A GIS-Supported Remote Sensing Technology For Petroleum Exploration and Exploitation
Bibliographic record
Abstract
Abstract This paper proposes a GIS-supported remote sensing (RS) technology for searching undiscovered oil and gas formations, and identifying potential petroleum exploration and exploitation targets. Remotely sensed surface lineament analysis, correlated with a variety of geoscience data, is applied for oil and gas exploration. A GIS technique is incorporated within the RS framework for manipulating the obtained data and improving effectiveness. This hybrid approach not only facilitates petroleum resources exploration, but also enhances the related spatial analyses, modelling studies, and systems planning. The method was applied to a case study in the Liaohe Oilfields in Liaoning Province, China, and reasonable and interesting outcomes have been generated. Introduction Many countries in the world continue to rely heavily on petroleum resources, due to rapid population growth and economic pressures. As a non-renewable energy resource with the nature of scarcity and depletion, petroleum exploration and exploitation has caused increased attention and concern for several decades since:distribution of petroleum deposits could be verified through exploration, andpetroleum supply may be increased to some extent by reasonable exploitation. Moreover, it seems quite evident that the period of massive discoveries of easily discovered oil and gas deposits had come to an end by the late 1960s(1). Since then, the exploration and development of petroleum resources have involved an enormous capital investment. Low efficiency exists in these processes due to limitations in technical and managerial effectiveness, especially in developing countries. This paper focusses on the provision of potentially more effective tools for petroleum exploration and exploitation. The process of petroleum exploration and exploitation requires the consideration, integration and updating of different types of information for creating a clear understanding of underground gas- and oil-bearing formations. Previously, the related information was collected, collated, and analysed through slow and painstaking manual methods(1). With the advent of aerial photography and satellite remote sensing, RS data has become a major source of the information required by decision-makers for petroleum exploration and exploitation. Meanwhile, various geological, geophysical, and geochemical data are now available from largescale geographical survey and laboratory analysis, with the provision of more detailed information about potential petroleum exploration targets. On the other hand, the remotely sensed data, together with other geoscience datasets, need to be properly managed, analysed, retrieved, displayed, and maintained. This could be accomplished through the introduction of geographic information systems (GIS) into the RS framework. In recent years, although remotely sensed data, together with geological and geophysical information, have been applied to many projects of petroleum exploration and exploitation(2, 3), few studies have been reported on integrated RS-GIS application. Extended from previous works, this study develops a RS-GIS framework to analyse correlations between remotely sensed lineaments data and subsurface geological and geophysical conditions for petroleum exploration and exploitation purposes. Its application to the Liaohe Oilfields in northeastern China is provided for demonstrating the applicability and effectiveness of this method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".