Recently, asphaltenes have been shown to form nanoaggregates in toluene at very low concentrations (10 -4 mass fraction). Subsequently, in situ analysis of a 3000 ft vertical column of crude oil by downhole fluid analysis (DFA) indicated that the asphaltenes in a black crude oil exhibit gravitational sedimentation according to the Boltzmann distribution and that the asphaltene colloidal size is ∼2 nm. Here, we perform a follow-up study of a reservoir black oil from a different field. The black oil in a 658 ft vertical column is analyzed by DFA and advanced laboratory analytical chemical methods. An asphaltene colloidal particle size is found to be ∼2 nm according to the Archimedes buoyancy term in the Boltzmann distribution. In addition, an equation of state (EoS) approach based on literature critical constants and molecular weights for asphaltenes gives an aggregation number of ∼8. Molecular compositional similarities between different oil samples were established with comprehensive two-dimensional gas chromatography (GC × GC). Likewise, results from electrospray ionization Fourier transform ion cyclotron resonance mass spectroscopy (ESI FT-ICR MS) of the samples are consistent with the oils being from the same equilibrium column of oil. The results herein support a growing body of literature indicating that asphaltenes in black oils form relatively tightly bonded nanoaggregates of a single size range. The similarity of results between asphaltenes in crude oil and asphaltenes in toluene points to a very limited role of resins in these nanoaggregates, in contrast to much speculation. The implications of this work on the determination of reservoir connectivity are discussed.

This paper describes the history matching and predictive case studies of twodeepwater Gulf of Mexico (GOM) fields using an advanced Bayes linear estimationtool.Advantages of the tool include significant acceleration of thehistory matching process, identification and quality measurements of multiplehistory matches, quantification of reservoir uncertainty, and an improvedunderstanding of reservoir performance.Additionally, a statisticalestimator of predictive simulation results is created to generate statisticallyvalid confidence intervals around performance predictions.This paperdescribes a practical workflow incorporating this tool to rapidly evaluatedeepwater producing gas fields, and illustrates its use to determine remainingfield potential and future development requirements of two fields, the Harrierand the Raptor Fields in the Pioneer Natural Resources-operated FalconCorridor. Introduction The deepwater GOM can contain fields with very prolific wells that can behighly profitable for an Operator.The loss of even one of these wells canadversely impact both short and long-term field production forecasts, thus cashflow and profitability.These impacts are especially significant whenthere are few, very high rate wells that contribute to the total fieldproduction.When such a well fails, it is crucial to understand the causesin order to determine how and if the situation can be remedied, the costnecessary to do so, and the risks involved.The goals are then tounderstand and reduce risks, to minimize cycle time and capital exposure, andto maximize profitability. If a well's failure to meet forecast expectations is attributed to reservoirperformance, a number of tools ranging from the very simple to the very complexcan be used to evaluate reservoir performance.The choice as to whichtools to use is dependent upon the amount and quality of data available, thecomplexity of the problem, the time available in which to make a decision, andthe magnitude of the capital required to execute the decision.Oftenhistory matching with 3-dimensional (3D) reservoir simulation is the tool ofchoice used to evaluate and explain production performance.However, thehistory-matching process can be very frustrating and time-consuming, even forfields that appear relatively simple in nature, because of the reservoirprocesses involved and the non-unique nature of the solution.[1]Consequently, much time and many resources can be spent in attempting toachieve even one history match.Frequently multiple solutions can be foundthat can satisfy history-match criteria but which yield divergent predictionoutcomes. Because of the high production rates in both the Harrier and Raptor Fields, rapid analysis and integration of production data were necessary to providequick answers to reservoir analysis and reservoir management questions, and toaddress well-intervention and deepwater rig availabilitydecisions.Pioneer selected 3D simulation and the implementation of anadvanced linear Bayesian tool to expedite the history matching and uncertaintyanalyses process.[2]3D static geologic models for both fields, built andupscaled for dynamic flow simulation prior to production start-up, had beenused for predictive simulations.Good quality pressure information frompermanent downhole gauges and daily gas production data were available for thecalibration of these models.Although there is a global workflow processthat encompasses the ‘seismic to simulation’ process, this paper focusesprimarily upon the dynamic history matching and predictive portion of theoverall process.