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In hydrocarbon exploration, rock physics analysis plays a key role by connecting seismic data to rock properties. Analysis of rock physics data enables geophysicists to understand how fluid content affects the seismic response and what they should look for to improve the chance of finding hydrocarbons. In the Nong Yao oil field, the use of rock physics and AVO analysis was used to improve the hydrocarbon prediction process. The preferred method starts with rock physics analysis of key wells. Fluid Replacement Modelling (FRM) is then performed across many wells in order to generate a predicted seismic response for different pore fluids (gas, oil and brine). The predicted AVO response is then calibrated against the actual AVO response from the seismic data from key wells in order to build a database. In the Nong Yao Field, over four hundred and fifty drilled data points from sixty-nine wells were utilized in the analysis. This database is analyzed in order to find the optimal combination of parameters for hydrocarbon prediction, which is then used to improve hydrocarbon prediction for future near-field drilling candidates. Near-field appraisal programs in the Nong Yao oil field are driven strongly by amplitudes and AVO, as rock physics analysis has shown that sands and shale lithologies can be easily discriminated based on acoustic impedance. Fluid prediction is more difficult based on acoustic impedance alone, as other factors such as variable sand thickness and seismic data quality mean that there is significant overlap between hydrocarbon and wet sands. Rock physics analysis has shown that AVO behavior can be included to provide better separation between hydrocarbon sands and wet sands. AVO signatures from all the data points are then analyzed using intercept vs gradient cross-plots. A background wet trend is defined with the clear observation that increasing distance from the background wet trend correlates to increasing chance of hydrocarbon fill. Data are categorized into weak, moderate and strong AVO response based upon their distance from the background wet trend and then this is used to modify the chance of success of near-field appraisal drilling targets utilizing conditional probability. This results in an increased chance of success of up to 20% in a strong AVO supported target and around 10% in a moderate AVO supported target. Targets are then quantitatively high-graded in an appraisal portfolio. |
