Analysis of well tests from naturally fractured reservoirs by automated type-curve matching
- Istanbul Technical Univ. (Turkey)
Due to the diversity of existing idealized naturally fractured reservoir models, e.g. double porosity idealized naturally fractured reservoir models, e.g. double porosity models with transient of pseudo-steady interporosity flow, etc., and the large number of parameters needed to be estimated in these models, the analysis of well test data form naturally fractured reservoirs by the well-known conventional graphical techniques is often difficult if not impossible. In recent years, the computer-aided automated type curve matching techniques based on nonlinear regression methods (e.g. least squares, least absolute value, etc.) have been increasingly popular in estimating well/reservoir parameters from transient well test data. In this work, the authors explore in detail the applicability of nonlinear regression techniques to obtain the parameter estimates from well-test pressure data from naturally fractured reservoirs. They implement both the nonlinear regression technique based on L{sub 2} norm (least-squares) minimization and the nonlinear regression technique known as ``robust`` regression based on L{sub 1} norm (least absolute value) minimization. Important characteristics of these nonlinear regression methods are discussed. The applicability of the proposed methods is demonstrated by analyzing several sets of synthetic and field data.
- OSTI ID:
- 103866
- Report Number(s):
- CONF-950310-; TRN: IM9541%%305
- Resource Relation:
- Conference: 9. Middle East oil show and conference: optimizing resources in a cost-conscious and challenging environment, Manama (Bahrain), 11-14 Mar 1995; Other Information: PBD: 1995; Related Information: Is Part Of Middle East oil show: Proceedings. Volume 2; PB: 660 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Slug test data analysis in reservoirs with double porosity behavior
Simple procedures for imposing constraints for nonlinear least squares optimization