Deep-water agglutinated Foraminifera (DWAF) are investigated from Paleocene sediments recovered from IODP Hole U1511B in the northeastern Tasman Sea. The recovered foraminifera display exceptional three-dimensional preservation: they are relatively unaltered by sediment diagenesis and compaction.We examined 27 samples fromCores U1511B-45R to -47R, and recovered over 70 species of DWAF. The assemblage consists entirely of “cosmopolitan” forms originally described from the Carpathians, Caucasus, Trinidad, and the western Tethys, implying that there is no provinciality among DWAF faunas in the world ocean.

To achieve maximum economic revenues in gas-condensate reservoirs, an optimisation tool is employed to estimate the optimum well placement. Uncertainty analysis in gas-condensate reservoirs is a prerequisite requirement before the developing phase of the hydrocarbon reservoir. Contrary to most conventional reservoir development, well spacing optimisation in gas-condensate fields has received less attention due to a general assumption that optimisation techniques and computational methodologies applied to oil fields development can be applied to gas-condensate fields. Uncertainty analyses were performed using fourth-order factorial design on a domain of gas-condensate field's data to identify key factors affecting the production of condensates from heterogeneous and ultra-low permeability reservoirs. Well placement objective functions for gas-condensate reservoirs were optimised as functions of cumulative condensate production using genetic algorithms. With compositional modeling, exhaustive search mechanisms were employed to validate the results of our proposed optimisation tool. Results from the proposed optimisation tool was more economically feasible compared to that of the exhaustive search mechanisms and thus, could be employed as a much simpler, less exhaustive and economically feasible optimisation tool for well placement projects in gas-condensate reservoirs. In using genetic algorithms we concluded that most optimisation tools do not have both reliability and efficiency. Genetic algorithms optimisation tool was observed to be the most reliable method for gas condensate reservoirs though excessive number of simulation runs for large fields makes their application expensive. A more strategic approach was used to formulate objective functions whilst incorporating the effect of condensate banking in gas-condensate reservoirs.

In state-of-the-art production data analysis and computational methodologies applied to multiphase lease condensate flow, condensate-banking effect is usually neglected to enable easier analytic treatment. However, neglecting the amplified role of condensate banking in multiphase flow in unconventional formations may generate misleading analytical results and calculations during hydraulic fracture design and optimization. This work proposes a semi-analytical model eminently applicable to unconventional reservoirs to incorporate the effect of condensate banking in hydraulic fracture design and well spacing. Analytical models for Darcy flow above and below the dew point pressures were considered whilst estimating the optimum fracture design in gas condensate reservoirs using Schechter's approach and incorporating the effects of the condensate blockage radius. The validity of the series of proposed semi-analytical solutions that capture condensate-banking effect in unconventional reserves is verified by discussing a number of cases and comparison against full-scale numerical simulation data.

Contrary to conventional reservoir development, uncertainty analysis and design optimization of gas-condensate fields have received less attention due to a general assumption that oil field production data analysis and computational methodologies and techniques can be applied to gas-condensate fields. Understanding the uncertainties and performance of gas-condensate reservoirs is vital to predict production profiles. This paper investigates and reviews published literature of the most essential factors affecting gas-condensate production in a domain of real data from gas condensate fields. In this paper, uncertainty analysis was performed using third and fourth order factorial design (Box Behnken technique) on a domain of gas-condensate fields' data to identify key factors affecting the production of condensates from heterogeneous and ultra-low permeability reservoirs. Objective functions and response surface models for gas-condensate reservoirs were optimized as functions of cumulative condensate production incorporating the effects of condensate banking in gas condensate fields. A second-order surrogate model was designed by regressing compositional results and then optimized as functions of the ten main factors affecting gas-condensate reservoir production. Uncertainty analysis of gas-condensate reservoirs revealed that condensate blockage radius, reservoir permeability, well spacing, reservoir thickness; compressibility, initial pressure; fracture spacing and initial condensate saturation were the most significant parameters affecting condensate production. Validation results revealed that the surrogate models of gas-condensate recoveries could be used with good confidence to predict condensate values in heterogeneous and ultra-low permeability reservoirs.

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