Linking Geostatistical Methods: Co-Kriging – Principal Component Analysis (PCA); with Integrated Well Data and Seismic Cross Sections for Improved Hydrocarbon Prospecting (Case Study: Field X)
AbstractIn this era of globalization, the demand for energy is rising in tandem with social and economic development throughout the world. Current hydrocarbon demand is much greater than domestic crude oil and natural gas production. In order to bridge the gap between energy supply and demand, it is imperative to accelerate exploration activities and develop new effective and efficient techniques for discovering hydrocarbons. Therefore, this study presents a new method for integrating seismic inversion data and well data using geostatistical principles that allow for the high level of processing and interpretation expected nowadays. The main part of this paper will concern the preparation and processing of the input data, with the aim of constructing a map of hydrocarbon-potency distribution in a certain horizon. It will make use of principal component analysis (PCA) and the co-kriging method. In the case study of Field X, we analyze a single new dataset by applying PCA to every existing well that contains multivariate rock-physics data. The interpretation that can be extracted from the output gives us information about the hydrocarbon presence in a particular depth range. We use that output as our primary dataset from which our research map is constructed by applying the co-kriging method. We also rely on an acoustic impedance dataset that is available for a certain horizon to fulfill the co-kriging interpolation requirement. All of the acoustic impedance data and output data that result from the application of PCA in a particular horizon give strong correlation factors. Our resulting final map is also validated with information from proven hydrocarbon discoveries. It is demonstrated that the map gives accurate information suggesting the location of hydrocarbon potency, which will need some detailed follow-up work to enhance the distribution probabilities. This method can be considered for hydrocarbon prediction in any area of sparse well control.