Gasser-Dorado, Julien (IFP Energies nouvelles) | Ayache, Simon Victor (IFP Energies nouvelles) | Lamoureux-Var, Violaine (IFP Energies nouvelles) | Preux, Christophe (IFP Energies nouvelles) | Michel, Pauline (IFP Energies nouvelles)
SAGD is commonly used as a thermal EOR method to produce heavy oil. However it suffers from the production of acid gases formed by aquathermolysis chemical reactions that occur between the steam, the sulfur-rich oil and the mineral matrix. The objectives of this paper are to take advantage of a comprehensive chemical model coupled to compositional thermal reservoir simulations to predict and understand the H2S production variation at surface according to the type of reservoir.
Thermal reservoir simulations coupled to both a SARA based 10-component / 5-reaction chemical model fully calibrated against laboratory data and a compositional PVT are used to simulate SAGD processes on heavy oil fields in Athabasca, Canada. Numerical results are then analyzed to provide a comprehensive analysis of the mechanisms leading to in-situ H2S generation and its production at wellheads based on compositional thermal simulations coupled to a fully laboratory calibrated SARA-based chemical model. Composition of the pre-steam, post-steam and produced oil are compared to understand the effect of the aquathermolysis reactions. The impact of heterogeneities on H2S production both in-situ and at surface can also be observed and explained, especially the variations in vertical permeability. Then simple reservoir models with two facies are used to further understand the impact of heterogeneities on H2S production at surface. Overall heterogeneous cases show important changes in the temperature distribution, fluid flows, reactions kinetics and steam chamber shape that lead to H2S production variations at surface. This detailed description of the involved mechanisms in acid gases production will allow operators to better forecast their H2S risks according to their reservoir properties.
This section discusses the impact of vertical variations in permeability and the effect of gravity on simple 2D reservoir situations in which the areal effects are ignored. Gravity effects always are present because for any potential waterflood project, oil always is less dense than water, even more so after the gas is included that is dissolved in the oil at reservoir conditions. The discussion below does not include the Pc effects on vertical saturation distributions. Through countercurrent imbibition, Pc effects help to counteract nonequilibrium water/oil saturation distributions. The mathematics of including Pc effects makes the problems too complicated for inclusion here.
Two upscaling exercises performed in 2013-14 and 2017-18 on two onshore green fields with conventional to viscous oil are presented, for which the upscaling tried to compensate the effects of grid coarsening, in particular the increase of numerical dispersion and the decrease of heterogeneity. Our methodology was to adjust the water/oil relative permeabilities called pseudo KRs in the coarse scale simulation, in order to reproduce the behavior in terms of pressure, rates, saturations and concentrations of the fine scale model, which was using microscopic rock KRs based on laboratory data.
As the upscaling depends on the fluid injected, it was done separately for waterflood and polymer flood. When done with polymer flood, the concentration of polymer had to be history matched also mainly by adjusting the Todd-Longstaff mixing parameter in addition to the KRs. As upscaling is case dependent, it was performed on several geological models, varying heterogeneity and grid size, but also rock KRs and even precocity of the polymer flood after some waterflood, to test the robustness of the approach.
It was found that pseudo-KRs for waterflood could be slightly degraded for viscous oils, whereas the upscaling was more neutral for conventional oils. This correlates well with field observation for viscous oils, where water production occurs generally a bit quicker than what numerical simulation predicts when using rock KRs, in absence of upscaling.
For polymer floods, which were considered in secondary or early tertiary mode, pseudo KRs were generally improved, mainly because the polymer steepened the saturation fronts, which can be well represented only with small lateral grid size.
The result of both upscaling exercises was that the increment of polymer flood versus waterflood was noticeably higher when computed on high resolution modelling. This is equivalent to saying that when using pseudo KRs resulting from this high resolution matching, the polymer increment on coarse grid is significantly higher than if computed without pseudo KRs. This improves the economic evaluation of the project, increasing the willingness to de-risk and implement early polymer floods on these fields.
Al-Rudaini, Ali (Heriot-Watt University) | Geiger, Sebastian (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Maier, Christine (Heriot-Watt University) | Pola, Jackson (Heriot-Watt University)
We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected.
Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator.
Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low.
The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.
The Alvheim field, offshore Norway, has subsea wells with long horizontal branches completed with sand screens. After 10 years of production, water production starts to constrain the oil production. Mechanical water shut-off is impossible in these wells, hence other methods are of interest. In a well workover in 2013, two high-viscous polymer pills were bull-headed and squeezed into the reservoir. The well productivity was reduced with around 50% while the water-cut dropped and pointed to potentially 3 mmstb of extra oil recovery. A research study was initiated with the objectives to understand the changed well performance and if polymer bull-heading can be a future method to reduce water production and enhance oil production.
An experimental laboratory program started with filtration tests of polymer solutions based on the polymer used in the well operation. Core flood experiments were performed by injecting polymer into two parallel mounted cores, then back producing these individually with either water or oil. Several combinations of parallel cores were tested with polymer injection: high vs. low permeability, high oil saturation vs. low oil saturation, outcrop sandstone vs. Alvheim core, as well as two different polymer versions.
The polymer recipe as used in the well operation showed to plug standard filters with filter size larger than the reservoir pore sizes but did not plug the cores. The polymer recipe as used in the well gave a better disproportionate permeability reduction (DPR) than the alternative polymer variant with similar viscosity. A theoretical model for the shear rate in the porous media matched the experimental measured data excellent. The core results show a stable permeability reduction factor of 100-450 for water, while only a factor 2-10 and decreasing with time for oil. The achieved DPR ratio of 45-80 is better than the trend from earlier published results.
The DPR as measured in the laboratory was next integrated in the reservoir model as part of the history match of the treated well. The Alvheim field has several reservoir zones separated with thin shales, and this reservoir zonation seems key for this EOR method to work.
The laboratory work, the reservoir studies and the field experience all point to a possible robust and simple EOR method for Alvheim and similar oil fields. The polymer seems to act as a "magic filter", allowing oil to pass while not water. Future work includes more research and maturing a new polymer pilot on Alvheim.
Da Silva Moreira, Paulo Henrique (LRAP / Universidade Federal do Rio de Janeiro) | Gomes da Silveira, Thaís Márcia (LRAP / Universidade Federal do Rio de Janeiro) | Drexler, Santiago (LRAP / Universidade Federal do Rio de Janeiro) | Couto, Paulo (LRAP / Universidade Federal do Rio de Janeiro)
The reliable prediction of reservoir performance requires the cost effective implementation of oil recovery systems, and it is necessary to simulate the fluid flow processes in the reservoir and to measure the rock and fluid properties that determine reservoir behaviour. However, a good prediction relies on accurate values of reservoir physical properties. Carbonates rocks in Brazilian Pre-salt are known for their heterogeneity. Characterizing their physical represents a great challenge and the combination of experimental and computational techniques lead to a more comprehensive understanding of the reservoir behavior.
In the present work, the relative permeability curves of a carbonate core sample with respect to oil and water are calculated by matching the data obtained in a labscale unsteady-state core flood experiment carried out at high pressure high temperature characteristics of Brazilian Pre-salt reservoirs. Corey-type equations were used to model the relative permeability due to its simplicity and having fewer parameters involved. The Monte Carlo Markov chain (MCMC) method was used as optimization tool, taking the fluid production and pressure drop measurements collected during the core flood experiment as input data. An alalysis of the sensitivity cofficients was carried out in order to deal with eventual linear dependences among the terms to be estimated. The Markov chain was generated and its convergence observed. The posterior distributions of the constant terms in the Corey equations were calculated and their mean values applied in order to calculate the relative permeability curves for the oil and water phases. The range of water saturation in which the relative permeability curves describe the core conditions after the breakthrough time, due to the occurrence of capillary end-effects, was calculated. The history match of fluid production and pressure drop was carried out, showing a good fit between the pressure curves. A gap was observed between the production curves due to the fact that the experimental measurements accounted the cumulative volume of oil and water, while the theoretical curve accounted the oil volume only.
Baruah, Nabajit (Oil & Natural Gas Corporation) | Mandal, Dipak (Oil & Natural Gas Corporation) | Jena, Smita Swarupa (Oil & Natural Gas Corporation) | Sahu, Sunil Kumar (Oil & Natural Gas Corporation)
This paper examines the prospect of Gas Assisted Gravity Drainage (GAGD) process in improving recovery from a sandstone reservoir by injecting produced gas back into the crestal part of the reservoir. Besides recovery improvement, immiscible gas injection ensures near Zero Flaring strategy. The process has been found to be ideal in reservoirs with high permeability and reasonable dip to maximize oil production wherever a sufficient gas source exists. Based on the study, gas injection is recommended at the crestal part of the reservoir under study at the rate equivalent to the produced gas to maintain pressure, arrest gas cap shrinkage and improve recovery.
Immiscible water-alternating-gas (iWAG) flooding is often considered as a tertiary recovery technique in waterflooded or about-to-be waterflooded reservoirs to increase oil recovery due to better mobility control and potentially favorable hysteretic changes to phase relative permeabilities. In such cases, typically, reservoir simulation models already exist and have been calibrated, often modifying saturation functions during the history matching stage. However, to utilize such models in forecasting iWAG performance, additional parameters may be required. These can be acquired by simulation of WAG coreflood experiments. While in many published cases, the parameter values obtained from matching experimental results are used without modification, this may not be advisable since the parameters are only valid at the core scale at which they were obtained. This paper discusses the challenge of systematically upscaling WAG parameters obtained at core scale to an existing full field model.
In this work, we use a multi-stage upscaling process from core scale to full field scale. The first stage uses a core scale model to match ‘representative’ core flood experiments and obtain WAG parameters. The second uses a well-to-well high-resolution 1D section of the full field model populated using gridblocks of core size to generate ‘reference’ WAG performance using the unaltered WAG parameters obtained from core. The third stage uses a similar 1D model but populated using gridblocks at full field model resolution to match the results from the reference model while adjusting the WAG parameters as little as possible. Finally, a model using the full field model resolution as well as the full field relative permeability functions which, it is assumed, have been tuned to match the history and account for dispersion is used to match the reference model results and obtain final upscaled WAG parameters.
The upscaled WAG parameters obtained at the end of this multi-stage process can be used at the field scale. This process allows clear quantification of the uncertainty associated with the upscaling process. Simulations at the third stage showed that once the full field to core scale grid size ratio exceeded a certain point (2500:1), there was a marked increase in the difference between upscaled and reference model results. It was found that if WAG parameters were changed in the full field model resolution model in order to match recovery results in the reference model, Land's parameter could change by up to 10% and relative permeability reduction factor could increase by up to 30% although it is expected that this will vary from case to case. It is therefore recommended to identify and use full field model resolutions to as close to the threshold as possible. The practice of using the core scale iWAG parameters in the full field model directly could under-estimate actual recovery, and overestimate injectivity. When considering the WAG mechanism alone, the value of the recovery underestimate increasing with pore volumes injected and, in our case, by up to 7% after injecting 1 pore volume of fluid.
This multi-stage simulation approach helps identify the adjustments required and uncertainties associated with simulating iWAG flooding in reservoir models. This approach utilizes options widely present in commercially available finite difference simulators, addresses the challenge of utilizing existing pseudo functions and provides a practical methodology through which iWAG performance forecasting can be improved.
Two new Non-Intrusive Reduced Order Modelling approaches to estimate time varying, spatial distributions of variables from arbitrary unseen inputs are introduced. One is a generalization of an existing'dynamic' approach which requires multiple surrogate evaluations to model the solutions at different time instances, the other is a'steady-state' approach that evaluates all time instances simultaneously, reducing the local approximation error. The ability of these approaches to estimate the water saturation distributions expected during a gas flood through a 2D, dipping reservoir is investigating for a range of unseen input parameters. The range of these parameters has been chosen so that a range of flow regimes will occur, from a gravity tongue to a viscous dominated Buckley-Leverett displacement. A number of practically relevant model error measures were employed as opposed to the standard L2 (Euclidean) norm. The influence of the number and the structure of training simulations for the model was also investigated, by employing two simple experimental design methods. The results show that POD based NIROM approaches are prone to significant deviations from the true model. The main sources of error are due to the non-smooth variation of system responses in hyperspace and the transient nature of the flows as well as the underlying dimensionality reduction. Since the first two sources are properties of the physical system modelled it may be expected that similar problems are likely to arise independently of the interpolation method and the reduction process used.
Accurate numerical modeling of fluid transport is essential in reservoir management. Higher-order methods help to improve accuracy by reducing the numerical diffusion, which is common for all first order methods. In this paper, we present an implementation of a MUSCL-type second-order finite volume method and demonstrate its capabilities on 2D and 3D unstructured grids. This includes corner point grids that are typically used in reservoir modeling.
A second order finite volume method is compared to standard first order method in terms of accuracy, performance and an ability to handle nonlinearities. There are several ways to build a second order finite volume method. In this paper we choose an optimization-based strategy to compute the steepest possible linear reconstruction. At the same time, a steepness-limiting procedure is included in the optimization as constraint. This ensures that the steepest possible reconstruction that does not lead to oscillations is computed. As a result, sharper fronts compared to standard schemes are obtained.
The paper demonstrates the described method on several benchmark cases with emphasis on relevant for practical reservoir simulation test cases. In particular, we use Norne field open data set, which enables cross validation with other implementations. We test the method on the transport case, where an analytical solution is known, to verify convergence behavior and to isolate the errors. Furthermore, the performance of first- and second-order methods is compared on multiphase flow problems typical for improved oil recovery: solvent and CO2 injection. The second order method shows superior performance in terms of accuracy.
This paper verifies the desirable properties of higher order method for reservoir simulation. Moreover, all the described implementations are available in an open source reservoir simulator Open Porous Media (OPM). As a result, these methods are accessible for reservoir engineers and can be used with industry standard modeling setups.