RU2544309C2 - Remote detection of deposits of hydrocarbons - Google Patents

Abstract

FIELD: physics, geophysics.

SUBSTANCE: invention relates to optical geological survey and can be used for detection of hydrocarbons at licensed plots. Aircraft survey of plot territory is executed when there is no snow cover. Note here that said survey is executed for the first time in the day time in spectral bands of 0.43-0.49 mcm, 0.5-0.59 mcm, 0.6-0.69 mcm, 0.7-0.9 mcm, 1.5-2.5 mcm, and for the second time in, by night in the bans of 8.0-14.0 mcm Territory buzzing is organised so that at least one route allows surveying of reference area whereat deposits of hydrocarbons exist. Registered digital images are subjected with the help of computer program to geometrical correction and geologically referenced, leveled in brightness and combined in single mosaic frame represented in cartographic projection. Low-contrast abnormalities are defined. For this, said computer program is used to subject every spectral-zone mosaic image is subjected to brightness normalisation and low-frequency filtration. Then, binarisation is executed on the basis of threshold. Note here that threshold is defined for every spectral zone over reference area of mosaic picture. Binary images of spectral zones are algebraically summed to get half-hue images wherein sections with maximum signal magnitude correspond to expected hydrocarbon abnormalities with program-defined geodesic coordinates.

EFFECT: higher validity of outlining the hydrocarbon abnormalities.

3 cl, 3 dwg

Description

The invention relates to the field of search for hydrocarbon deposits (HC) in the developed (licensed) areas using spectrozonal scanner aerial images. Licensed areas are areas of, for example, 500 km ² , represented on a cartographic scale from 1: 50,000 to 1: 100,000 and allocated by the Ministry of Natural Resources to detect oil or gas deposits.

The invention is basically based on the well-known fact that fluids, rising from hydrocarbon deposits through the earth's crust, not only create biogeochemical anomalies on the earth's surface, but also change its temperature. Biogeochemical processes lead to a change in the spectral brightness of soils, stones, and vegetation in different ranges of the electromagnetic spectrum [1-3]. Work to identify such anomalies using materials from aerospace imagery has been actively carried out in the USSR since the mid 80s of the last century. However, the small value of the useful signal, determined during the interpretation of texturally heterogeneous images, allows us to characterize the known methods as unreliable, leading to the release of a large number of false contours of the alleged hydrocarbon deposits. In particular, due to the distorting effect of the atmosphere, contrasts are reduced, which allow to separate areas with “normal” and inhibited vegetation, as well as areas with low temperature relative to the environment. In addition, satellite images in the thermal spectral range of 8-12 μm are characterized by low spatial resolution (the best spatial resolution of about 60-90 m is provided by satellite images of the Landsat system) and are suitable for the analysis of large regions at a scale of 1: 500000. All this limits the practical use of satellite imagery for the detection of hydrocarbon anomalies of small areas characteristic of licensed areas.

So, in the author's certificate [4], to detect underground inhomogeneities with different thermophysical properties, double thermal aerial photography is performed: the first after sunset, and the second at dawn. Unfortunately, the use of survey materials during the period of maximum heating and cooling does not make it possible to identify weak temperature contrasts, which are manifested only with general temperature alignment of images. In addition, the exclusion from the analysis of signs that appear in other spectral ranges did not allow the use of this method in practice. Similar disadvantages are inherent in the method [5].

In the patents and copyright certificates considered in [6, 7, 8], it is proposed to analyze the anomalous areas characteristic of hydrocarbon deposits by analyzing the vegetation cover shown in the photographs. For this, for example, in [6], space photographing of the territory is performed in one spectral zone. Abnormal areas are identified by topological features, compiled on the basis of coefficients of fractal dimension.

The certificate of authorship [7] performs photographing of the earth's surface in two visible spectral ranges of 0.6 and 0.7 μm in the spring, under the assumption that vegetation begins earlier on the abnormal areas.

In the copyright certificate [8], photographing is carried out similarly, but already in the three visible ranges of the spectrum 0.54-0.56, 0.65-0.68 and 0.75-0.77 microns in four time intervals of the growing season. By analyzing the contrasts of different images, the presence of areal anomalies is judged.

The main disadvantages of all three of the considered methods are:

- lack of a comprehensive analysis of video data in the visible and thermal ranges of the electromagnetic spectrum;

- the use of low-sensitivity space photographs to detect low-contrast anomalies;

- the absence of the operation of luminance alignment of individual frames and their "stitching" into a single image, tied to the coordinates and completely covering the license area.

These shortcomings, as well as the fact that support information about the territory with known hydrocarbon deposits is not involved in the analysis, make these methods unreliable.

In the method [9], in order to increase the reliability of the search for hydrocarbon deposits, it is proposed to select geological ring structures (GCS) based on aerospace, ground-based photographic, geophysical, geological and biochemical surveys. The selected GCS are ordered in accordance with the equation of the genetic series, combined with explored deposits and exploratory wells are drilled at the intersection of the ordered GCS. The advantage of this method is the fact that with its help in 1998 the Bolotnoye oil field was discovered. However, this method cannot be attributed to purely remote hydrocarbon search methods designed to detect hydrocarbon anomalies on the Earth’s surface, since it uses aerial photographs only to highlight geological ring structures. In addition, GCS mini and macro sizes are typical for licensed sites, and for their ordering according to the genetic criterion, it is first necessary to form a GCS of a global level, characteristic for a scale of 1: 500000.

In the patent of Ukraine [10], in order to increase the reliability of forecasting oil and gas deposits, a multispectral structural field method is used. The method uses the relationship of the characteristics of the landscape with the places of hydrocarbon deposits. The method consists in determining the presence of optical and altitudinal anomalies, using the results of comparing multispectral features, as well as analyzing the landscape topography, side field structure and surrounding background, comparing the anomaly data with the reference ones and evaluating the prospects of oil and gas deposits.

For the formation of a multispectral field of landscapes, the data of ground-based spectrophotometric measurements are used, as well as satellite imagery and photo-aerial photographs in the visible spectral ranges of 0.5-0.8 microns. Moreover, images of the earth's surface are used to isolate the same fractions of vegetation, and optical anomalies are determined during the joint processing of ground and remote measurements.

The need to perform manual spectrophotometric measurements on the entire licensed area, as well as the presence of a priori information about the structure of the landscape and oil and gas horizons characterize this method as expensive and difficult for practical use. Here, just as in the method [9], thermal imaging materials are not used in the search for anomalies, and spectrozonal images are not directly used to determine the areas of the diffusion yield of hydrocarbons, but only to compile a landscape map.

A direct search for hydrocarbons by remote sensing data is implemented in the RSDD-H method, which is used by Scorforth Limited [1]. However, the essence of the method is not described anywhere. Therefore, the closest analogue to the alleged invention is the method described in [11]. In accordance with it, a scanner thermal imaging (spectral range of 8-14 microns) aerial photographs of a known deposit and a surveyed area are first performed. The main condition for conducting the survey is the condition that the value of the useful signal exceeds the value of the variation of the thermal field caused by heating of the surface by solar radiation. The resulting digital images are subjected to radiometric correction, and then all the pictures are aligned with the average value of the brightness temperature. Anomalous areas are identified by the threshold criterion T> ΔT _then , where ΔT _then is the threshold value of the temperature contrast, calculated from the results of shooting reference areas containing and not containing hydrocarbon deposits.

The main disadvantage of this method is that it does not take into account other signs of the presence of hydrocarbons, manifested in changes in the spectral composition of soils, vegetative characteristics of vegetation and which are observed in the visible spectral region.

The purpose of the invention is to increase the reliability of determining the contours of hydrocarbon anomalies in the licensed areas due to the integrated registration of spectral features observed on scanner aircraft images of the earth's surface.

According to the proposed method, an aircraft scanner equipped with a GPS receiver, a device for determining the orientation of the aircraft by the roll, pitch and yaw angles and an on-board computer for recording digital images and navigation information associated with a single time scale is used to shoot the licensed area. The scanner has a linear scan in the angle with a viewing angle of 70 ° and provides shooting of the underlying surface in six spectral ranges: 0.43-0.49 microns; 0.5-0.59 microns; 0.6-0.69 microns; 0.7-0.9 microns; 1.5-2.5 microns and 8.0-14 microns. The radiometric resolution of the generated images is 10 bits, which allows you to determine the temperature of the earth's surface in the thermal spectral channel with an accuracy of no worse than 0.1 K.

Survey and reference sections are surveyed in six spectral ranges, overlapping routes at a time during the absence of snow cover twice in the afternoon in the spectral ranges: 0.43-0.49 microns; 0.5-0.59 microns; 0.6-0.69 microns; 0.7-0.9 microns; 1.5-2.5 microns and at night in the spectral range of 8.0-14 microns. At the same time, at least one of the day and night shooting routes must capture the supporting territory, on which there are oil or gas deposits. The survey is carried out from a height of 6-7 km so that the spatial resolution of the images is within 7-10 m, which corresponds to the topographic scale of the representation of objects in the image 1: 50000 ÷ 1: 100000.

Digital images registered by the on-board computer I _λj (m, n), where I is the brightness of a pixel with coordinates (m, n), λ is the serial number of the spectral range, j is the route number, are subjected to specialized processing in a computer program. Moreover, daytime images of the first five spectral zones and night images of the 6th spectral range are processed according to the same scheme, according to which the image of each survey path and each spectral zone is aligned in brightness, then the inter-path brightness differences are eliminated, based on navigation measurements, the scanning law, coordinates of reference and homonymous points, geometric distortions of each image I _λj (m, n) are corrected. Geometrically corrected images of all routes are transformed into a cartographic projection and combined into one mosaic image D _λ (x, y), where D _λ is the pixel brightness code (x, y) of the mosaic image in the λth spectral range. An example of a mosaic image for a single spectral range is shown in figure 1. The pixel coordinates of the mosaic image (x, y) are connected with the geodetic coordinates of the terrain (B, L) by the equations of cartographic design, which makes it possible to establish a one-to-one correspondence between the coordinates of the objects on the licensed area, map and image.

. Затем все спектрозональные изображения нормализуются по яркости, Due to the action of biogeochemical processes, the spectral and, therefore, reflective and brightness characteristics of the objects of the earth's surface (soils, vegetation) located within the reference area change with respect to the same objects that are located on territories without hydrocarbon deposits. Therefore, to detect small contrasts caused by the action of hydrocarbon anomalies in spectrozonal mosaic images, the images are subjected to brightness processing based on the parameters obtained from the analysis of the reference region presented in the image. To do this, according to known geodetic coordinates in the images, the center of the depicted reference area is marked on which production is already underway or potentially there are hydrocarbon deposits. Using software tools in interactive mode on the images D _λ (x, y), the outline of the reference area displayed on them is performed. In the contoured region, histograms of the distribution of pixel brightness are formed for each spectrozonal image. The histograms cut off the noise samples, and then calculate the ranges of changes in pixel brightness, $$D_{\lambda ,\min }÷D_{\lambda ,\max }$$

. Then all the spectral images are normalized in brightness,

$$D_{\lambda }^{*}\left(x,y\right)=\{\begin{array}{l}0,D_{\lambda }\left(x,y\right)<D_{\lambda ,\min },\hfill \\ \frac{D_{\lambda }\left(x,y\right)-D_{\lambda ,\min }}{D_{\lambda ,\max }-D_{\lambda ,\min }}\cdot 1024,\hfill \\ 1024,D_{\lambda }\left(x,y\right)>D_{\lambda ,\max }.\hfill \end{array}{D}_{\lambda ,\min }\le D_{\lambda }\left(x,y\right)\le D_{\lambda ,\max },$$

, $$\lambda =\overline{1,6}$$

- нормализованные по яркости изображения видимых и тепловых диапазонов спектра.Where $$D_{\lambda }^{*}\left(x,y\right)$$

, $$\lambda =\overline{\mathrm{one},6}$$

- normalized brightness images of the visible and thermal ranges of the spectrum.

, подвергается низкочастотной фильтрации с целью устранения шумов и выделения однородных по яркости областей, $${\stackrel{\wedge }{D}}_{\lambda }(x,y)=D{*}_{\lambda }(x,y)\otimes H$$

,Each normalized image $$D_{\lambda }^{*}\left(x,y\right)\left(\lambda =\overline{\mathrm{one},6}\right)$$

is subjected to low-pass filtering in order to eliminate noise and highlight areas uniform in brightness, $${\stackrel{\wedge }{D}}_{\lambda }(x,y)=D{*}_{\lambda }(x,y)\otimes H$$

,

- оператор свертки изображения с маской фильтра Гаусса Н. Пример отфильтрованного изображения $${\stackrel{\wedge }{D}}_{\lambda }(x,y)$$

приведен на фиг.2. Отфильтрованные изображения подвергаются пороговой бинаризацииWhere $$\otimes$$

- the operator of convolution of the image with the filter mask of Gauss N. Filtered Image Example $${\stackrel{\wedge }{D}}_{\lambda }(x,y)$$

shown in figure 2. Filtered images undergo threshold binarization

$$S_{\lambda }(x,y)=\{\begin{array}{l}\mathrm{one},{\stackrel{\wedge }{D}}_{\lambda ,\min }(x,y)\le {\stackrel{\wedge }{D}}_{\lambda }(x,y)\le {\stackrel{\wedge }{D}}_{\lambda ,\max }(x,y),\\ \mathrm{and}n\mathrm{but}he,0,\end{array}$$

- бинарное изображение, $${\stackrel{\wedge }{D}}_{\lambda ,\min }(x,y)$$

, $${\stackrel{\wedge }{D}}_{\lambda ,\max }(x,y)$$

- пороги бинаризации, рассчитанные по оконтуренным областям на основе отфильтрованных изображений $${\stackrel{\wedge }{D}}_{\lambda }(x,y)$$

. В результате бинаризации код яркости 1 будет присваиваться пикселям, имеющим такие же яркостные характеристики, как и пиксели опорной оконтуренной области. Where $$S_{\lambda }(x,y)$$

- binary image $${\stackrel{\wedge }{D}}_{\lambda ,\min }(x,y)$$

, $${\stackrel{\wedge }{D}}_{\lambda ,\max }(x,y)$$

- binarization thresholds calculated for contoured areas based on filtered images $${\stackrel{\wedge }{D}}_{\lambda }(x,y)$$

. As a result of binarization, a luminance code of 1 will be assigned to pixels having the same luminance characteristics as the pixels of the reference contoured area.

ложных областей предполагаемых углеводородных аномалий. Подобные ситуации случайны и проявляются по-разному в разных спектральных диапазонах. Например, ложная температурная аномалия, выявленная на снимке в спектральном диапазоне 8,0-14 мкм, не проявляется на снимке, полученном в видимом спектральном диапазоне 0,43-0,49 мкм. Поэтому для повышения надежности выполняется комплексный учет выявленных УВ аномалий во всех спектральных диапазонах. Для этого бинаризированные изображения шести спектральных зон алгебраически суммируются, $$S\left(x,y\right)={\displaystyle \sum _{\lambda =1}^6{S}_{\lambda }\left(x,y\right)}$$

, где $$S(x,y)$$

- полутоновое изображение лицензионного участка. На этом снимке участки с максимальным значением сигнала S с наибольшей вероятностью соответствуют углеводородным аномалиям с определяемыми компьютерной программой геодезическими координатами (B, L). Эти участки по геодезическим координатам наносятся на топографическую карту для последующего геологического и сейсмического подтверждения наличия на них УВ сырья. In practice, there may be cases when the reflective characteristics of objects on the earth's surface, subject to biogeochemical changes, may coincide with the reflective characteristics of objects of a completely different type. This will result in binarized images $$S_{\lambda }(x,y)$$

false areas of alleged hydrocarbon anomalies. Similar situations are random and manifest themselves differently in different spectral ranges. For example, a false temperature anomaly detected in the image in the spectral range of 8.0-14 μm does not appear on the image obtained in the visible spectral range of 0.43-0.49 μm. Therefore, to increase reliability, a comprehensive accounting of the identified hydrocarbon anomalies in all spectral ranges is performed. For this, binarized images of six spectral zones are algebraically summed, $$S\left(x,y\right)={\displaystyle \sum _{\lambda =\mathrm{one}}^6{S}_{\lambda }\left(x,y\right)}$$

where $$S(x,y)$$

- grayscale image of the license area. In this image, the sections with the maximum signal S are most likely to correspond to hydrocarbon anomalies with geodetic coordinates determined by the computer program (B, L) . These sections, according to geodetic coordinates, are plotted on a topographic map for subsequent geological and seismic confirmation of the presence of hydrocarbon raw materials on them.

наиболее достоверных с точки зрения наличия УВ участков доступен для контроля. Для этого изображение $$S(x,y)$$

инвертируется по яркости и отображается на экране монитора. Пример инвертированного изображения приведен на фиг.3. Здесь наиболее темные участки снимка характеризуют области предполагаемых углеводородных аномалий (максимальное значение S), области белого цвета - не перспективные для дальнейшего поиска, отдельно выделенные серые области описывают ложно выделенные участки УВ аномалий. Image search process $$S(x,y)$$

the most reliable from the point of view of the presence of HC sites is available for control. For this image $$S(x,y)$$

inverted by brightness and displayed on the monitor screen. An example of an inverted image is shown in figure 3. Here, the darkest parts of the image characterize the regions of the alleged hydrocarbon anomalies (maximum value of S ), the white areas are not promising for further searches, the separately highlighted gray areas describe the falsely identified areas of hydrocarbon anomalies.

The method has a good practical confirmation. On one of the identified anomalies in 2003, exploratory well 226 of Kungakskaya area was drilled, oil deposits accepted by the Central Concert Hall of the Russian Federation were identified. In 2004, on the identified anomaly, in terms of coinciding with the Amber structure, a prospecting well 403 of Krylovskaya area was drilled, on which an oil flow of 28.4 tons per day was obtained.

List of sources used

1. Balabayev D., Hutchison P., Tomin G. Surface footprints of the hydrocarbon trap / 2010 / www.scotforth.com.

2. Gupta P., Chakraborty R., Awasthi A. / Model thermal anomalies over petroliferous basins Indian Institute of technology Roorkee / OGJ August 2010, vol. 108, No. 28, pp. 72-75.

3. Lloyd F. / Finding, mapping temperature anomalies can aid oil, gas exploration / Exploration Inc., Houston / OGJ June, 2001, vol. 99, No. 23.

4. Lyashenko OV, Afanasov Yu.A., Melnikov I.G. / Method for detecting underground inhomogeneities with different thermophysical properties / Copyright certificate SU (11) 1383259 A1 G01V 9/00, 1988.

5. Lunev V.I., Parovinchak M.S., Ryumkin A.I. / Method for identifying areas promising for the search and exploration of hydrocarbon deposits / Patent RU (11) 2169934 (13) (51) IPC7 G01V 9/00, 1999.

6. Davydov V.F., Novoselov ON, Shcherbakov A.S. et al. / Method for detecting anomalies of the underlying surface / Patent RU (11) 2160912 (13) C1 IPC 7 G01V 8/00, 2000.

7. Alekseev G.N., Volchegursky L.F. et al. Remote method of searching for structures promising in oil and gas fields / Copyright certificate SU (11) 1495736 A1, G01V 9/00, 1989.

8. Rozhin L.A., Kozlov V.V. and other Method of remote sensing of the earth's surface / Copyright certificate SU 1716469 A1, G01V 9/00, 1992.

9. Lunev V.I., Parovinchak M.S., Rostovtsev V.I. A method of searching for hydrocarbon deposits / Patent RU (11) 2165633 (13) C1, IPC 7 G01V 9/00, 2001.

10. Pererva V.M., Teplyakov M.O. et al. Multispectral structural-field method for predicting oil and gas deposits / Patent UA 63073A (51), IPC (2006) G01V 9/00, 2004.

11. Zlobin E.L., Mozhaeva V.G., Mozhaev B.N. et al. Method for the search for hydrocarbon deposits / Patent RU (11) 2054702 (13) C1, IPC 6 G01V 9/00, 1996.

Claims (3)

     1. A method for remote search for hydrocarbon deposits, including surveying the territory of the studied area during the absence of snow cover from the aircraft during successive flying over overlapping routes using a highly sensitive multi-zone scanner in the visible and infrared regions of the spectrum, characterized in that the survey is carried out for the first time during the day in the spectral ranges 0.43-0.49 microns; 0.5-0.59 microns; 0.6-0.69 microns; 0.7-0.9 microns; 1.5-2.5 microns, and the second time - at night in the range of 8.0-14.0 microns, the airspace is organized so that at least one of the routes captures the supporting region, on which there are hydrocarbon deposits; registered digital images of each spectral range using a special computer program are geometrically corrected and georeferenced, aligned in brightness and combined into a single mosaic frame presented in a map projection, using the same program, each spectral zonal mosaic image in order to determine low-contrast brightness anomalies is independently subjected to brightness normalization and low-pass filtering, and then binarization based on a threshold, we define For each spectral zone along the reference region of the mosaic image, the binary images of the spectral zones are algebraically added to produce a grayscale image S (x, y), in which the sections with the maximum signal S correspond to the expected hydrocarbon anomalies with geodetic coordinates determined by the program.      2. The method according to claim 1, characterized in that the search for hydrocarbon anomalies occurs on the basis of signs that appear on airplane scanner images of the visible earth's surface - 0.43-0.49 microns, 0.5-0.59 microns, 0.6 -0.69 microns, 0.7-0.9 microns, 1.5-2.5 microns and thermal - 8.0-14.0 microns spectral ranges.      3. The method according to one of claims 1, 2, characterized in that the decision on the presence of areas containing hydrocarbon anomalies on the licensed area is made according to the maximum coincidence of the signs appearing in different spectral ranges on geocoded mosaic images.

Images