Hydrocarbon deposit mapping validation by the means of ground-based spectrometry, remote sensing and geophysical data

• Дата: 2019
• Автор: Titarenko, Olga
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine 2019
• Теми: validation; spatial data; oil and gas potential; remote sensing data; spectrometry
• Онлайн доступ: https://ujrs.org.ua/ujrs/article/view/151
• Назва журналу: Ukrainian Journal of Remote Sensing of the Earth

The probability estimation of oil and gas inside certain area is essential for decision making on the industrial exploitation of oil and gas bearing features. A quantitative assessment of the hydrocarbon contour mapping accuracy using ground-based spectrometric measurements, remote, geological and geophysical data requires a special validation procedure. Its purpose is to evaluate achieved accuracy and reliability as well as the conformance to specified requirements. The input data for validation of the hydrocarbon deposit contour by field spectrometry are the one points’ locations relative to the other contours detected by independent methods, such as remote, geological and geophysical. As the field spectrometry performed along spatial trace, the geometric drifts of other methods’ cross-points are estimated. The algorithm for the validation of hydrocarbon deposit contour mapping by field spectrometry, remote, geological and geophysical data is proposed in this paper. The algorithm was tested on over the Novotroitsky and East Rogintsy hydrocarbon deposits (Ukraine). Measurements along 14 spatial traces over the Novotroitsky’s deposit and 28 traces over the East Rogintsy’s one was carried out to perform validation. The average error probability was 0.28, which demonstrates an admissible reliability of hydrocarbon deposits contours’ mapping by field spectrometry data. The preliminary validation estimates engagement during the hydrocarbon deposits mapping provides the fact-based statistical consistency of the quantitative measurements received. In addition, it is possible to filter the outliers reasonable before final information product release, which will enhance the overall reliability.