Quality-Assured Initialization Optimizes the Value of Reservoir Simulation

Abstract

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 160248, ’Optimizing the Value of Reservoir Simulation Through Quality- Assured Initialization,’ by Paul F. Worthington, SPE, Gaffney, Cline & Associates, and Shane K.F. Hattingh, SPE, ERC Equipoise, prepared for the 2012 SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 22-24 October. The paper has not been peer reviewed. Initializing a reservoir simulator requires populating a 3D dynamic-grid-cell model with subsurface data and fit-for-purpose interrelational algorithms. In practice, these prerequisites rarely are satisfied fully. Implementation of four key points has enhanced the authentication of reservoir simulators through more-readily attainable history matches with less tuning required. This outcome is attributed to a more-systematic initialization process with a lower risk of artifacts. These benefits feed through to more-assured estimates of ultimate recovery and, hence, hydrocarbon reserves. Introduction Initialization is the process of rendering a subsurface-rock/fluid model into a representative starting point for reservoir simulation within the constraints introduced by imposing a 3D-grid array. The effectiveness of a hydrocarbon-reservoir simulator depends on the reliability of its initialization, which, in turn, is governed by the quality of the underpinning reservoir description. The classical view of initialization establishes the correct volumetric distributions of fluids within the grid cells that represent the subsurface reservoir when it is at initial conditions of an assumed hydrostatic equilibrium. Yet a functional reservoir must store fluids and allow them to flow. Therefore, an initialization process that is based on static data must render these data dynamically conditioned so that the ensuing dynamic model can have the greatest possible meaning. It is prerequisite that the reservoir description be realistic and representative and that its conditioning for simulation be carried out in a manner that retains reservoir character in a workable format. These requirements are rarely satisfied in practice. Shortcomings can be traced to ambiguous terminology, inconsistent definitions of reservoir properties, inappropriate parameter selection, incomplete data sampling, inconsistent scaling up, cross-scale application of interpretative algorithms, erroneous identification of net reservoir, unrepresentative fluid analyses, and incorrect application of software options. The objective is to improve the synergy between static and dynamic reservoir models. The subject matter is derived from the authors’ studies of challenging field situations (i.e., from real solutions). The proposed refinements are directed at obviating the shortcomings stated previously. They are restricted to water-wet systems within which intergranular flow predominates. Pivotal Role of Porosity Analysis of core-calibrated well logs usually delivers total or effective porosity according to the adopted petrophysical model. Therefore, if total porosity is imported from a static model, as is common practice, knowledge of either the shale-volume fraction or the electrochemically-bound-water saturation is needed to convert total porosity to effective porosity. Porosity is used in dynamic simulation as part of a term in the fluid-flow equations, to estimate fluid volumes in place, to indicate rock type, or to indicate permeability.