Reservoir Simulation Design Strategy for Next-Generation Multi-level Assisted History Matching
Authors: Mirzadeh S. , Chambers R. , Carvajal G. A. , Singh A.  P. , Maucec M. , Knabe S. , Al-Jasmi A.  K. , Hossam El Din I. H. 
Published: May 2016

This paper introduces the bases for the design of next-generation automated workflows to implement advanced assisted history-matching (AHM) techniques. The paper presents procedures for geostatistical modeling, high-end dynamic flow simulation modeling, and the use of streamline tracing and visualization to generate a basic (fundamental) model for AHM. The accuracy of the base model is essential because this model is the starting point of the AHM process; therefore, the quality of the AHM process is dependent on the base model.

The geomodel benefits from a combination of multiple lithotype proportion mapping (LPM) and plurigaussian simulation (PGS), which successfully represents complex, carbonate depositional settings with eight lithofacies and high-permeability channels. By honoring geostatistical variograms and core-log constraints, a reservoir model is generated with 1.4 million cells. The LPM indicated that 108 layers are sufficient to describe the vertical resolution of lithofacies in the reservoir. A three-dimensional (3D), three-phase, black-oil single-porosity numerical simulation model was developed. The dynamic model has three-phase relative permeability normalization that computes the effects of parameterizing rock type and permeability distribution in the static model. The model is complex, as it has 16 equilibrium regions and two pressure volume temperature (PVT) regions. The simulation model includes 49 wells in 5 waterflood patterns to match 50 years of production, 12 years of injection, and 8 years of forecasting. The model was optimized for minimum simulation time. The base case was used for a) closed-loop, multilevel probabilistic history matching with parameterization of geostatistical and reservoir-dynamic properties and b) dynamic model ranking (DMR) and uncertainty quantification based on predicted oil recovery factor (ORF).

This workflow was implemented at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. It generates faster and more accurate history matching updates, produces a high-resolution reservoir model with no upscaling, and calculates waterflood indicators, including voidage replacement, water injector efficiency, producer well allocations, sweep efficiencies, and recovery factors.

Introduction

With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive the future KOC operations to the next level of excellence, the operator's senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as "smart flows," to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at KwIDF in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013) and references therein.