Understanding asphaltene gradients and dynamics of fluids in reservoirs had been greatly hindered by the lack of knowledge of asphaltene nanoscience. Gravitational segregation effects on oil composition, so important in reservoir fluids, are unresolvable without knowledge of (asphaltene) particle size in crude oils. Recently, the "modified Yen model" also known as the Yen-Mullins model, has been proposed describing the dominant forms of asphaltenes in crude oils: molecules, nanoaggregates and clusters. This asphaltene nanoscience approach enables development of the first predictive equation of state for asphaltene compositional gradients in reservoirs, the Flory-Huggins-Zuo (FHZ) EoS. This new asphaltene EoS is readily exploited with "downhole fluid analysis" (DFA) on wireline formation testers thereby elucidating important fluid and reservoir complexities.Field studies confirm the applicability of this scientific formalism and DFA technology for evaluating reservoir compartmentalization and especially connectivity issues providing orders of magnitude improvement over tradional static pressure surveys. Moreover, the mechanism of tar mat formation, a long standing puzzle, is largely resolved by our new asphaltene nanoscience model as shown in field studies. In addition, oil columns possessing large disequilibrium gradients of asphaltenes are shown to be amenable to the new FHZ EoS in a straightforward manner. We also examine recent developments in asphaltene science. For example, important interfacial properties of asphaltenes have been resolved recently providing a simple framework to address surface science. At long last, the solid asphaltenes (as with hydrocarbon gases and liquids) are treated with a proper chemical construct and theoretical formalism. New asphaltene science coupled with new DFA technology will yield increasingly powerful benefits in the future.

We describe here a test of a new technology for successful drilling of horizontal wells in thin oil columns. We constructed a three-dimensional (3D) earth model of the overburden and of the target reservoir layers on the basis of predrilling data and updated this model in real time on the basis of logging-while-drilling (LWD) measurements transferred to remote locations using the World Wide Web. This strategy allowed us to check and update the planned drilling trajectory continuously with all the information available at any given time. We used uncertainties in the depth of markers observed in a number of offset vertical wells to determine the uncertainty in the thickness of layers in the earth model. This 3D model comprised best estimates of the thicknesses and a covariance matrix that quantified their initial uncertainties. We then drilled a pilot well. Trajectory data, LWD logs, and resistivity images from downhole measurement-while-drilling (MWD) and LWD tools were transmitted in real time from the Simpson No. 22 drill site in Indiana to a prototype application running in Connecticut. As we acquired new measurements in the pilot well, we compared log curves predicted by the model to the measured logs. Our prototype allowed an interpreter to update the location of markers as the well was drilled; an update of the entire 3D earth model and its uncertainty was then automatically computed in near-real time. Quantified uncertainties are key in this stage to ensure that the model update is in agreement with all the data considered previously. This procedure was repeated while drilling the horizontal drain hole, which was successfully steered within a dipping 6-ft-thick layer for 808 ft. Our prototype also allowed for remote collaboration: 3D model updates, LWD data, and resistivity images were available to collaborators who were connected to the network and simultaneously ran copies of the prototype at additional locations. In particular, the remote availability of real-time resistivity images was key to the successful well placement, as these images show how the well trajectory follows the layering. Remote collaboration means that drilling decisions can be made collaboratively by a globally distributed team in a secure network environment. This can be a key capability for geosteering, especially in remote locations or when staffing is constrained. Introduction High-angle and horizontal wells offer the opportunity to tap previously unrecoverable hydrocarbons that occur in thin columns. To achieve this objective it is often necessary to precisely position the well with respect to fluid contacts and/or sedimentary layers. Frequently there is uncertainty in the disposition of these features, so that the target cannot be geometrically defined and real-time data on the position of the features are required to geosteer the well.1 We wished to test the application of novel completion technologies to drain oil from a very thin (originally 13-ft-thick) oil column in the Mount Vernon Unit of the Lamott Consolidated field, Posey County, Indiana (Fig. 1). To do this, we needed to drill an 808-ft-long horizontal well in oil-bearing sandstone.2 The East Mount Vernon Unit is operated by Team Energy and produces oil from the Tar Springs and Cypress sandstones. Most production is from the Mississippian Cypress sandstone reservoir. The previously existing vertical wells produce at a very high water cut (~ 95%) because of the thin nature of the Cypress reservoir oil-column. The Simpson No. 22 well was drilled first as a deviated pilot well, to penetrate the Cypress sandstone close to the planned heel of the horizontal section, and subsequently as a smooth build section (< 4°/100 ft) leading to a horizontal section in the Cypress reservoir (Fig. 2).

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This paper describes a new Downhole Fluid Analysis technology (DFA) being implemented in Latin America for improved reservoir management. DFA is a unique process in fluid characterization for improving fluid sampling, reservoir compartmentalization evaluation and support flow assurance analysis. It combines known and new fluid identification sensors, which allow real time monitoring of a wide range of parameters as GOR, fluorescence, apparent density, fluid composition (CH4, C2, C3-C5, C6+, CO2), free gas and liquid phases detection, saturation pressure, as well WBM & OBM filtrate differentiation and pH, which is key for real time contamination monitoring at the well site with the objective of representative sampling and reservoir compartmentalization analysis. This process is not limited to light fluid evaluation or sandstones. The combination of DFA Fluid Mapping with pressure measurements has shown to be very effective for compartmentalization characterization. The ability of thin barriers to hold off large depletion pressures has been established, as the gradual variation of hydrocarbon quality in biodegraded oils. In addition, heavy oils can show large compositional variation due to variations in source rock charging but without fluid mixing [1]. Using this method we present field DFA data acquisitions and integrate into numerical simulation modeling to conceptually evaluate the impact of fluid composition / properties gradation and compartmentalization in the productivity of some Latin America reservoirs. Introduction Exploration wells provide a narrow window of opportunity for collecting hydrocarbon samples to make development decisions; therefore, obtaining high-quality samples and performing an adequate fluid scanning along the hydrocarbon column is imperative whether the prospect is in deep water or on the continental shelf. That is, one can obtain a continuous downhole fluid log. This log records (vertical) composition variation along with some indications of compartments or connectivity. Testing well production is a common way to obtain fluid samples, but usually does not allow a detailed areal or vertical fluid scanning for compartmentalization, gradual variation of hydrocarbon quality or density inversion analysis, and is not always feasible for economic or environmental reasons. Downhole samples define fluid properties that are used throughout field development. Downhole Fluid Analysis technology (DFA) is a concept, rather than a specific tool. Currently, DFA relies on near-infrared spectroscopy (NIR) and new novel approaches. The details of NIR application for DFA have been described elsewhere [2, 3].