We extend single-porosity flow diagnostics to dual-porosity systems using a novel retardation factor R to account for the effect of fracture-matrix transfer on breakthrough times and displacement efficiency during two-phase flow in fractured reservoirs. R is based on an analytical solution for capillary-driven fluid exchange between the fractures and rock matrix. By linearizing R the time-of-flight τ^⋆ is adjusted to include fracture-matrix transfer and derive new metrics, the dynamic Lorenz coefficient (Formula presented.) to quantify the dynamic heterogeneity, and the dual-porosity sweep efficiency Ev^⋆ to estimate how efficiently the injected fluid displaces the reservoir fluid over time. We have tested different formulations of R across three case studies with increasing complexity to analyze the applicability and limitations of dual-porosity flow diagnostics. This analysis reveals that as long as flow in the fractures is faster than fracture-matrix transfer, dual-porosity flow diagnostics provide useful approximations when assessing displacement efficiencies and identifying the wells that are at most and least likely to experience early breakthrough. We show that (Formula presented.) and Ev^⋆ can be combined with stochastic optimization algorithms to improve the displacement efficiency in a 3D reservoir case study. Since a single dual-porosity flow diagnostics calculation requires less than 1 min while a full-physics simulation takes 2 h, we can now quickly screen a large parameter space to identify scenarios that need to be studied in more detail using full-physics simulations. Hence, our new dual-porosity flow diagnostics complement and accelerate state-of-the-art uncertainty quantification and optimization workflows for fractured reservoirs.

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Reliable production forecasting for fractured carbonate reservoirs is a challenge. Natural fractures, adverse wettability and complex matrix heterogeneity are all uncertain and can all negatively impact upon recovery. Ideally, we should consider different reservoir concepts encapsulated in a large ensemble of reservoir models to quantify the impact of these and other geological uncertainties on reservoir performance. However, the computational cost of considering many scenarios can be significant, especially for dual porosity/permeability models, rendering robust uncertainty quantification impractical for most asset teams. Flow diagnostics provide a complement to full-physics simulations for comparing models. Flow diagnostics approximate the dynamic response of the reservoir in seconds. In this paper we describe the extension of flow diagnostics to dual porosity models for naturally fractured reservoirs. Our new diagnostic tools link the advective time of flight in the fractures to the transfer from the matrix, identifying regions where transfer and flux are not in balance leading to poor matrix oil sweep and early breakthrough. Our new diagnostics tools have been applied to a real field case and are shown to compare well with full-physics simulation results.@en

When simulating foam floods, uncertainties exist in both the foam and reservoir parameters however the combination of these uncertainties are rarely incorporated in forecasting. Foam flooding is an effective enhanced oil recovery method that controls mobility, reduces gas relative permeability, delays gas breakthrough and helps improve sweep efficiency. Thus it is often used in highly heterogeneous reservoirs where significant subsurface uncertainties exist. Foam uncertainties exist as (a) foam stability is controlled by a number of factors such as critical water and surfactant concentrations, brine salinity, and oil saturation which are unknown in the subsurface spatially and (b) foam flood simulation requires the accurate description of multiple parameters used in the foam flood models which are unknown. This study quantifies and compares the impact of uncertainties associated with foam model parameters with the heterogeneity of a fractured carbonate reservoir, an analogue to the highly prolific Arab D formation. Foam model parameters are not known a-priori but can be tuned to experimental data, which ideally represent a range of foam regimes and reservoir conditions. Geological heterogeneities in fractured carbonate reservoirs are complex and include, matrix wettability, fracture density/orientation and initial saturation distribution. To quantify uncertainties geological uncertainties in fractured carbonate reservoirs, an automated framework was used to history match the production response of a fractured carbonate field by varying geological parameters. This accounts for the geological uncertainties during a waterflood, which are then combined with foam uncertainties from experimental analysis in the optimisation step, by optimising the mean response of the model to foam flooding across a range of geological and foam scenarios. Our workflow used a combination of Particle Swarm Optimisation for history matching and manual optimisation, the final results of which show a wide range of possible impacts of foam flooding from different but equally well matched reservoirs. The novelty of our work is that it demonstrates how parameters that control foam stability and hence effectiveness in mobility control are related to both foam properties and geological uncertainty. Carrying these uncertainties into foam model properties from core to field scale will translate into considerably more robust estimates of uncertainty when predicting field-scale recovery compared to simulations that only consider uncertainty in the reservoir model.@en

Fractured carbonate reservoirs are known for its complexity, this has big impact on production especially when EOR methods are used. Foam is known for its potential to improve recovery by diverting the flow to the poorly swept regions. In order to capture the uncertainties associated with foam EOR in fractured carbonates, multiple simulations of different geological scenarios must be tested.@en

This paper describes the application and testing of innovative dual porosity flow diagnostics to quantitatively rank large ensembles of fractured reservoir models. Flow diagnostics can approximate the dynamic response of multi-million cell models in seconds on standard hardware. The need for new faster screening methods stems from the challenge of making robust forecasts for naturally fractured carbonate reservoirs. First order uncertainties including the distribution and properties of natural fractures, matrix heterogeneity and wettability can all negatively impact on recovery. A robust multi-realisation approach to production forecasting is often rendered impractical due to the time cost for simulating many models. We have extended existing flow diagnostics techniques to dual porosity systems by accounting for the matrix-fracture exchange. New metrics combine the transfer rate with the advective time of flight in the fractures identifying risk factors for early water breakthrough and providing quantitative measures of dynamic heterogeneity. We have compared ranking a large ensemble of synthetic fractured reservoir models using dual porosity flow diagnostics and using full-physics simulation. The synthetic ensemble explores a number of different geological concepts around the fracture distributions, wettability and matrix heterogeneity which can. Not only does the flow diagnostic ranking agree well with the cumulative oil ranking the run time for the flow diagnostics is <0.25% of the total simulation time. This significant reduction in the time to compare models allows more time to spend running full physics simulation on the important and geologically diverse cases that offer the most insight.@en

In this study we have applied new dual-porosity flow diagnostics to a recently discovered offshore fractured carbonate reservoir undergoing charactersiation studies. Carbonate reservoirs are typically highly hetrogeneous, naturally fractured and often mixed to oil wet, all of these factors are uncertain and can negatively impact upon recovery. With few wells drilled at the time of this study significant uncertainty hinders robust decision making. A robust multi-realisation approach is rendered impractical as the run time for a single realisation of the dual-porosity model is in excess of several days. Instead of using brute force we have utilised grid based flow diagnostics as a fast screening tool to select reservoir models for further full-physics simulations. The CPU time of flow diagnostics is almost negligable. Flow diagnostics are numerical tests perfomed on the static model that provide the time-of-flight, tracer partitions, drained/swept volumes and well pairs. In addition, dual-porosity metrics link the advective flow in the fractures to transfer from the matrix, indicating regions where flow and transfer are unbalanced and hence at risk of early breakthrough. Over 30 flow diagnostic tests were performed in under 10 minutes, the equivalent screening would take weeks using simulation. Results have shown that the fracture intensity and wettability are the most signifcant uncertainties that impact upon transfer and recovery, this effect would be missed by an equivalent single-porosity model. Well placement in this reservoir is very sensitive; the results show the proposed placement is effective for the assumed yet uncertain facies distribution. This broad sensitity screening has guided the ongoing modelling strategy, in partcular pinpointing the need for detailed characterisation of the fracture and facies distributions. Flow diagnostics are hence an excellent way to complement production forecasting workflows by providing a tool for quickly ranking and selecting scenarios for further detailed full-physics simulation, allowing us to focus more computational resources on reservoir models that are of particular interest.@en

Carbonate reservoirs contain much of the world's remaining hydrocarbon resources. These reservoirs are challenging to characterise and forecast production due to uncertainty in the fracture distribution and wettability. Ideally a large number of models needs to be simulated to quantify uncertainty but, due to time constraints, typically only a small subset of scenarios is considered. A dual-porosity module with a flow diagnostic toolbox has been implemented into MRST by the Carbonate Reservoir Group at Heriot-Watt University. The new diagnostics provide useful information that approximates the reservoir dynamics and matches full-physics simulation results, but can be obtained in a fraction of the time needed for simulation. Using flow diagnostics hence enables the fast screening and ranking of vast numbers of static models for fractured reservoirs. This enables us to consider the full range of geological uncertainty whilst minimising the number of models required for traditionally heavy simulation tasks such as, production forecasting, optimisation and uncertainty analysis that rely on full-physics simulations. Flow diagnostics hence complement and extend knowledge obtained from conventional fractured reservoir characterisation, calibration, and simulation workflows.@en

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.@en