Flow Diagnostics for Naturally Fractured Reservoirs

Abstract In this paper we describe new and innovative flow diagnostics tools for dual porosity models for naturally fractured reservoirs. Our new diagnostic tools allow us to compare and rank large numbers of geological models based on their approximate dynamic response in almost negligible time. Fast ranking methods allow us to select a representative ensemble of models that quantify geological uncertainty for robust production forecasting via full physics reservoir simulation. Reliable production forecasting for fractured carbonate reservoirs is a challenge. Natural fractures, adverse wettability and complex matrix heterogeneity are all highly uncertain and can all negatively impact upon recovery. Ideally we should consider a large and diverse ensemble of reservoir models to quantify the impact of geological uncertainty on reservoir performance. However, the computational cost can be significant, especially for dual porosity/permeability models. A brute force approach using powerful workstations, clusters or cloud computing can be taken to reduce the time investment. But this is not always possible, rendering robust uncertainty quantification impractical for many asset teams. Often only a small subset of scenarios is considered which may collapse into a single base case, from which development decisions are made. Base cases often fail to predict future production, need frequent modifications, lack geological realism and provide incomplete risk assessments, often causing asset teams to miss economic opportunities. Flow diagnostics can provide dynamic reservoir information in a fraction of the time for full physics simulation. We propose a workflow where we utilise flow diagnostics as a ranking tool to complement forecasting using reservoir simulation throughout. Our approach addresses the model run time, allowing us to use standard hardware. Flow diagnostics solve simplified physics to approximate the dynamic response of the reservoir, from this we can calculate and visualize key dynamic properties (e.g., time-of-flight, drained and swept reservoir volumes, time-to-breakthrough, decline rates, sweet spots, well-allocation factors). Flow diagnostics provide robust indicators of dynamic heterogeneity that allow us to select a diverse ensemble of models that captures the range of uncertainty. In this work, novel diagnostics utilising physically based transfer models have been developed to account for the fracture-matrix exchange, which otherwise could only be obtained from lengthy simulation. A new Damköhler number based metric DaDP links the advective time-of-flight in the fractures to the transfer from the matrix. DaDP identifies fast and slow draining regions of the matrix, stagnant regions within the fracture network and wells at risk of water breakthrough. This information can subsequently be used to optimise well placement and rates to maximise production and delay water breakthrough.

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