Two adjacent reservoirs in offshore oil fields have been evaluated using extensive data acquisition across multiple disciplines; several surprising observations were made. Differing levels of biodegradation were measured in the nearly adjacent reservoirs, yet related standard geochemical markers are contradictory. Unexpectedly, the more biodegraded oil had less asphaltene content, and this reservoir had some heavy end deposition in the core but upstructure, not at the oil-water contact (OWC) as would be expected, especially with biodegradation. Wax appears to be an issue in the nonbiodegraded oil. These many puzzling observations, along with unclear connectivity, gave rise to uncertainties about field development planning. Combined petroleum systems and reservoir fluid geodynamic considerations resolved the observations into a single, self-consistent geo-scenario, the co-evolution of reservoir rock and fluids in geologic time. A spill-fill sequence of trap filling with biodegradation helps explain differences in biodegradation and wax content. A subsequent, recent charge of condensate, stacked in one fault block and mixed in the target oil reservoir in the second fault block, explains conflicting metrics of biodegradation between C7 vs. C16 indices. Asphaltene instability and deposition at the upstructure contact between the condensate and black oil, and the motion of this contact during condensate charge, explain heavy end deposition in core. Moreover, this process accounts for asphaltene dilution and depletion in the corresponding oil. Downhole fluid analysis (DFA) asphaltene gradients and variations in geochemical markers with seismic imaging clarify likely connectivity in these reservoirs. The geo-scenario provides a benchmark of comparison for all types of reservoir data and readily projects into production concerns. The initial apparent puzzles of this oil field have been resolved with a robust understanding of the corresponding reservoirs and development strategies.
We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.
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