Introduction and Application of the Key Technology of Subsalt Reservoir Prediction in Jingbian Area

Abstract

Jingbian area located in the middle and east of Ordos basin, where the dolomite gas reservoir develops in ${\text{Ma}_{5}}^{7}$, the seventh subsection of Ordovician Majiagou Formation, and gypsum and salt rock develop above it, forming good sealing conditions for the dolomite gas reservoir. Well T74 gained a high yield of millions of parties in ${\text{Ma}_{5}}^{7}$ recently, revealing it has a good natural gas resource. However, the drilling rate of industrial airflow well is low, and gas accumulation law in gas reservoir is complex, which can be characterized by tight dolomite reservoir, poor porosity, strong heterogeneity, rapidly lateral changes of the reservoir, and little difference from the geophysics of the surrounding rock, which leads to more difficulties in seismic prediction. In view of the difficulties above, forward modeling of elastic wave equation was carried out, typical wells of different types were chosen, and the logging curves were normalized to establish impedance model, and field seismic data acquisition was simulated to get model seismic data. Lastly, the characteristics of the favorable seismic waveform of reservoir were clearly defined. Then, binding study was performed by pre-stack inversion based on physical analysis, analysis on attributes of frequency domain, and the results of the study had the better performance in well location selection of ${\text{Ma}_{5}}^{7}$. The results of forward modeling of elastic wave equation show that a medium strong peak appears owing to gypsum and salt rocks of Ma ⁶₅ richly developed, and a weak complex peak in Ma ⁷₅ indicates good reservoir and high yield. Petrophysical analysis indicates that the gas ratio of Lambda to Mu (λ/μ) is below 0.8 and the high-frequency seismic attenuation and negative anomaly of fluid mobility. In the study, elastic wave equation forward modeling was applied in Jingbian area firstly and the waveform reflection mode of reservoir was identified, and the sensitive fluid mobility attribute was selected to detect the gas bearing property. Lastly, the gas reservoir was effectively identified, and the compliance rate of reservoir prediction was increased to 78%.

Copyright 2018, Shaanxi Petroleum Society.

This paper was prepared for presentation at the 2018 International Field Exploration and Development Conference in Xi’an, China, 18–20 September, 2018.

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