Mancilla-Polanco, Adel (University of Calgary) | Johnston, Kim (University of Calgary) | Richardson, William D. L. (University of Calgary) | Schoeggl, Florian F. (University of Calgary) | Zhang, Y. George (University of Calgary) | Yarranton, Harvey W. (University of Calgary) | Taylor, Shawn D. (Schlumberger-Doll Research)
The phase behavior of heavy-oil/propane mixtures was mapped from temperatures ranging from 20 to 180°C and pressures up to 10 MPa. Both vapor/liquid (VL1) and liquid/liquid (L1L2) regions were observed. Saturation pressures (VL1 boundary) were measured in a Jefri 100-cm3 pressure/volume/temperature (PVT) -cell and blind-cell apparatus. The propane content at which a light propane-rich phase and a heavy bitumen-rich (or pitch) phase formed (L1/L1L2 boundary) was visually determined with a high-pressure microscope (HPM) while titrating propane into the bitumen. High-pressure and high-temperature yield data were measured using a blind-cell apparatus. Here, yield is defined as the mass of the indicated component(s) in the pitch phase divided by the mass of bitumen in the feed. A procedure was developed and used to measure propane-rich-phase and pitch-phase compositions in a PVT cell.
Pressure/temperature and pressure/composition phase diagrams were constructed from the saturation-pressure and pitch-phase-onset data. High-pressure micrographs demonstrated that, at lower temperatures and propane contents, the pitch phase appeared as glassy particles, whereas at higher propane contents and temperatures, it appeared as a liquid phase. Ternary diagrams were also constructed to present phase-composition data. The ability of a volume-translated Peng-Robinson cubic equation of state (CEOS) (Peng and Robinson 1976) to match the experimental measurements was explored. Two sets of binary-interaction parameters were tested: temperature-dependent binary-interaction parameters (SvdW) and composition-dependent binary-interaction parameters (CDvdW). Models derived from both types of binary-interaction parameters matched the saturation pressures and the L1L2 boundaries at one pressure but could not match the pressure dependency of the L1L2 boundary or the measured L1L2 phase compositions. The SvdW model could not match the yield data, whereas the CDvdW model matched yields at temperatures up to 90°C.
Wang, Kun (University of Calgary) | Liu, Hui (University of Calgary) | Yan, Lin (Exploration and Development Research Institute, PetroChina) | Luo, Jia (University of Calgary) | Wu, Keliu (China University of Petroleum) | Li, Jing (University of Calgary) | Chen, Fuli (Exploration and Development Research Institute, PetroChina) | Dong, Xiaohu (China University of Petroleum) | Chen, Zhangxin (University of Calgary)
In reservoir simulation, an ILU preconditioner is the most widely used preconditioner for preconditioning linear systems due to its simplicity and low computational cost. However, an ILU preconditioner sometimes is not effective enough, especially for a large-scale parallel reservoir simulation problem with a highly heterogeneous geological model. A constrained pressure residual (CPR) preconditioner is considered a more efficient one, which employs two stages of a preconditioning process: the first stage uses the Algebraic Multi-grid (AMG) method to solve a pressure system, and the second stage uses the ILU method to solve the whole system. Its disadvantage is the high computational cost of the AMG method. In order to reduce the computation costs on preconditioners and the resulting linear solvers, we have developed an adaptive preconditioning strategy  to automatically select a preconditioner between an ILU preconditioner and a CPR preconditioner or switch the ILU preconditioner to the CPR preconditioner and vice versa during a linear solution process. In this paper, the adaptive strategy is further analyzed and studied to understand its numerical performance and to choose optimal switch criteria.
Fluid-rock interactions can modify certain reservoir properties, notably porosity, permeability, wettability, and capillary pressure, and they may significantly influence fluid transport, well injectivity, and oil recovery. The profound influence of low-salinity-brine flooding is primarily based on wettability alteration, while that of CO2 flooding is based on oil swelling, viscosity reduction, and interfacial tension reduction. Low saline brine, when combined with CO2, leads to higher CO2 solubility and diffusion, and increased brine acidity. The low-salinity-brine-CO2 injection further contributes to the synergy of mechanisms underlying the two processes to improve oil recovery.
A reactive transport model, which uses surface complexation reactions (SCR) to describe the equilibrium between the rock surface sites and ion species in the brine solution coupled with transport equation, was developed to predict a set of low-salinity-brine-CO2 flooding experiments conducted on carbonate rocks. While conducting batch simulations of the model, it was shown that the thermodynamic parameters reported in the literature for SCRs in a rock–brine system are not suited to natural carbonate rocks. The same thermodynamic parameters could not fit the model to experimental zeta potential data with pulverized and intact carbonate cores at varying potential determining ion concentrations. The model was further utilized to predict the effluent compositions of potential determining ions in single-phase flooding experiments on natural carbonate cores. The failure of thermodynamic parameters in the prediction of reactive transport single-phase experiments, implies that zeta potential is not enough to optimize such parameters for the reactive transport model.
The reactive–transport model parameters were fitted to the single-phase experiments and a temperature-dependent relationship was generated for the thermodynamic parameters. Then, the optimized model was used in investigating the equilibrium between rock, oil and brine in a set of low-salinity-brine-CO2 flooding experiment. The model showed an incremental recovery of 28% over the formation water flooding, similar to the reported recovery from the experiment. The simulation results show that the incremental recovery can be associated with increased CO2 solubility leading to the formation of
A multiscale fracture model for low-permeability brittle rocks which accounts for their microstructure is presented. The work hinges on a microcrack-damage model within a poroelasticity and multiscale framework. A set of damage tensors describes the effect of dual-scale porosities (nanopores and microcracks) on both the hydraulic and poroelasticrock properties. Failure is formulated as a material degradation phenomenon driven by microcrack growth which impacts on hydro-mechanical properties. Essentially, the multiscale model reconstructs the coupling effect of hydro-mechanical forces at the continuum level from the ground up through the upscaling of the phase interactions at the fundamental scales of the material, which is novel in rock mechanics applied to hydraulic fracturing. As an illustration of the enhanced capabilities of the developed model, numerical simulations based on the extended finite element method are presented consideringbench mark problems and lab experimental results of hydraulic fracturing in heterogeneous brittle rocks.
For thermal heavy oil recovery, conventional steam injection processes are generally limited to reservoirs of relatively shallow depth, high permeability, thick pay zone and homogeneity. An alternative approach of applying Electromagnetic (EM) energy may be used to generate heat in reservoirs that are not suitable for steam injection or to improve the economics of the heavy oil recovery compared with steam injection. EM in-situ heating of oil reservoirs, in the form of EM energy absorption by dielectric materials, leads to an increase in temperature, a reduction in oil viscosity and an improvement in oil mobility. Recent studies have shown that EM heating is capable of reducing carbon emissions and water usage. However, the existing EM field simulators are limited to modeling of homogeneous media with respect to dielectric properties, which affects EM wave propagation and in-situ heat generation. For oil sands recovery where reservoir heating by EM energy is promising, it is desirable to simulate reservoirs in inhomogeneous formations, in which dielectric properties vary according to specific location. In this work, important background information regarding the EM wave propagation in inhomogeneous media is provided. A Helmholtz equation for the magnetic field by deformation of Maxwell's equations is presented that makes it feasible to find EM field solutions for such inhomogeneous media. Solution of only the magnetic field makes this work execution faster than the classical methods in which both magnetic and electric fields need to be calculated. By solving the equations of EM wave propagation and fluid flow in oil sands reservoirs simultaneously, this work provides a fully-implicit modelling method for the EM heating process. The feasibility of EM heating in oil sands is examined in two case studies: a) a horizontal well containing an antenna within and b) a horizontal well-pair with an antenna located in the upper well.
We present a novel sampling algorithm for characterization and uncertainty quantification of heterogeneous multiple facies reservoirs. The method implements a Bayesian inversion framework to estimate physically plausible porosity distributions. This inversion process incorporates data matching at the well locations and constrains the model space by adding
The proposed workflow uses an ensemble-based Markov Chain Monte Carlo approach combined with sampling probability distributions that are physically meaningful. Moreover, the method targets geostatistical modeling to specific zones in the reservoir. Accordingly, it improves fulfilling the inherent stationarity assumption in geostatistical simulation techniques. Parameter sampling and geostatistical simulations are calculated through an inversion process. In other words, the models fit the known porosity field at the well locations and are structurally consistent within main reservoir compartments, zones, and layers obtained from the seismic impedance volume. The new sampling algorithm ensures that the automated history matching algorithm maintains diversity among ensemble members avoiding underestimation of the uncertainty in the posterior probability distribution.
We evaluate the efficiency of the sampling methodology on a synthetic model of a waterflooding field. The predictive capability of the assimilated ensemble is assessed by using production data and dynamic measurements. Also, the qualities of the results are examined by comparing the geological realism of the assimilated ensemble with the reference probability distribution of the model parameters and computing the predicted dynamic data mismatch. Our numerical examples show that incorporating the seismically constrained models as prior information results in an efficient model update scheme and favorable history matching.
Wang, Kun (University of Calgary) | Luo, Jia (University of Calgary) | Yan, Lin (Exploration and Development Research Institute, PetroChina) | Wei, Yizheng (Computer Modeling Group Ltd) | Wu, Keliu (China University of Petroleum) | Li, Jing (University of Calgary) | Chen, Fuli (Exploration and Development Research Institute, PetroChina) | Dong, Xiaohu (China University of Petroleum) | Chen, Zhangxin (University of Calgary)
EOS-based phase equilibrium calculations are usually used in compositional simulation to have accurate phase behaviour. Phase equilibrium calculations include two parts: phase stability tests and phase splitting calculations. Since the conventional methods for phase equilibrium calculations need to iteratively solve strongly nonlinear equations, the computational cost spent on the phase equilibrium calculations is huge, especially for the phase stability tests. In this work, we propose artificial neural network (ANN) models to accelerate the phase flash calculations in compositional simulations. For the phase stability tests, an ANN model is built to predict the saturation pressures at given temperature and compositions, and consequently the stability can be obtained by comparing the saturation pressure with the system pressure. The prediction accuracy is more than 99% according to our numerical results. For the phase splitting calculations, another ANN model is trained to provide initial guesses for the conventional methods. With these initial guesses, the nonlinear iterations can converge much faster. The numerical results show that 90% of the computation time spent on the phase flash calculations can be saved with the application of the ANN models.
Liu, Hui (University of Calgary) | Chen, Zhangxin (University of Calgary) | Shen, Lihua (University of Calgary) | Zhong, He (University of Calgary) | Liu, Huaqing (AMSS, Chinese Academy of Sciences) | Yang, Bo (University of Calgary) | Ji, Dongqi (University of Calgary) | Zhu, Zhouyuan (China University of Petroleum) | Zhan, Jie (Xi'an Shiyou University)
This paper deals with the development of our parallel reservoir simulator that is designed for giant reservoir models. It considers oil, water and polymer, and a reservoir can be a conventional reservoir without fractures or a naturally fractured reservoir. For polymer flooding, the simulator can model polymer retention, adsorption, an aqueous phase permeability reduction and viscosity increase, and an inaccessible pore volume. Here fractures are modeled by the dual porosity and dual permeability method. The finite difference (volume) method is applied to discretize the model, upstream techniques are employed to deal with rockfluid properties, and the fully implicit method in time is applied. The linear systems from the Newton method are ill-conditioned and a scalable CPRtype preconditioner is employed to accelerate the solution of these linear systems. The computed results are compared with those from commercial simulators, and they match very well.
Li, Ying (Southwest Petroleum University) | Li, Haitao (Southwest Petroleum University) | Chen, Shengnan (University of Calgary) | Lu, Yu (Southwest Petroleum University) | Li, Xiaoying (University of Calgary) | Luo, Hongwen (Southwest Petroleum University) | Liu, Chang (Southwest Petroleum University) | Cui, Xiaojiang (Southwest Petroleum University)
Capillary pressure and relative permeability are the two main factors determining the multiphase flow in oil and gas reservoirs. Dynamic capillarity, which includes the dynamic capillary pressure and the dynamic relative permeability, should be considered when performing waterflooding in low permeability oil reservoirs. To stimulate the production, hydraulic fracturing has been applied in low permeability oil reservoirs. In this work, dynamic capillarity in fractured low permeability reservoirs were investigated through numerical simulation, which applied the capillary pressure and relative permeability data obtained from steady and dynamic waterflooding experiments. The numerical simulation conducted sensitive analysis using CMG. The results show that if the steady data are used in the prediction, the oil saturation reduces more evenly and more quickly, and the production capability of the reservoir is overestimated. Moreover, the production well will be predicted to breakthrough earlier, with a higher breakthrough water flow if the dynamic capillarity is neglected This work demonstrates the importance of considering dynamic capillarity in fractured low permeability reservoirs, and provides another perspective to predict the production in fractured low permeability reservoirs.
Gas hydrates reservoirs are a type of unconventional reservoir that is an extremely abundant and ubiquitous source of energy. They are also relatively cleaner than most other hydrocarbon sources which makes them an even more attractive source of energy. The potential of this source of energy has, however, not been utilized since very little production has ever taken place from these reservoirs due to their complexity. This research provides an understanding of gas hydrates thermodynamics and reservoir properties in order to assist in properly modelling the hydrate flow in porous media. The research also provides a road map to the current production methods that have been used in pilot tests in order to produce from gas hydrates reservoirs. The production methods explained include depressurization, thermal stimulation, inhibitor injection, combined methods, carbon dioxide injection, and mining. The mechanism of each method is fully explained, and the advantages and disadvantages of each method are also explained. Several case studies worldwide are also discussed to show how each production method has been used to produce from the gas hydrate reservoirs. The results from the case studies are also used to reach conclusions on how each method can be improved upon. To the author’s knowledge, no publication has provided a complete overview on gas hydrates and their production mechanism which makes this research a crucial step in providing an overview on many aspects of gas hydrates reservoirs and their production mechanisms and potential. Understanding the mechanisms to produce from gas hydrate reservoirs is a crucial step in the hydrocarbon industry to allow us to tap into this vast source of energy in the near future.