Remote sensing techniques for onshore oil and gas exploration

Abstract

Remote-sensing data are highly useful for the oil and gas industry. Remote sensing has proven to be an integral tool for downstream and upstream oil and gas operations through evaluation of infrastructure for well-site planning and for exploration through large-scale regional reconnaissance. Spectral analysis is a key form of processing to evaluate for surface outcrops and surface hydrocarbon seepage. We will show methodologies using remote-sensing data and how they assist in the overall understanding of the hydrocarbon system. A number of studies are shown in which hyperspectral and multispectral data sets have proven useful in understanding the geology and inferring information about the subsurface. Case studies are presented from Pennsylvania, USA; Neuquén province, Argentina; and Lebanon. Advantages and disadvantages of the use of these methodologies and the future of remote sensing in the oil and gas industry are discussed.

Active underwater detection with an array of atomic magnetometers

“…7. In the graph, S2 ∆r is plotted versus the depth underwater and the distance from the sensors' plane of a thin Al plate (105×73×3 mm 3 ) in position (2,3).…”

“…The effect is larger when the target is closer to the sensor, where the secondary field is stronger: up to 10 times larger than the neighboring values. Furthermore, a systematic decrease of the signal from all sensors is observed when the object is in the center of the grid (2,2). This allows unambiguous localization of the target in five different positions.…”

“…Detection, localization, and tracking of remote or concealed objects is an open problem in many fields, in particular when penetration through absorbing or scattering media is required [1][2][3][4][5][6]. A critical case is underwater detection, where many conventional detection techniques are often ineffective [7].…”

“…FIG.7. Underwater target detection: ∆r of S2 as a function of the depth of the target, a thin Al plate (105×73×3 mm 3 ), detected at 10 kHz, placed in position(2,3), above the sensor. The dashed horizontal line marks the "zero-level" compatible with no possible detection, corresponding to 100 mm underwater (190 mm distance from the sensors' plane).…”

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

“…The intention was to formally handle uncertain and imprecise or fuzzy knowledge, so to speak, in real world applications. This mathematical theory proved, as it was intended by the founder, to be quite versatile in handling every sort of application that involves manipulating or decisionmaking in imprecise or noisy environments [11], [17], [18]. A fuzzy system is defined by a set of conditional linguistic statements, the validity of which is assumed based on experience and can be expressed in a variety of ways [19]:…”