We describe the development of a knowledge-based system to predict relative permeabilities to describe the flow of fluids in oil, gas or condensate reservoirs. The software applies heuristic knowledge and artificial intelligence techniques to identify the appropriate experimental methods for measuring the relative permeabilities, and to decide on the relevant mathematical models and computational steps to simulate the experiments. The selected models and computational steps are used together with the built-in database to generate the relative permeability data. Rules that relate the combination of field development scenario, fluid PVT properties, rock lithology and petrophysical properties are included in the knowledge base. The basis of the software is that, in some instances, precisely defined rules based on quality published data and our expertise can do better than deterministic and purely statistical methods. This view is especially true in areas with limited and/or poor-quality data, as currently exists in gas/condensate and gas/water relative permeability predictions. The paper describes the software design approach, philosophy and architecture. The mathematical and heuristic models used to generate the relative permeability data are briefly described. The target applications of the software are as follows:Tool to generate relative permeability and capillary pressure data for input to numerical simulators and material balance calculations;Tool to perform a series of "what if' calculations to determine the effects of lithology, fluids saturations and PVT properties, interfacial tension and velocity on endpoint saturations and relative permeability functions;Tool to analyse/interpret laboratory coreflood data;Tool to generate relative permeability data when coreflood data is not available or is incomplete (e.g. when only endpoint data are available); andTool for use by the reservoir engineer to design a special core analysis program for a new field or study. Introduction Relative permeability is used to describe multiphase flow in a porous medium. Such data are important input to many reservoir engineering calculations, providing a basic description of the way in which the phases will move in the reservoir. Definition of the flow process can have a significant effect on the predicted hydrocarbon production rate and duration and is important in calculating the volume of recoverable hydrocarbon reserves. The predicted production rates, the plateau level and duration, plus the expected water cut will all influence development plans. The number of wells, the balance between injectors and producers, the sizing of separation equipment, and design of facilities in general can all be impacted upon by the multiphase flow properties of the reservoir. Ultimately, together with many other inputs, relative permeability assists in determining reservoir economics, and hence guiding investment decisions. Although ways to determine relative permeability from measurements made in the field have been proposed, they are fraught with problems and have never been regularly used. The most common method for determining relative permeability has been laboratory special core analysis. Laboratory measurement of representative relative permeability data on a reservoir core-fluid system is a complex task. The experiments are costly, typically more than $100,000 each, and time consuming, often taking up to six months to complete. P. 219

A knowledge-based system has been developed which predicts relative permeabilities to describe the flow of fluids in oil, gas or condensate reservoirs. The software applies heuristic knowledge and artificial intelligence techniques to identify the appropriate experimental methods for measuring the relative permeabilities, and to decide on the mathematical models and computational steps to use to generate the data. The selected models and computational steps are used together with the inbuilt database to generate the relative permeability data which honour the physics of the flow system. Rules that relate the combination of field development scenario, fluid PVT properties, rock lithology and petrophysical properties are included in the knowledge base. The paper describes the parts of the software which address the complex problems associated with relative permeability predictions in gas condensate reservoirs undergoing pressure depletion. The current version of the software runs on a PC under the Microsoft Windows operating system and exploits fully the graphical user interface for data input and output. Introduction The increasing emphasis on optimising recovery from gas condensate fields and the extensive development and use of reservoir simulators for predicting reservoir performance are together creating a widespread need for reliable basic data on rock flow behaviour. In general, in reservoir study involving two phase flow, the relative permeability is the parameter with the major control on reservoir performance. Relative permeabilities provide a basic description of the way in which the phases will move in the reservoir. Definition of the flow process can have a significant effect on the predicted gas/oil production rate and duration, and is important in calculating the volume of recoverable hydrocarbon reserves. The predicted production rates, the plateau level and duration, plus the expected water cut will all influence development plans. The number of wells, the balance between injectors and producers, the sizing of separation equipment, and design of facilities in general can all be impacted upon by the multiphase flow properties of the reservoir in the near wellbore region. Ultimately, together with many other inputs, relative permeability assists in determining reservoir economics, and hence guiding investment decisions. Laboratory measurement of representative relative permeability data on a reservoir core-fluid system is a complex task. The experiments are costly, typically more than $100,000 each, and time consuming, often taking up to six months to complete. Accuracy is limited to the specific core samples and is bounded by narrow saturation limits. A fundamental theoretical approach to modelling multiphase fluid flow in porous rocks is prevented by the complex nature of the problem. Major difficulties arise in mathematically describing flow through a porous system where the lengths, diameters and connectivity of channels are largely unquantifiable. For gas condensate systems the issue is complicated further as the thermodynamic behaviour of a multicomponent system close to their critical region needs to be taken into account. As a result, the experimentally determined gas condensate relative permeabilities are few and usually present a wide range of scattering. Consequently, it is very difficult to determine a representative average function on any basis, with a reservoir unit basis being the most difficult. P. 637

Drill stem testing of low permeability reservoirs is challenging because high pressure drawdown around the wellbore readily lowers fluids below saturation pressure and creates two-phase flow into the wellbore. The fluids produced at surface no longer represent the original reservoir fluid. This paper shows the benefits of a careful methodology of data selection and equation of state (EOS) modeling to validate data used to characterize the reservoir fluid. Cases are examined where standard methods of capturing and characterising the reservoir fluid failed either because of sampling difficulties caused by two-phase flow within the reservoir. The first example is a lean gas condensate where there was a discrepancy between surface and bottom hole sample compositions. By an extended analysis of Gas: Oil ratio (GOR) over all the flow tests and taking a broader perspective of the welltest data, it was demonstrated the surface data were superior to the bottom hole samples. The second study is of a disguised volatile oil which had previously been classified as a rich gas-condensate based on welltest GOR. However, a review of the GOR versus rate and EOS modeling of reservoir saturation pressures gave a consistent indication that the native fluid was not a condensate but a volatile oil. In both examples the methodology of careful data selection followed by consistency of EOS modeling gave interpretations contrary to rule of thumb assumptions and revealed the true reservoir fluid compositions.

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