Identification of Potential Oil Sites Using Remote Sensing & GIS Technology; a case study of Missakiswal area District Jhelum, Punjab Province

Saqib, R
Directorate General of Mines & Minerals, Punjab, Lahore, Pakistan

Rehmen, S
Lecturer, Department of Earth Science, University of Sargodha, Pakistan

Ajmal, M
GIS Analyst in Salinity Research Institute Pindi Bhattian, Punjab, Pakistan



Abstract: Present Research is focused to investigate the objectives for determination of indicators, to identify the surface rock exposure through satellite image and to detect the possible oil containing site of the study area. The petroleum exploration is considered an art in which many geological variables are gathered, using expensive and sophisticated methods. Afterwards data is carefully interpreted, to finally make predictions which are very important, since the investment that have to be made for an exploration are really high, and often large parts of the population depend on them. The present study is motivated by the idea to develop a method in which remote sensing data is used in order to optimize future investments, in the petroleum exploration, especially in remote areas; satellite images have played a major role to establish the geological mapping foundation. Purpose of the current study is to analyze the rock distribution and structural geology through most advanced techniques of remote sensing and GIS. This research will facilitate in future to analyze the surface characteristics of any other geological area, which would depict the similar characteristics by using GIS & RS technology. It will facilitate the geophysicist and geologist about geological mapping. Such type of prior mapping would save a huge amount of oil exploring companies and will become the basic for mapping for oil exploration before conducting surveys like seismic surveying, electrical logging, well logging, gravity meter surveying and to dig an experimental well, all these surveys take a huge budget of oil companies. It can provide the information; either oil is there or not.

Introduction
Exploration of hydrocarbon resources is really very important for any country. To identify these natural resources different organizations opt different techniques, like to send the crew in the field for initial level of geological mapping. This process took a long time and it becomes little bit difficult to get the synoptic view of the study area under feasibility mapping. Remote sensing & GIS technology is a latest tool to get the clear picture of the study area for geological mapping; it really works in far flung areas where mapping is not easy through ordinary survey techniques. As there are many satellites in space to provide the data of surface but the contributions of Landsat Thematic Mapper(TM) is of great importance in the field of geology and natural resource management. This tool of remote sensing is of pivotal nature in this research to identify the structural features and Lithology of the study area near Dina, District Jehlum.

Current Research is focused to determine possible indicators for oil detection like the structure of the area including the anticline, folds and fault. These structures are not easy to map on foot but through this technique it is very easy to establish the basic rule in future to carry out such type of mapping. The second objective of the study was to analyze surface rock exposure through satellite image, particularly the rock type of this area. The field visit of few sites in this area was necessary to achieve this objective to get the ground control points of major rock lithology. The third and final objective of this research was to detect the possible oil containing site of the study area by combining the data of structural geology and rock lithology.

This paper reports original research for the first time in the field of Remote Sensing and GIS in Pakistan, which will open a new avenue for geologists in the field of Oil sites identifications and mapping. Oil industry will get benefit from this research in future and this will also facilitate the rest of geological mapping applications.

Geographical location of study area
The geographical location of study area is in the upper side of Dina (a small town) 17.7 kilometers north west of city of Jehlum on longitude of 73’58”E and latitude of 33’2”N.It is linked with Dina on one side and Rawalpindi on the other by the Grand Trunk Road and the north western railway both running from Peshawar to Lahore. The climate of the area is hot in summer and dry cold in winter. The average annual rainfall is 880 mm and temperature during the winter season remains between 8 degree centigrade to 20 degree centigrade which shoots upto 42 degree centigrade during summer. Its elevation varies from 1,000 to 2,000 ft (300 to 600 m) in a system of residual hills and hillocks formed from glacial debris as remnants of the Ice Age and comprises mostly on sedimentary rocks of tertiary origin. (See Figure 1)


Figure 1 study area overview

Methodology
The methodology, adopted for this rigorous scientific work is to explore the study area for oil sites detection by developing a new technique of remote sensing along with Geographical Information System (GIS). It was developed by using Landsat TM5 image (comprised on seven bands) and this image was processed on the basis of sample training (sample rocks) sites collected through Global positioning System (GPS) data. The collection of sample training sites is based on the field knowledge; particularly the geology of that area and supervised classification method is applied, whereas the structural geology is interpreted on the basis of visual characteristics of this study area. An overview of the methodology can be visualized in Figure 2. This figure clearly depicts the entire procedure, right from the data acquisition to data processing and to generate end results.


Figure2: Data flow diagram of methodology

Satellite data acquisition & Georeferencing
The satellite image used for this research work is obtained from Landsat TM5 sensor. Fisher (1977) stated about Landsat system

“The Landsat system (originally named ERTS; Earth Resources Technology Satellite) was primarily orientated towards land-use purposes but was also designed to test four geological hypotheses (1) that some dynamic geologic phenomena could be better viewed in a time-lapse mode, e.g. sedimentation and glaciation; (2) that colour would prove useful in mapping rock types, geochemical anomalies and alteration products; (3) that some geological and hydro-geological features are only intermittently visible; (4) that large structures exist on the Earth's surface which because of their size have gone unrecognized by ground or aerial survey”.

The satellite has total seven wavelength regions to get the maximum surface information. The data was acquired in the month of May so it was already a cloud free and there was no need to go through the image enhancement processes, furthermore there is no mighty mountains which would create big shadows or hindrance in the reflection of sunlight back to sensor onboard. The Image has spatial resolution of 30×30 meter and it covers an area of 10×10 kilometer. The satellite image is was not geo-oriented; to bring it according to a universal reference system, the process of georeferencing is carried out on software ER Mapper 6.4 evaluation version. This process begins with a geocoding wizard and involves five steps, after georeferencing the image is oriented according to a geometric projection system.

Sample Training Sites
The Sample Training sites were chosen according to the coordinates collected from field. For this, the area from which the sample trainings are selected was surveyed; and ground control points of different rock types obtained through Global Positioning System (GPS) receiver. These reference points were collected with a minimum error of 3 meters with the help of Magillion GPS receiver. Hence four types of rocks are selected as sample training with respect to their Ground Control Points (GCP). The reference sample sites coordinates are here in table 1.

Table 1: GPS data detail

Structural Geology & Lithology Interpretation
Field observation strengthened the knowledge about the sample training sites (see table 1) to identify different rocks and their correlation exist with structural geology. The field view provides information about four types of sedimentary rocks which are unevenly distributed in the area. These include limestone, soft sandstone, compact sandstone and shale. Distribution pattern of rocks is analyzed by their respective coordinates. Each of these sample classes are described in table 1. Limestone is the major reservoir rock, this sort of rock is more distributed in the image, and limestone is actually a series of rocks composed of mainly calcium and magnesium carbonates, second type of rock which was calculated as soft sandstone with a large grain size and soft texture. The texture and grain size of the soft sandstone facilitate the seepage of oil (Leverson, 1953). This was taken according to its coordinate values covering the most part of the area. The shale is distributed through out the area. The distribution of shale in the area is mixed up with other types of rocks. the compact sandstone is sparsely distributed in the area, actually it is in very small amount, it possesses little hard texture comparatively the soft sandstone and it might not be a good reservoir rock, due to its low permeability and hence the capillary action of the rock is not enough to support the seepage of the rock.

The structural geology of the area is not so much complex. The major structural element in this area is the anticline which runs from North-East (NE) to South-West (SW), the inclination angle and the regional slope of the anticline is toward SW, the anticline northern face possesses the soft sandstone and lower part of it possesses soft limestone and shale is distributed among these two types of rocks. Above the anticline is the stratigraphic folding pattern which runs parallel to the anticline, it is the simple pattern of folding which normally posses the limestone, soft sandstone, compact sandstone and shale. A fault line is also there which is running parallel in between the anticline and the simple stratigraphic folding pattern, this fault line physically disappear before the base of the anticline but it may extend to the base. So the three major structural features are there which support each other in this regard of surface indication. It was found that there is the combinational reservoir traps. The rock type distribution and structural geology correlated with each other.

The existence of combination trap is predicted there on the base of anticline and the fault line which is extended towards its base. Limestone is deposited there at the base and the structural geology was interpreted by the visualization of image by using the digitization of the general structure of the area which can be easily visualized and distinguished. Three main themes of the structural geology were distinguished according to their physical appearance and on the basis of field knowledge. Figure 3 is a complete demonstration of the structural geology.


Figure 3: Structural identification of surface geology

Supervised Classification
One of the main steps in this research is the partitioning of the feature space. Supervised classification is realized by the spectral characteristics of the classes by identifying sample area. The information of sample classes is derived from the training samples. Four different types of spectral signatures are identified in the supervised classification, each of them were taken on the image according to their respective coordinates. Coordinates in the field were taken in Lat/Long with WGS84 graticule system. So to classify the samples ER Mapper viewer window, lat/ long were assigned to the sample area. The main and most important pre-processing in supervised classification is the selection of reference class or training sets in the image .The aim of selection of this reference class is to obtain sets of spectral data that can be used to determine the decision rule for the classification of each pixel in the whole image data set. So on the basis of this reference class the whole image was classified. The selection of this reference class was done on the basis of field knowledge. Spatial distributions of different rocks were taken by using GPS. Four different classes of rocks were taken from the field (reference table1). Pixels on these positions on the image were taken as reference class for soft sandstone, shale, limestone, compact sandstone (see Figure 4).


Figure 4: Spectral curves of different rock sample

The color composite of the image is made by combining bands 7, 2 and 4 in order to understand the best rock distribution of the rocks and to visually interpret the image by using the digitization technique. Figure 5 show process of image classification started with the creation of false color composite by combining three bands i.e. 7, 4, and 2. Four major reference classes were taken into account. For the selection of reference class of limestone is not a difficult task, because it can visually be taken from the image by using very different spectral response as other classes. Four different classes were selected on the image according to their respective coordinates on the ground collected by GPS.


Figure 5: False Color composite

Each of these classes was assigned different color. Now the image was ready for supervised classification. For classification algorithm “minimum distance to mean classifier (MDM)” was taken. During classification the Euclidian distance from an unknown pixel to various cluster centers are calculated. The unknown pixel is assigned to that class to which the distance is least this is the most expensive and accurate classifier. In image different rocks have been classified, red color is assigned to limestone, this sort of rock is more distributed in the image, lime stone is actually a series of rocks composed of mainly calcium and magnesium carbonates. This material is retrieved from aqueous solution either by chemical precipitation or as a result of organic extraction.

The second type of rock which is represented through green color on the image was calculated as soft sandstone, with a large grain size and soft texture. The light blue color is for the shale which is distributed through out the image. The distribution of shale on the image in very small pieces is result to be the differential erosion of the sandstone. In orange color the compact sandstone is depicted on the image actually it is in very small amount, it possesses little hard texture comparatively the green soft sandstone and it might not be a good reservoir rock, due to its low permeability and hence the capillary action of the rock is not enough to support the seepage of the rock. (Figure: 6)


Figure 6: Classified Image of Study Area

Conclusion
A high level of geological expertise required suggesting the potential sites for drilling but the potential site is identified here on the basis of following logical reasoning:
  1. Presence of a reservoir rock and the existence of pore space in the rock.
  2. Regional slope of the strata and the anticline, which is toward the SW of the image
  3. Simple and permeable underground formation to become a trap, folded into an anticline.
Potential sites are identified on the basis of correlation of structural geology with respect to their rock type distribution three target locations are identified. These are the sites which possess the most important reservoir rock limestone and the other one soft sandstone which possess the capillary action due to soft texture and the capability to enhance the seepage of liquid. The first target location is identified on the south east of the anticline; this area is occupied by the limestone and soft sandstone which incorporate with anticline and the rest of the structural pattern in this area. The second target location identified on southern base of the anticline the most essential factor in this site is that a fault line extends from northern area towards south western it extends towards the base of anticline, where it makes the combinational reservoir trap. The third one is identified on the western part of the limestone impregnated area with simple folding. So the three target locations are likely to be drilled for experimental well. (Figure 7)


Figure 7: Targeted Location for experimental wells

Notes on Contributors
Rizwan Saqib: is GIS Development Officer, in Mines & Minerals Department, Government of Punjab, Pakistan, besides the job he is involved in an M.Phill research in university of the Punjab, Lahore. Has a strong determination to understand the surface geology relation with satellite images. Remote sensing geology is one of the favorite subject of his aptitude Saif-ur-Rehmen: is Lecturer in Department of Earth Sciences, University of Sargodha, the sedimentology and stratigraphy is one of the best area of interest. He is an M.Phill Research fellow in the University of the Punjab. Ajmal M: is GIS Analyst in salinity research institute in Pindi Bhattian, his area of research is mapping and GPS.

References
  • Ahmad, Z. and Siddiqi, R. H., 1992. Minerals and rocks for industry. (Vol. 1). Quetta: Geographical survey of Pakistan.
  • Bakker, W. H. and Lucas, L. F., Principles of remote sensing.
  • Directorate of industries and mineral development., 1992. Pre-investment study: Jhelum district 1992. Lahore: Directorate of industries and mineral development.
  • Gupta, R. P., 2003. Remote sensing geology. (2nd ed). New Delhi: Springer.
  • Jensen, J.R., 2000. Remote sensing of the environment: an earth resource perspective. Delhi: Dorling Kindersley.
  • Kazmi, A. H., 1990. Significance of the coal resources of Pakistan. Quetta: Geographical survey of Pakistan.
  • Kennedy, M., 2002. The global position system and GIS: An introduction. Boca Raton: CRG Press.
  • Leverson, A. I., 1953. Geology of petroleum. London: Oxford press.
  • Peters, E. R., n.d. The use of multispectral satellite imagery in the exploration for petroleum and minerals. London: Hunting Geology and Geophysics Limited.
  • Philip, H. S., 1978. Remote sensing: the quantitative approach. London: British Library.
  • Potwar Plateau., 2009. Encyclopædia Britannica Online http://www.britannica.com/EBchecked/topic/472944/Potwar-Plateau, Retrieved April 11.
  • Potwar Plateau., 2009. Wikipedia. http://en.wikipedia.org/wiki/Potohar#Geography, Retrieved April 11. Retrieved from http://rst.gsfc.nasa.gov/sect5/sect5_5.html
  • Saqib, R., 2004. Detection of potential oil sites using remote sensing and GIS technology: a case study of Missakiswal. Master in GIS. University of the Punjab, Lahore. Unpublished
  • Schowengerdt, R. A., 1997. Remote sensing: models and methods for image processing. 2nd ed. New Delhi: Elsevier.
  • Shah, S. Z. H., 2006. The minerals of the Punjab. Lahore: Heritage Publishers.
  • Thompson, B. A., 1926. The significance of surface oil indicators. (12), 603-622
  • Thompson, B. A., 1950. Oil filed exploration and development. London: Technical press.
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