Exploration Geophysics

Haldar, S.K.

doi:10.1016/b978-0-12-814022-2.00006-x

Cited by 20 publications

(15 citation statements)

References 1 publication

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“…In well logging, the in situ properties of rocks around the wellbore are indirectly estimated from electric, acoustic, and nuclear indicators. e interpretations of these indicators reflect the existence of hydrocarbon, petrophysical properties, and lithology of the formation [47,48]. In this study, the following well logs' records were used: Formation resistivity (FR): it is a measure of electrical resistivity in three increasing depths from the well which is mainly interpreted to know the fluids' saturations, and hence, the existence of hydrocarbon [49,50].…”

Section: Well Logsmentioning

confidence: 99%

[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

Siddig

Mahmoud

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples’ limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells’ datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient (R) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation’s parameters are reported to allow its use on different datasets.

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Section: Well Logsmentioning

confidence: 99%

[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

Siddig

Mahmoud

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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“…In the recent decade, the feasibility and effectiveness of lightweight UAV-borne magnetometry systems have been demonstrated by various geophysical applications, from identifying various rock types and structures in the subsurface and delineating ore deposits to locating synthetic ferrous objects, such as UXO (Perry et al, 2002;Cunningham, 2016;Malehmir et al, 2017;Parvar et al, 2017;Kolster and Døssing, 2021). However, developing a low-noise UAV-borne magnetometry system is challenging given the compact size of a UAV platform; i.e., magnetometers easily fall in the vicinity of sources of magnetic interference from the platform, such as motors, electric-powered devices, and even current-carrying cables (Forrester, 2011;Sterligov and Cherkasov, 2016;Hansen, 2018;Tuck et al, 2018;Tuck, 2019). As a result, UAV-borne magnetometry systems are often suspended in a magnetometer bird (housing magnetometers, global navigation satellite system (GNSS) antenna, and a data logger) a few meters below the airframe to minimize the interference from the platform (Malehmir et al, 2017;Parvar et al, 2017;Parshin et al, 2018;Sterligov et al, 2018;Nikulin and de Smet, 2019).…”

Section: Introductionmentioning

confidence: 99%

Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV)

Jirigalatu¹,

Krishna

Silva

et al. 2021

Geosci. Instrum. Method. Data Syst.

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Abstract. Using unmanned aerial vehicles (UAVs) for airborne magnetometry offers not only improved access and rapid sampling but also reduced logistics costs. More importantly, the UAV-borne aeromagnetometry can be performed at low altitudes, which makes it possible to resolve fine features otherwise only evident in ground surveys. Developing such a UAV-borne aeromagnetometry system is challenging owing to strong magnetic interference introduced by onboard electric and electronic components. An experiment concerning the static magnetic interference of the UAV was conducted to assess the severity of the interference of a hybrid vertical take-off and landing (VTOL) UAV. The results of the static experiment show that the wing area is highly magnetic due to the proximity to servomotors and motors, whereas the area along the longitudinal axis of the UAV has a relatively smaller magnetic signature. Assisted by the static experiment and aerodynamic simulations, we first proposed a front-mounting solution with two compact magnetometers. Subsequently, two dynamic experiments were conducted with the setup to assess the dynamic interference of the system. The results of the dynamic experiments reveal that the strongest source of in-flight magnetic interference is the current-carrying cables connecting the battery to the flight controller and that this effect is most influential during pitch maneuvers of the aircraft.

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“…Magnetic surveying has been extensively used in the search for mineral deposits, oil and gas reservoirs, geothermal resources, as well as for a variety of other purposes such as natural hazards assessment, basement structural studies, mapping subsurface archaeology and unexploded ordnance (UXO) (Nabighian et al, 2005;Eppelbaum, 2011;Fairhead, 2012;Hinze et al, 2013;Eppelbaum, 2015;Kruse, 2013;Haldar, 2018;Turner et al, 2015;Zhou, 2018). In general, magnetic measurements can provide information on and insight into the physical, chemical, and even biological processes that have affected the iron phases within.…”

Section: Introductionmentioning

confidence: 99%

“…Airborne magnetometry (aeromagnetometry) is an inexpensive, efficient, and effective regional reconnaissance tool (Reeves, 2005;Eppelbaum, 2015;Haldar, 2018). The method offers improved accessibility to areas restricted to terrestrial surveys such as remote areas, offshore areas, and thickly-vegetated regions as well as rapid sampling of the local geomagnetic field compared to its ground or space counterpart (Council, 1995;Haldar, 2018).…”

Section: Introductionmentioning

confidence: 99%

Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV)

Jirigalatu¹,

Krishna²,

Silva³

et al. 2020

Preprint

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Abstract. Airborne magnetic surveys are an important and efficient tool for mapping the subsurface, providing insights e.g. into mineral deposits. Compared to traditional ground methods, airborne magnetic surveys offer great advantages with improved access and rapid sampling. But the cost and hassle of transporting and operating a conventional manned airborne magnetic survey system are strong impediments for its wider use. In addition, the conventional airborne systems are challenged by the need for low-altitude (&leq; 80 m) surveying to detect small-scale subsurface features evident in ground surveys. Portable and compact airborne magnetic survey systems using unmanned aerial vehicles (UAVs) can not only bridge the gap between conventional airborne magnetic surveys and ground magnetic surveys but also complement magnetic surveys to fit broader geophysical applications. Therefore, developing high-quality, stable, and portable UAV-borne survey systems is of high interest to the geophysical exploration community. However, developing such a system is challenging owing to strong magnetic interference introduced by onboard electric engines and other onboard electronic devices. As a result, tests concerning the static and dynamic magnetic interference of a UAV are critical to assess the severity of the interference and can help to improve the design of the system at the early stage of development. A static experiment and two dynamic experiments were conducted to understand the characterization of the magnetic interference of our hybrid vertical take-off and landing (VTOL) UAV. The results of the static experiment show that the wing area is highly magnetic due to the proximity to servomotors and motors, but the area along the longitudinal axis of the UAV is relatively magnetically quiet. To reduce the magnetic signature, the highly-magnetic servomotors on the wings were replaced with less magnetic servomotors of a brush-less type. Assisted by aerodynamic simulations, we further designed a front-mounting solution for two compact magnetometers. Two dynamic experiments were conducted with this setup to understand the dynamic interference of the UAV in operation. The results of the dynamic experiments reveal that the strongest source of in-flight magnetic interference is mainly due to the cables connecting the battery to the flight controller and that this effect is most influential during pitch maneuvers of the aircraft.

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Exploration Geophysics

Cited by 20 publications

References 1 publication

[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

[Retracted] Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV)

Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV)

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