A Knowledge-Based System for the Identification and Generation of Relative Permeabilities of Reservoir Rocks

Abstract

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

The Fuzzy Expert Exploration Tool for the Delaware Basin: Development, testing and applications

“…The MUD system stored expert knowledge in a series of rules contained in a knowledge base. A second example of a knowledge-based expert system is the RELPERM software (Ali & Fawcett, 1996). This system contains expert-derived rules that help the user to acquire relative permeability models for use in reservoir simulators while minimizing the need for costly laboratory studies.…”

Collection, storage and application of human knowledge in expert system development

“…Expert systems and knowledge management techniques have been used successfully in the petroleum industry. For example, an expert system using fuzzy logic was developed for aiding in the completion of multilateral wells (Garrouch et al , 2004) and knowledge‐based systems have been developed to help measure relative permeability (Ali & Fawcett, 1996) and diagnose formation damage (Xiong, 2001). The FEE Tools and the CFS incorporate emerging technologies, including knowledge engineering in the development of the knowledge base and fuzzy logic in the inference process.…”