Estimation of fracture aperture from petrophysical logs using teaching–learning-based optimization algorithm into a fuzzy inference system

Ghoochaninejad, Hesam Zarehparvar; Asef, Mohammad Reza; Moallemi, Seyed Ali

doi:10.1007/s13202-017-0396-1

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…It is important to determine this value to understand the production in fractured formation [2,20]. Ghoochaninejad et al [83] proposed a novel method of fracture aperture estimation from well logs, utilizing the teaching-learning-based optimization algorithm (TLBO) as the artificial intelligence technique, created for reservoir parameters estimation. At the beginning, authors defined the hydraulic fracture on the base of mechanical fracture, and consequently operated this concept.…”

Section: Computer Modeling Deep Learning and Artificial Intelligence ...mentioning

confidence: 99%

“…At the beginning, authors defined the hydraulic fracture on the base of mechanical fracture, and consequently operated this concept. Ghoochaninejad et al [83] cited several works, in which authors proposed various ways to determine aperture size, among others, utilizing NMR, multiaxial electromagnetic induction logging, X-ray micro-computed tomography, and also the detection of fracture width by using conventional well logs (Nakashima and Kikuchi, 2007; Wu, 2013; Ramandi et al, 2016 and Shalaby and Islam, 2017 (all in [83]). The method proposed by [83] was an integration of fuzzy inference system and teachinglearning-based optimization algorithm (TLBO).…”

Section: Computer Modeling Deep Learning and Artificial Intelligence ...mentioning

confidence: 99%

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Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

Stadtmüller

Jarzyna

2023

Energies

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The purpose of this review paper is to show the possibilities of carbonate reservoir characterization using well logging and laboratory measurements. Attention was focused on standard and new methods of well logging acquisition and interpretation including laboratory experiments to show a part of the history of carbonate rock investigations as hydrocarbon or water reservoirs. Brief information on the geology, mineralogy and petrography of carbonate rocks was delivered. Reservoir properties, i.e., porosity (including fracturing), permeability, and saturation, were defined to emphasize the specific features of carbonates, such as fractures, and vugs. Examples of methodologies were selected from the commonly used laboratory techniques (thin sections examination, mercury and helium porosimetry, X-ray diffraction—XRD) combined with the standard well logs (bulk density—RHOB, neutron porosity—NPHI, sonic slowness—DT, and deep resistivity—Rd) to show the methods that have been used since the very beginning of the scientific and engineering studies of carbonates. Novelty in well logging, i.e., resistivity and acoustic imaging, nuclear magnetic resonance–NMR, dipole shear sonic imaging–DSI, and a spectral neutron-gamma log-geochemical device–GLT combined with modern laboratory investigations (NMR laboratory experiments, scanning electron microscopy SEM), showed how continuous information on mineral composition, porosity and saturation could be obtained and juxtaposed with very detailed laboratory data. Computed X-ray tomography (CT) enabling the 2D and 3D analyses of pores and fractures was presented as a quantitative methodology, effective in pore space characterization, revealing rock filtration abilities. Deep learning and artificial intelligence were used for joining various types of data. It was shown that thanks to new computational technologies original data from very small samples (micro scale), extensively describing the flow ability of the reservoir, could be extended to mezzo scale (core samples) and macro scale (well log images). Selected examples from the published papers illustrated the review. References cited in the text, together with the issues included in them, were the rich source of the practical knowledge processed These were checked by the authors and could be used in other projects.

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Section: Computer Modeling Deep Learning and Artificial Intelligence ...mentioning

confidence: 99%

Section: Computer Modeling Deep Learning and Artificial Intelligence ...mentioning

confidence: 99%

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

Stadtmüller

Jarzyna

2023

Energies

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“…Zarehparvar Ghoochaninejad and his colleagues estimated hydraulic aperture of detected fractures using well log responses using a Teaching-Learning-Based Optimization algorithm (TLBO), which trained an initial Sugeno fuzzy inference system [19]. Aghli et al, tried to find a quick generalized method for identification of FZs using PLs and then used the Velocity Deviation Log (VDL) to identify fracture aperture opening and their effects on porosity and permeability in high fracture density zones [20].…”

Section: Related Workmentioning

confidence: 99%

Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs

Azizi¹,

Reza²

2021

ICA

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In the last decade, a few valuable types of research have been conducted to discriminate fractured zones from non-fractured ones. In this paper, petrophysical and image logs of eight wells were utilized to detect fractured zones. Decision tree, random forest, support vector machine, and deep learning were four classifiers applied over petrophysical logs and image logs for both training and testing. The output of classifiers was fused by ordered weighted averaging data fusion to achieve more reliable, accurate, and general results. Accuracy of close to 99% has been achieved. This study reports a significant improvement compared to the existing work that has an accuracy of close to 80%.

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“…For example, to accurately model hydraulic flow in a fractured medium, it is important to characterize the geometry of fractures, their connectivity and their hydraulic properties, such as hydraulic aperture and transmissivity (Ghoochaninejad et al., 2018; Long et al., 1982; Neuman, 2005). Observations at outcrops, tunnels or boreholes provide precise measurements for accurate descriptions of these properties but are often limited in number and do not provide spatially distributed information.…”

Section: Introductionmentioning

confidence: 99%

Modelling and inferring fracture curvature from borehole GPR data: A case study from the Bedretto Laboratory, Switzerland

Escallon,

Shakas,

Maurer

2023

Near Surface Geophysics

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Fracture curvature has been observed from the millimetre to the kilometre scales. Nevertheless, characterizing curvature remains challenging due to data sparsity and geometric ambiguities. As a result, most numerical models often assume planar fractures to ease computations. To address this limitation, we present a novel approach for inferring fracture geometry from travel‐time data of electromagnetic or seismic waves. Our model utilizes co‐kriging interpolation of control points in a three‐dimensional surface mesh to simulate fracture curvature effectively, resulting in an unstructured triangular grid. We then refine the fracture surface into a structured grid with equidistant elements so that both small‐scale heterogeneities and large‐scale curvature can be modelled. To constrain the fracture geometry, we perform a deterministic travel‐time inversion to optimally place these control points. We validate our methodology with synthetic data and address its limitations. Finally, we infer the geometry of a large (more than 200 m) fracture observed in single‐hole ground‐penetrating radar field data. The fracture surface closely agrees with borehole televiewer observations and is also constrained far from the boreholes. Our modelling approach can be trivially adapted to multi‐offset ground‐penetrating radar or active seismic data.

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Estimation of fracture aperture from petrophysical logs using teaching–learning-based optimization algorithm into a fuzzy inference system

Cited by 10 publications

References 30 publications

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

Applied Machine Learning Methods for Detecting Fractured Zones by Using Petrophysical Logs

Modelling and inferring fracture curvature from borehole GPR data: A case study from the Bedretto Laboratory, Switzerland

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