioning

confidence: 99%

“…The limited level of porosity and permeability present in tight gas reservoirs poses a significant challenge for the efficient flow of gas through them. To put it differently, the intricate geological structure of these reservoirs restricts the flow of gas within them, making it arduous to extract the valuable resource 2 . The factors that impact gas relative permeability in these reservoirs involve a complex interplay of factors that affect fluid flow in porous rock matrix, with a focus on water saturation (SW) and water-wet carbonate rock.…”

Section: Introductionmentioning

confidence: 99%

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Hu,

Meng,

Guo

et al. 2024

Sci Rep

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Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

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“…This is probably because the model assumes uniform aperture and smooth surfaces, which are not common in natural fractures, and is supported by our observations, where most fractures have moderate roughness (see Figure 10b). The absence of roughness in the permeability equation can lead to an overestimation of permeability by as much as 39% (Glover et al, 1998;Rashid et al, 2023).…”

Section: Reservoir Quality Of Naturally Fractured Plutonic Rocksmentioning

confidence: 99%

Fracture evaluation of the plutonic basement in the Upper Magdalena Basin: Implications for the development of naturally fractured reservoirs in the Northern Andes

Rodríguez‐Cuevas,

Cardona,

Monsalve

et al. 2024

Geological Journal

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Plutonic rocks typically have negligible matrix porosity and permeability. However, fractures and mineral alterations create storage space and flow pathways that turn plutonic rocks into fluid reservoirs. Despite significant hydrocarbon discoveries, naturally fractured reservoirs in plutonic rocks have been poorly studied. In most Colombian basins, the crystalline basement has undergone multiple deformational events and is thrust over the Cretaceous to Cenozoic source and reservoir rocks of the conventional petroleum system. This structural configuration is ideal for the migration of oil into a fractured basement. A multiscale fracture analysis, including field, petrographical and petrophysical techniques was conducted on the Permian and Jurassic plutonic basement of Upper Magdalena Basin in order to understand the controls on brittle deformation, the development of fracture networks and their potential to form hydrocarbon reservoirs. The results indicate that protolith textures and structures, including magmatic and mylonitic foliation, favours fracturing. Dykes exhibit higher fracture density (7–48 fractures/m), porosity (mean = 0.4%) and permeability (mean = 125,818.75 mD) than the host rock (2–25 fractures/m; 0.23%; 12,066.09 mD). Intersection zones from regional faults, are characterized by the highest fracture and lineament intensity. Our results suggest that dyke swarms and interacting damage zones can significantly enhance the reservoir quality of plutonic rocks by providing storage in fractures and fluid pathways to the host rock.

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The effect of fracturing on permeability in carbonate reservoir rocks

Cited by 11 publications

References 71 publications

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Experimental and numerical modeling of deformation-cracking mechanics of 3D-printed rock samples with single fracture

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Fracture evaluation of the plutonic basement in the Upper Magdalena Basin: Implications for the development of naturally fractured reservoirs in the Northern Andes

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