Topological analysis of fault network in naturally fractured reservoirs: A case study from the pre-salt section of the Santos Basin, Brazil

Mendes, Luiza de Carvalho; Correia, Ulisses Miguel da Costa; Cunha, Oton Rubio; Oliveira, F Milton Evangelista de; Vidal, Alexandre Campane

doi:10.1016/j.jsg.2022.104597

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…The discrete fracture network (DFN) model comprises a series of geometric objects representing individual fractures [54]. When integrated, the network topology emphasises the relationships between individual structures and can reveal how fault interactions and connectivity occur [55,56]. The summary of the workflow is displayed in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Fracture Network Analysis of Karstified Subis Limestone Build-Up in Niah, Sarawak

Ong,

Jamaludin

2023

Applied Sciences

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Understanding complex carbonate fracture networks and karstification at various geological scales is challenging, especially with limited multi-scale datasets. This paper aims to reduce uncertainty in the fracture architecture of Central Luconia karstified reservoirs by narrowing observational gaps between seismic and well data by using the discrete fracture models of exposed limestone outcrops as analogues for the subsurface carbonate reservoir. An outcrop-based fracture network characterisation of a near-surface paleo-karst at Subis Limestone combined with lineament analysis was conducted to extract fracture parameters. The karst structure was first delineated using a digital elevation map and outcrop examination. Then, topology analysis was performed, following the creation of two-dimensional discrete fracture models. Two main fracture sets oriented northeast–southwest and northwest–southeast and 79 potential dolines were identified. Fracture intersections, northeast–southwest major orientations, and drainage systems highly influenced the karst features. The Subis Limestone fracture model revealed that the highest number of fractures and total length of fractures were concentrated in the northern part of the Subis Limestone build-up (X: 250–350, Y: 150–250) and became denser towards the northwest direction of the outcrop (X: 600–800). The fractures in the Subis paleo-karsts appear isolated, with I-nodes ranging from 0.74 to 0.94. Hence, it is crucial to incorporate matrix porosity into multiple scales of fracture network modelling to improve upscaling and the modelling of fracture–vug networks, as well as to minimise the underestimation of discrete fracture networks in fractured and karstified limestone.

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Section: Methodsmentioning

confidence: 99%

Fracture Network Analysis of Karstified Subis Limestone Build-Up in Niah, Sarawak

Ong,

Jamaludin

2023

Applied Sciences

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“…This characteristic can be utilized to identify fracture reservoirs. These identification principles have been confirmed by a large body of research from scholars and are now widely used, especially yielding good application results in formations with large‐scale fracture development and solution cavity development 21,22 . Although seismic data can reflect a lot of information about fractures and cavities in carbonate rocks, for the same formation with complex systems of different scales, shapes, and distribution densities, relying solely on seismic data may not always obtain high identification accuracy.…”

Section: Introductionmentioning

confidence: 93%

“…These identification principles have been confirmed by a large body of research from scholars and are now widely used, especially yielding good application results in formations with large-scale fracture development and solution cavity development. 21,22 Although seismic data can reflect a lot of information about fractures and cavities in carbonate rocks, for the same formation with complex systems of different scales, shapes, and distribution densities, relying solely on seismic data may not always obtain high identification accuracy. Additionally, for carbonate rock reservoirs with small-scale fractures and low fracture distribution density, the accuracy of geophysical identification methods can be significantly reduced.…”

Section: Introductionmentioning

confidence: 99%

Carbonate reservoir fracture‐cavity system identification based on the improved YOLOv5s deep learning algorithm

Feng,

Zhao,

Zhang

et al. 2024

Energy Science & Engineering

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In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, the drilling process is highly prone to the loss of drilling fluid. This not only affects drilling efficiency but can also lead to severe accidents, such as blowouts. Therefore, it is crucial to understand the distribution pattern of these fractures. However, the formation of carbonate rock fracture‐cavity system systems, being controlled by various factors, is difficult to precisely identify. This limitation hampers the efficient development of such types of oil and gas fields. This paper presents a case study of the M55 sub‐section carbonate gas reservoir in the Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data to identify response characteristics of fractures in the carbonate reservoirs. And its identification results have been confirmed to be accurate by various fracture data obtained through different means, such as the core samples, cast thin section photographs, imaging logging data, and seismic attributes. This method incorporates the Ghost convolution module to replace the Conv module in the backbone network of the YOLOv5s model, and modifies the C3 module into a Ghost Bottleneck module, effectively making the model more lightweight. Additionally, a Convolutional Block Attention Module is integrated into the Neck network, enhancing the model's feature extraction capabilities. Finally, the method employs the Efficient Intersection over Union Loss function instead of the Complete Intersection over Union Loss, reducing the network's regression loss. The validation results using actual data demonstrate that this method achieves an average recognition accuracy of 87.3% for the fracture‐cavity system, which is a 3% improvement over the baseline model (YOLOv5s). This enhancement is beneficial for precisely locating the drilling fluid loss positions in carbonate reservoirs.

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Dynamic re-sedimentation of lacustrine carbonates in the Búzios Field, pre-salt section of Santos Basin, Brazil

Altenhofen,

Rodrigues,

Borghi

et al. 2024

Journal of South American Earth Sciences

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Topological analysis of fault network in naturally fractured reservoirs: A case study from the pre-salt section of the Santos Basin, Brazil

Cited by 8 publications

References 34 publications

Fracture Network Analysis of Karstified Subis Limestone Build-Up in Niah, Sarawak

Fracture Network Analysis of Karstified Subis Limestone Build-Up in Niah, Sarawak

Carbonate reservoir fracture‐cavity system identification based on the improved YOLOv5s deep learning algorithm

Dynamic re-sedimentation of lacustrine carbonates in the Búzios Field, pre-salt section of Santos Basin, Brazil

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