Reservoir management optimization: case study of an oil rim located in the Gulf of Guinea comparing dynamic simulation of low cost smart wells with conventional wells

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Application of multilevel concepts for uncertainty quantification in reservoir simulation

Uncertainty quantification is an important task in reservoir simulation and is an active area of research. The main idea of uncertainty quantification is to compute the distribution of a quantity of interest, for example oil rate. That uncertainty, then feeds into the decision making process. A statistically valid way of quantifying the uncertainty is a Markov Chain Monte Carlo (MCMC) method, such as Random Walk Metropolis (RWM). MCMC is a robust technique for estimating the distribution of the quantity of interest. RWM is can be prohibitively expensive, due to the need to run a huge number of realizations, 45% - 70% of these may be rejected and, even for a simple reservoir model it may take 15 minutes for each realization. Hamiltonian Monte Carlo accelerates the convergence for RWM but may lead to a large increase computational cost because it requires the gradient. In this thesis, we present how to use the multilevel concept to accelerate convergence for RWM. The thesis discusses how to apply Multilevel Markov Chain Monte Carlo (MLMCMC) to uncertainty quantification. It proposes two new techniques, one for improving the proxy based on multilevel idea called Multilevel proxy (MLproxy) and the second one for accelerating the convergence of Hamiltonian Monte Carlo is called Multilevel Hamiltonian Monte Carlo (MLHMC). The idea behind the multilevel concept is a simple telescoping sum: which represents the expensive solution (e.g., estimating the distribution for oil rate on finest grid) in terms of a cheap solution (e.g., estimating the distribution for oil rate on coarse grid) and `correction terms', which are the difference between the high resolution solution and a low resolution solution. A small fraction of realizations is then run on the finer grids to compute correction terms. This reduces the computational cost and simulation errors significantly. MLMCMC is a combination between RWM and multilevel concept, it greatly reduces the computational cost compared to the RWM for uncertainty quantification. It makes Monte Carlo estimation a feasible technique for uncertainty quantification in reservoir simulation applications. In this thesis, MLMCMC has been implemented on two reservoir models based on real fields in the central Gulf of Mexico and in North Sea. MLproxy is another way for decreasing the computational cost based on constructing an emulator and then improving it by adding the correction term between the proxy and simulated results. MLHMC is a combination of Multilevel Monte Carlo method with a Hamiltonian Monte Carlo algorithm. It accelerates Hamiltonian Monte Carlo (HMC) and is faster than HMC. In the thesis, it has been implemented on a real field called Teal South to assess the uncertainty

Analysis of Injectivity Decline in some Deepwater Water Injectors

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A comprehensive workflow for real time injection-production optimization based on equilibrium displacement

        Irregular well network with high oil recovery rate is used in the development of offshore oilfield, which usually leads to imbalanced waterflooding and poor development performance. In this paper, according to the Buckley-Leverett Equation and general waterflooding theory, a quantitative relationship between water-cut, liquid production and water injection rate is gained to improve the unbalanced lateral waterflooding of the present well network. All the single-well water-cuts are considered to obtain balanced waterflooding of present well network through liquid production and water injection rate adjustments. A new injection-production adjustment method is proposed, with the corresponding calculation program being compiled to realize real-time optimization and adjustment. This method is applied to the 1-1195-1 sand body of Bohai BZ Oilfield. The daily oil increment is 80 m3/d and the cumulative annual oil increment is 2.6×104 m3 , which is consistent with the expected program. It can therefore contribute to engineers’ optimizing the injection-production strategy of reservoirs, as well as facilitating revitalizing mature water foods and, more importantly, facilitating the design and implementation of an appropriate IOR pilots. The presented reliable method could provide certain significance for the efficient development of offshore oilfields.Cited as: Chang, H., Liu, Y., Lei, Y., Zhang, Q. A comprehensive workflow for real time injection-production optimization based on equilibrium displacement. Advances in Geo-Energy Research, 2020, 4(3): 260-270, doi: 10.46690/ager.2020.03.0

Theoretical Investigation of Immiscible Multiphase Flow Mechanisms in Porous Media with Capillarity

The correct description of multiphase flow mechanism in porous media is an important aspect of research in fluid mechanics, water resources and petroleum engineering. The thorough understanding of these mechanisms is important for many applications such as waterflood, CO2 sequestration, and enhanced oil recovery. Being different from single phase flow that is well described by Darcy’s law and well understood for over 160 years, the multiphase flow mechanism requires more mathematical involvement with more complex fluid interaction which inevitably will incorporate relative permeability and capillary pressure into its description. For typical two-phase flow problems, especially at the conventional reservoir scale, the Buckley-Leverett flow equations are normally applied with negligible capillarity to capture the flow behavior. However, as we extend our studies to higher resolution using multiscale calculations, or evaluate tighter or higher contrast heterogeneous reservoirs, capillarity becomes increasingly important. Also, for situations such as spontaneous imbibition that wetting fluid is displaced by non-wetting invading fluid, it is possible that capillary force becomes the dominating driving force with negligible viscous and gravity contributions. To better characterize the multiphase flow mechanism with capillarity, in this research, a detailed investigation is carried out in pursuit of more rigorous mathematical description and broader applicability. The numerical simulation analysis of the described problem has long been a subject of interest with numerous publications addressing it. Being different from the traditional methods where numerical simulation is used, we pursue the analytical description of the flow behavior using Lagrangian approach which is better in describing these frontal propagation problems. Also, the analytical solution tends to give more insight into the underlying physical characteristics of the problem itself. As one of the most important outcomes, the methodology derives a new dimensionless capillary group that characterizes the relative strength of capillarity at the continuum scale based on the analytical solution. Knowledge of this can be used for stability analyses, with future potential application in the design of computational grids to properly resolve the capillary physics

Theoretical Investigation of Immiscible Multiphase Flow Mechanisms in Porous Media with Capillarity

The correct description of multiphase flow mechanism in porous media is an important aspect of research in fluid mechanics, water resources and petroleum engineering. The thorough understanding of these mechanisms is important for many applications such as waterflood, CO2 sequestration, and enhanced oil recovery. Being different from single phase flow that is well described by Darcy’s law and well understood for over 160 years, the multiphase flow mechanism requires more mathematical involvement with more complex fluid interaction which inevitably will incorporate relative permeability and capillary pressure into its description. For typical two-phase flow problems, especially at the conventional reservoir scale, the Buckley-Leverett flow equations are normally applied with negligible capillarity to capture the flow behavior. However, as we extend our studies to higher resolution using multiscale calculations, or evaluate tighter or higher contrast heterogeneous reservoirs, capillarity becomes increasingly important. Also, for situations such as spontaneous imbibition that wetting fluid is displaced by non-wetting invading fluid, it is possible that capillary force becomes the dominating driving force with negligible viscous and gravity contributions. To better characterize the multiphase flow mechanism with capillarity, in this research, a detailed investigation is carried out in pursuit of more rigorous mathematical description and broader applicability. The numerical simulation analysis of the described problem has long been a subject of interest with numerous publications addressing it. Being different from the traditional methods where numerical simulation is used, we pursue the analytical description of the flow behavior using Lagrangian approach which is better in describing these frontal propagation problems. Also, the analytical solution tends to give more insight into the underlying physical characteristics of the problem itself. As one of the most important outcomes, the methodology derives a new dimensionless capillary group that characterizes the relative strength of capillarity at the continuum scale based on the analytical solution. Knowledge of this can be used for stability analyses, with future potential application in the design of computational grids to properly resolve the capillary physics

Optimal operating strategy for wells with downhole water sink completions to control water production and improve performance

Downhole water sink (DWS) technology is an alternative to conventional limited-entry completions to control water production in wells with bottom water drive. DWS wells comprise two completions: the bottom completion produces water and keeps the top completion open to oil inflow. The system performance depends on careful manipulation of the top and bottom rates to maximize oil productivity and produce oil-free water from the bottom completion. Conventional nodal analysis cannot provide a solution for DWS wells because the critical rates for water coning change with water drainage rate. A reservoir simulator is used to model two-phase flow to the dual completions. Suites of related simulations are created and managed using algorithms to generate inflow performance relationships and build accompanying tubing performance models. A nodal analysis approach for dual completed wells is proposed. The approach identifies the operational range of top and bottom rates with water coning at the top completion and oil-free water production at the bottom completion subject to a range of practical operational constraints such as maximum drawdown. Because the operational range changes in time, optimization methods must evaluate the dynamic performance and maximize the well\u27s discounted revenue by appropriately scheduling the best top and bottom production rates. New successive nodal analysis and stepwise optimization methods evaluate the best performance for a given moment and time increment. This localized strategy is compared with two algorithms that optimize the entire production schedule globally rather than sequentially - a conjugate gradient method (CGM) and a hybrid CGM-polytope method. Operating strategy can be optimized to maximize oil production early in wells\u27 life using water drainage. Hybrid optimization (global search) finds the best solutions, but demands considerable computation. Stepwise (localized) optimization technique perform nearly as well for rate scheduling, final recovery, well life, and cumulative water production, and these methods are significantly more efficient computationally compared to the hybrid method. All the optimization methods analyzed in this study (static, stepwise, and global strategies) suggest that better well productivity can be achieved by maintaining low water saturation around the producing completion with DWS completions

Realtime reservoir characterization and beyond: cyber-infrastructure tools and technologies

The advent of the digital oil _x000C_eld and rapidly decreasing cost of computing creates opportunities as well as challenges in simulation based reservoir studies, in particular, real-time reservoir characterization and optimization. One challenge our e_x000B_orts are directed toward is the use of real-time production data to perform live reservoir characterization using high throughput, high performance computing environments. To that end we developed the required tools of parallel reservoir simulator, parallel ensemble Kalman _x000C_lter and a scalable work ow manager. When using this collection of tools, a reservoir modeler is able to perform large scale reservoir management studies in short periods of time. This includes studies with thousands of models that are individually complex and large, involving millions of degrees of freedom. Using parallel processing, we are able to solve these models much faster than we otherwise would on a single, serial machine. This motivated the development of a fast parallel reservoir simulator. Furthermore, distributing those simulations across resources leads to a smaller total time to completion by making use of distributed processing. This allows the development of a scalable high throughput work ow manager. Finally, with thousands of models, each with millions of degrees of freedom, we end up with a super uity of model parameters. This translates directly to billions of degrees of freedom in the reservoir study. To be able to use the ensemble Kalman _x000C_lter on these models, we needed to develop a parallel implementation of the ensemble Kalman _x000C_lter. This thesis discusses the enabling tools and technologies developed to address a speci _x000C_c problem: how to accurately characterize reservoirs, using large numbers of complex detailed models. For these characterization studies to be helpful in making production decisions, the time to solution must be feasible. To that end, our work is focused on developing and extending these tools, and optimizing their performance

A column based variance analysis approach to static reservoir model upgridding

The development of coarsened reservoir simulation models from high resolution geologic models is a critical step in a simulation study. The optimal coarsening sequence becomes particularly challenging in a fluvial channel environment where the channel sinuosity and orientation can result in pay/non-pay juxtaposition in many regions of the geologic model. The optimal coarsening sequence is also challenging in tight gas sandstones where sharp changes between sandstone and shale beds are predominant and maintaining the pay/non-pay distinction is difficult. Under such conditions, a uniform coarsening will result in mixing of pay and non-pay zones and will likely result in geologically unrealistic simulation models which create erroneous performance predictions. In particular, the upgridding algorithm must keep pay and non-pay zones distinct through a non-uniform coarsening of the geologic model. We present a coarsening algorithm to determine an optimal reservoir simulation grid by grouping fine scale geologic model cells into effective simulation cells. Our algorithm groups the layers in such a way that the heterogeneity measure of an appropriately defined static property is minimized within the layers and maximized between the layers. The optimal number of layers is then selected based on an analysis resulting in a minimum loss of heterogeneity. We demonstrate the validity of the optimal gridding by applying our method to a history matched waterflood in a structurally complex and faulted offshore turbiditic oil reservoir. The field is located in a prolific hydrocarbon basin offshore South America. More than 10 years of production data from up to 8 producing wells are available for history matching. We demonstrate that any coarsening beyond the degree indicated by our analysis overly homogenizes the properties on the simulation grid and alters the reservoir response. An application to a tight gas sandstone developed by Schlumberger DCS is also used in our verification of our algorithm. The specific details of the tight gas reservoir are confidential to Schlumberger's client. Through the use of a reservoir section we demonstrate the effectiveness of our algorithm by visually comparing the reservoir properties to a Schlumberger fine scale model