Use of New Rate-Integral Productivity Index in Interpretation of Underbalanced Drilling Data for Reservoir Characterization

摘要

The prospect of dynamic reservoir characterization using flow and pressure data gathered during underbalanced drilling (UBD) is a powerful driver for implementation of UBD. The mathematical aspects of this complex, ill-posed, inverse problem have been the subject of research in the past decade. This paper focuses on practical, field implementation of UBD reservoir characterization, and the problems that consequently arise. Interpretation of data from UBD is made difficult by transducer errors, operational transients, and noise in data. It is therefore often very difficult to interpret the reservoir characteristics from the instantaneous productivity index (PI). In this paper, we introduce a parameter known as the Rate Integral Productivity Index (RIPI), which borrows from the theory of rate-transient analyses. The mathematical and physical basis of RIPI and its relationship to the instantaneous PI are presented. The behavior of RIPI and its implications for reservoir characterization are discussed. RIPI de-noises the data, and scales the problem such that the trends in data are more obvious, enabling robust interpretation of UBD data, and increasing the confidence in calls made regarding reservoir characteristics. Application of RIPI to field data is illustrated through several examples. Data acquisition, processing, and preparation for UBD reservoir characterization are discussed. In particular, the importance of filtering, de-noising, and identifying and excluding operationally induced transients is described. Limitations imposed by the data gathering methods are highlighted. It is shown that the ability of RIPI to reduce noise in raw PI data allows trends to be read more easily. The use of RIPI for static and dynamic characterization of supermatrix features (such as fractures, thief zones, etc.) is illustrated. The limitations of the approach and future trends are discussed.