Multi-attribute ant-tracking and neural network for fault detection: a case study of an Iranian oilfield

Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Yaraki, Mehdi Tavakoli

doi:10.1088/1742-2132/10/1/015009

Cited by 50 publications

(14 citation statements)

References 12 publications

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“…The edge means the lateral discontinuities of seismic amplitude. Ant tracking [6] is used to detect and extract the continuous features from the seismic attributes. The ant-tracking algorithm is based on the ant-colony algorithm to capture trends in noisy data [6].…”

Section: Condition Of the Seismic Data Volumementioning

confidence: 99%

“…Geologists and engineers tried to use different types of data to describe the fractures, e.g., seismic attributes, geophysical logs, drilling cores, and outcrops [4,5]. A seismic edge detection method was used to conduct stratigraphic and structural interpretations of geological features [6,7]. However, some subtle faults and fractures may cause minor changes in the seismic waveform which are not easily correlated using the conventional interpretation of the seismic section.…”

Section: Introductionmentioning

confidence: 99%

“…Dip and azimuth attributes were put forward by Dalley et al [8], who described the basic algorithm for using dip and azimuth attributes to interpret the faults. Later, dip and azimuth attributes were used in identifying subtle faults, as well [6,9]. More recently, curvature attribute was widely used in delineating faults [6,10,11] and predicting fracture orientations and distributions [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Later, dip and azimuth attributes were used in identifying subtle faults, as well [6,9]. More recently, curvature attribute was widely used in delineating faults [6,10,11] and predicting fracture orientations and distributions [7,12,13]. Coherency attribute developed by Bahorich properties, e.g., porosity and permeability, can be calculated by upscaling the DFN model [27,46,47] into cells in order to simulate the reservoir fluid flow [48].…”

Section: Introductionmentioning

confidence: 99%

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Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

2017

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This paper presents an integrated approach of discrete fracture network modelling for a naturally fractured buried-hill carbonate reservoir in the Jingbei Oilfield by using a 3D seismic survey, conventional well logs, and core data. The ant tracking attribute, extracted from 3D seismic data, is used to detect the faults and large-scale fractures. Fracture density and dip angle are evaluated by observing drilling cores of seven wells. The fracture density distribution in spatiality was predicted in four steps; firstly, the ant tracking attribute was extracted as a geophysical log; then an artificial neural network model was built by relating the fracture density with logs, e.g., acoustic, gamma ray, compensated neutron, density, and ant tracking; then 3D distribution models of acoustic, gamma ray, compensated neutron and density were generated by using a Gaussian random function simulation; and, finally, the fracture density distribution in 3D was predicted by using the generated artificial neural network model. Then, different methods were used to build the discrete fracture network model for different types of fractures of which large-scale fractures were modelled deterministically and small-scale fractures were modelled stochastically. The results show that the workflow presented in this study is effective for building discrete fracture network models for naturally fractured reservoirs.

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Section: Condition Of the Seismic Data Volumementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

2017

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“…Conventional post-stack seismic data is an imaging of seismic data of its entire frequency range, but main frequency and signal to noise ratio of seismic data is low, it is difficult to achieve the subtle interpretation about structure, using conventional methods [5] . In the seismic interpretation, different frequency response of the same fault is different, different frequency response for faults of different sizes are different.…”

Section: ) Frequency Processingmentioning

confidence: 99%

Multiple-track ant body attribute extraction method improved

Du¹,

Xin²,

Wang³

et al. 2016

Proceedings of the 7th International Conference on Environment and Engineering Geophysics &Amp; Summit Forum of Chinese Academy

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Abstract-to interpret small faults is always the core question for seismic exploration. Seismic attributes can help us understand the fault and fracture system better. The earthquake frequency controls seismic resolution, if the resolution is relatively low, minor faults or micro-fractures portrayed through the property body may not match reality. High frequency means high resolution, easier to distinguish small faults. In order to identify the fault better, we make a simple raw data processing to extract the high frequency part of the seismic. While using seismic attribute technology, improved ant-tracking method introduced into the three-dimensional data volume of the work ， area structure will be explained. Because of its differentiating attributes extraction and analysis ability anttracking has a strong indicative of faults.

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Analysing Seismic Attributes

Nanda¹

2021

Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production

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Multi-attribute ant-tracking and neural network for fault detection: a case study of an Iranian oilfield

Cited by 50 publications

References 12 publications

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

Multiple-track ant body attribute extraction method improved

Analysing Seismic Attributes

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