Streamline-Based History Matching for Multicomponent Compositional Systems

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 174750, “Streamline-Based History Matching of Arrival Times and Bottomhole-Pressure Data for Multicomponent Compositional Systems,” by Shusei Tanaka, Dongjae Kam, Akhil Datta-Gupta, and Michael J. King, Texas A&M University, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Streamline-based history-matching techniques have provided significant capabilities in integrating field-scale water-cut and tracer data into high-resolution geologic models. However, application of the streamline-based approach for simultaneous integration of water cut and bottomhole pressure (BHP) has been rather limited. In this paper, the authors introduce a novel semianalytic approach to compute the sensitivity of the BHP data with respect to gridblock properties. Introduction The streamline-based method has many advantages in terms of computational efficiency and applicability. The main advantage of the streamline-based method is that it is able to calculate parameter sensitivity with a single streamline simulation or post-processing of the grid-based finite-difference simulation results. The calculated sensitivity is comparable with the sensitivities computed from numerical perturbation or with adjoint-based sensitivities. It is possible to calculate the parameter sensitivity from a commercial finite-difference simulator by use of streamlines traced using the flux field. This allows accounting for detailed physics by means of finite-difference simulation while taking advantage of the power of the streamlines for sensitivity computations. Streamline and Parameter Sensitivity Streamline-based history matching starts with the tracing of streamlines from a given set of static and dynamic conditions. This process is outlined in detail in the complete paper. Amplitude, Travel-Time, and Generalized Travel-Time Inversion (GTTI) Methods The approach to minimizing the objective function through all the observed points and simulation results is defined as amplitude inversion. The travel-time inversion, instead, attempts to match single reference times such as water-breakthrough point or peak tracer response. The amplitude matching is general in terms of reduction of the objective function, because while the travel-time inversion reduces the objective function at only a single point, the amplitude matching can cover all the data points. However, the travel-time-based approach is often used because amplitude inversion is a highly nonlinear problem and presents difficulties in computational cost and in reducing the objective function when there is a large amount of wells and data points.