A Geoscientific Study To Identify Hydrocarbon Prospective Zones In Cauvery Basin

Introduction

With the increase in demand for petroleum products and diminishing indigenous production, it has become necessary to look for probable potential zones even in locales that were hitherto known as bleak prospects. Mesozoic sediments throughout the world are known for hydrocarbons and are potential source rocks for more than 50% of the world's hydrocarbon reserves. In India, significant hydrocarbon finding in the stratigraphic sequence has not been established, as the major part of the Mesozoic sediments is underlying the Deccan Traps. Detection and mapping of the Mesozoic sediments below the Deccan Trap has been a long-standing complex geophysical problem facing the oil industry.

This research work describes an experimental and manual study of hydrocarbon detection in the region of ramanathapuram district tamilnadu. In the experimental study of micro seepage signals detection in the, using Multispectral satellite image processing. Multispectral remote sensing is an emerging technology for the oil & gas industry. Since its first application, Earth Observation has seen an enormous breakthrough in a brand new field such as geosciences for hydrocarbon exploration: both the awareness of the micro seepage phenomenon and data processing methods for its detection have greatly improved in the last years. Results clearly show that the spectral anomalies identified from satellite are closely related to the known oilfields and that the micro seepage maps can provide new high-quality data to reduce exploration risk.

Literature Review

This section provides a critical review and analysis of different satellite image processing techniques for hydrocarbon detection. Abrams, M. A et al (2005) properly predict subsurface petroleum properties, interpretation of near-surface geochemical data must recognize many potential problems including recent organic matter input, transported hydrocarbons, bacterial alteration, mixing, contamination, and fractionation effects. Surface geochemical data should always be integrated with other geological data. Calibration datasets to determine the utility of near surface geochemical techniques within particular basinal settings are essential when evaluating prospects for hydrocarbon charge. Chandrasekhar, P et al. (2011) presents remote sensing and multi geo physical data was carried out over parts of Deccan Syneclise, for eliminating the inherent ambiguities associated with each of the individual methods, and to understand the hydrocarbon prospects.

The subsurface sections constructed using geophysical data such as gravity, electrical resistivity, deep resistivity sounding, magnetotellurics and seismic along various profiles were interpreted for identification of subsurface faults along with their stratigraphic associationRasheed et al., (2008) discuss the Microbial prospecting method for hydrocarbon research and exploration is based on the premise that the light gaseous hydrocarbons migrate upward from subsurface petroleum accumulations by diffusion and effusion, and are utilized by a variety of microorganisms present in the sub-soil ecosystem. Mingmin Chi et al. (2007), this paper addresses classification of multispectral remote sensing images with kernel-based methods defined in the framework of Semi Supervised Support Vector Machines (S3VMs).

In particular, analyzed the critical problem of the non-convexity of the cost function associated with the learning phase of S3VMs by considering different (S3VMs) techniques that solve optimization directly in the primal formulation of the objective function. The hydro carbon detection in all the above methodologies as discussed by various researchers pointed out that there is an issue to be solved. The proposed method as narrated in the introduction will attract and solve all the issues and enhance the effectiveness of the hydrocarbon detection system.

Problem Statement

• Less Attention has been paid to the value of alterations on petroleum system evaluation such as getting a better understanding of hydrocarbon seeps migration pathways.
• Need To interpret the soil and other anomalies which indicate the micro and or macro seepages of hydrocarbon from the subsurface geological structures
• Need to develop some newer concepts in remote sensing and Geographic Information System (GIS) applications for hydrocarbon research and exploration.
• Need To identify new oil/gas bearing zones using remote sensing and GIS techniques.
• Development of enhancement and classification algorithms and techniques needed for effective and efficient classification of Hydrocarbon data of satellite image processing applications.

Objectives

• The main objective of the study is to assess the hydrocarbon prospects of the Deccan Syneclise Basin Cauvery basin of Ramanathapuram coast based on the adsorbed soil gas analysis.
• The investigations soil samples collected from Cauvery Basin were analyzed for the presence of propane oxidizing bacteria using Standard Plate Count (SPC) method.
• To develop a comprehensive GIS data base by integrating remotely sensed data with subsurface information.
• In this research work introduce a linear iterative segmentation and unsupervised indistinct covering classification techniques for detect the hydrocarbon region from satellite images.
• In this work utilize versatile unsupervised Multi-Resolution Classification strategy for hydro carbon identification in ramanathapuram district using satellite image application

Methodology

The organization of this research work describes the application of hydro carbon detection techniques is shown in the figure 1. Modeling of a Hydro carbon detection From Satellite images using segmentation and classificationDesign two operating strategies namely UICC and VUMR Implement two operating strategies using MATLAB softwareObtain the results of Hydrocarbon Detection sensitivity, specificity and accuracyCompare the obtained Results with existing Hydro carbon Detection methodsDevelop the mathematical model of a proposed Hydrocarbon detection system.

Proposed Solution

In this research work uses a new technique which was devised with the help of GIS by analyzing the surface lineaments and subsurface linarites effectively. In this present study, a satellite image based analysis was conducted for extracting surface lineaments, and for the subsurface linearities, the basement linearities were extracted from seismic, magnetic, and gravity data. An orientation analysis of these surface and subsurface linear features was performed to detect the basic structural grains of the study area. The correlation between these structural grains and subsurface oil and gas traps was performed to understand the connectivity to the reservoirs. This work discusses in detail about the importance of using surface and subsurface lineament analyses for delineating hydrocarbon reservoirs in the ramanathapuram sub-basin of Cauvery Basin, Tamil Nadu India based on satellite image processing methods.

Proposed solution- I: Hydrocarbon indications in the soils of Ramanathapuram district, Tamil NaduCauvery Basin is considered geologically prospective for oil and gas reserves; however, a major part of the basin is covered by the Cauvery, hindering the exploration of Mesozoic hydrocarbon targets. The analyses of 1006 near-surface soil samples of Cauvery Basin show high concentrations of C1–C 4 hydrocarbons and propane oxidizing bacteria. The presence of significant concentrations of ΣC2+ hydrocarbons provides additional evidence of seepage induced anomalies. The methane isotopic compositions within the range of thermally derived methane may represent migrated thermo genic hydrocarbons. Further, carrying out integrated geochemical studies comprising of different surface and subsurface exploration techniques in the anomalous hydrocarbon zones of Cauvery Basin could probably aid to assess the true potential of the basin. From this study, it clear that the regions in around Ramanathapuram district look promising for future hydrocarbon research and exploration.

Proposed Solution-II: Hydrocarbon Extraction from Hyper spectral Satellite Image by Using Linear Iterative Segmentation and Unsupervised Indistinct Covering Classification (UICC) TechniqueIn This proposed solution describes an experimental study of Hydro carbon detection in ramanathapuram district using multi-sensor satellite time series based on linear iterative segmentation (LIS) and Unsupervised Indistinct Covering Classification (UICC). Results show that the spectral anomalies identified from a satellite are closely correlated to the known oilfields and that the micro seepage maps can produce new high-quality data to reduce exploration risk. Experimental results show that our algorithm offers the better performance regarding the computational complexity and the prediction precision in comparison with LIS and other remote sensing algorithms.

Proposed solution - III: Versatile Unsupervised Multi-Resolution Classification Method Based Hydrocarbon Extraction from Satellite Images. This proposed solution intended to enhance the spatial and spectral data of satellite images by detection hydrocarbon in ramanathapuram district (tamilnadu state) utilizing Versatile Unsupervised Multi-Resolution modeling classification approach. The proposed Versatile Unsupervised Multi-Resolution (VUMR) based method that automatically classifies the hydrocarbon regions from spatiotemporal remote sensing images. First, a kernel has designed according to the structure of multi-spectral and multi-temporal remote sensing data. Secondly, the Versatile Unsupervised Multi-Resolution framework with fine-tuned parameters has intended for training region samples and learning spatiotemporal discriminative representations. The performance of the proposed hydrocarbon detection method is validated through simulation using the Matlab software.

Versatile unsupervised Multi resolution Classification: The Versatile unsupervised Multi resolution algorithm is a speculation of the slightest mean square algorithm that alters network weights to limit the mean squared error between the original and classified outputs of the satellite images. The algorithm steps of Versatile Unsupervised Multi Resolution classification methods as follows.

1. Take an input satellite image from study region.
2. Make Preprocessing utilizing Resourceful Ethical Filter
3. Segmentation utilizing Multi-Scale Invariant Clustering Segmentation
4. Concentrate the Vector Zone Hydro Carbon Feature Extraction
5. Gathering the comparable components.
6. Apply the proposed classification to remove the hydro carbon region from segmented image.

Discover the Posterior Probability (PP) for each pixel. b) find out the threshold ranges for every pixels in classified regions

Results And Discussions

In this section gives the detail explanation if proposed hydrocarbon detection strategies. The investigations soil samples collected from Cauvery Basin were analyzed for the presence of propane oxidizing bacteria using Standard Plate Count (SPC) method. In this work also to propose unsupervised classification strategy has been actualized and assessed for its execution and the proposed techniques have been validated through simulation utilizing Matlab software. The remote sensing data provides synoptic view and repetitive coverage for thematic mapping of natural resources. GIS is built upon knowledge from geology, geography, cartography, computer science and mathematics and can be applied in any field, directly or indirectly. As compared with Unsupervised Indistinct Covering Classification and SVM methods the proposed versatile unsupervised multi resolution classification strategy was give perfect results of sensitivity, specificity and accuracy.

Conclusion

Cauvery Basin is one of the pericratonic rift basins located in the east coast of Tamil Nadu, India. The rifting has resulted in a series of horsts and grabens. The present study uses a new technique which was devised with the help of GIS by analyzing the surface lineaments and subsurface linearities effectively. Hydrocarbon prospective zones in the study area were eventually demarcated by integrating geology, soil alteration, geomorphology, surface lineaments, subsurface faults, geochemical anomaly, magnetic and gravity anomalies, electrical resistivity data and Hyperian data. The output was also cross checked with the existing oil wells. In this research work introduce two unsupervised classification strategies for a satellite image based analysis was conducted for extracting surface lineaments, and for the subsurface linearities, the basement linearities were extracted from seismic, magnetic, and gravity data. The two classification strategies are Unsupervised Multi-Resolution (VUMR) and Unsupervised Indistinct Covering Classification.

The present study was attempted using Hyperion data to identify hydrocarbon seepages based on mineral alteration in a part of Cauvery Basin. The results indicate that it is possible to identify and recognize certain oil/gas seepages based on the spectral characteristics.

Compared to conventional hydro carbon extraction methods with a proposed Versatile Unsupervised Multi-Resolution method achieving the best results for accuracy is 97. 72%, sensitivity is 98. 25 %, and specificity is 94. 02%· It is concluded that the surface indication of hydrocarbon detection with Hyper spectral study clearly point towards the presence of structures which in turn point towards the presence of potential areas for future exploration.