An Embedded Grid-Free Approach for Near-Wellbore Streamline Simulation

SPE Journal ◽  
2017 ◽  
Vol 23 (02) ◽  
pp. 567-588 ◽  
Author(s):  
Bin Wang ◽  
Yin Feng ◽  
Juan Du ◽  
Yihui Wang ◽  
Sijie Wang ◽  
...  
Keyword(s):  
Velocity Field ◽  
Oil Recovery ◽  
Reactive Transport ◽  
Carbon Dioxide Sequestration ◽  
Computational Time ◽  
Effective Manner ◽  
Element Simulator ◽  
Local Grid ◽  
Virtual Boundary ◽  
Two Stages

Summary Reactive-transport phenomena, such as carbon dioxide sequestration and microbial enhanced oil recovery (EOR), have been of interest in streamline-based simulation (SLS). Tracing streamlines launched from a wellbore is important, especially for time-sensitive transport behaviors. However, discretized gridblocks are usually too large compared with the wellbore radius. Field-scale simulations with local-grid-refinement (LGR) models often consume huge computational time. An embedded grid-free approach to integrate near-wellbore transport behaviors into streamline simulations is developed, comprising two stages of development: tracing streamlines in a wellblock (a gridblock containing wells) and coupling streamlines with neighboring grids. The velocity field in a wellblock is produced using a gridless virtual-boundary-element method (VBEM), where streamlines are numerically traced with the fourth-order Runge-Kutta (RK4) method (Butcher 2008). The local streamline system is then connected with the global streamline system, which is produced with the Pollock (1988) algorithm. Finally, the reactive-transport equation will be solved along these streamlines. The algorithm presented for solving near-wellbore streamlines is verified by both a commercial finite-element simulator and a Pollock-algorithm-based 3D streamline simulator. A series of computational cases of reactive-transport simulation is studied to demonstrate the applicability, accuracy, and efficiency of the proposed method. Velocity field, time-of-flight (TOF), streamline pattern, and concentration distribution produced by different approaches are analyzed. Results show that the presented method can accurately perform near-wellbore streamline simulations in a time-effective manner. The algorithm can be directly applied to one grid containing multiple wells or to off-center wells. Furthermore, assuming streamlines are evenly launched from the gridblock boundary or ignoring transport in the wellblock is not always reasonable, and may lead to a significant error. This study provides a simple and grid-free solution, but is capable of capturing the flow field near the wellbore with significant accuracy and computational efficiency. The method is promising for reservoir SLS with time-sensitive transport, and other simulations requiring an accurate assessment of interactions between wells in one particular gridblock.

A Numerical Investigation of Low Salinity Polymer Flooding Effects from a Geochemical Perspective

2021 ◽  
Author(s):  
Omar Chaabi ◽  
Emad W. Al-Shalabi ◽  
Waleed Alameri
Keyword(s):  
Aqueous Phase ◽  
Oil Recovery ◽  
Reactive Transport ◽  
History Matching ◽  
Solution Chemistry ◽  
Related Effect ◽  
Exchange Reactions ◽  
Two Phase ◽  
Low Salinity ◽  
Geochemical Reactions

Abstract Low salinity polymer (LSP) flooding is getting more attention due to its potential of enhancing both displacement and sweep efficiencies. Modeling LSP flooding is challenging due to the complicated physical processes and the sensitivity of polymers to brine salinity. In this study, a coupled numerical model has been implemented to allow investigating the polymer-brine-rock geochemical interactions associated with LSP flooding along with the flow dynamics. MRST was coupled with the geochemical software IPhreeqc. The effects of polymer were captured by considering Todd-Longstaff mixing model, inaccessible pore volume, permeability reduction, polymer adsorption as well as salinity and shear rate effects on polymer viscosity. Regarding geochemistry, the presence of polymer in the aqueous phase was considered by adding a new solution specie and related chemical reactions to PHREEQC database files. Thus, allowing for modeling the geochemical interactions related to the presence of polymer. Coupling the two simulators was successfully performed, verified, and validated through several case studies. The coupled MRST-IPhreeqc simulator allows for modeling a wide variety of geochemical reactions including aqueous, mineral precipitation/dissolution, and ion exchange reactions. Capturing these reactions allows for real time tracking of the aqueous phase salinity and its effect on polymer rheological properties. The coupled simulator was verified against PHREEQC for a realistic reactive transport scenario. Furthermore, the coupled simulator was validated through history matching a single-phase LSP coreflood from the literature. This paper provides an insight into the geochemical interactions between partially hydrolyzed polyacrylamide (HPAM) and aqueous solution chemistry (salinity and hardness), and their related effect on polymer viscosity. This work is also considered as a base for future two-phase polymer solution and oil interactions, and their related effect on oil recovery.

Improved Calculation of Wellblock Pressures for Numerical Simulation of Non-Newtonian Polymer Injection

SPE Journal ◽  
2021 ◽  
pp. 1-12
Author(s):  
Irfan Tai ◽  
Marie Ann Giddins ◽  
Ann Muggeridge
Keyword(s):  
Numerical Simulation ◽  
Oil Recovery ◽  
Polymer Solutions ◽  
Shear Thinning ◽  
Injection Well ◽  
Solution Viscosity ◽  
Grid Refinement ◽  
Equivalent Radius ◽  
Local Grid Refinement ◽  
Local Grid

Summary The viability of any enhanced-oil-recovery project depends on the ability to inject the displacing fluid at an economic rate. This is typically evaluated using finite-volume numerical simulation. These simulators calculate injectivity using the Peaceman method (Peaceman 1978), which assumes that flow is Newtonian. Most polymer solutions exhibit some degree of non-Newtonian behavior resulting in a changing polymer viscosity with distance from the injection well. For shear-thinning polymer solutions, conventional simulations can overpredict injection-well bottomhole pressure (BHP) by several hundred psi, unless a computationally costly local grid refinement is used in the near-wellboreregion. We show theoretically and numerically that the Peaceman pressure-equivalent radius, based on Darcy flow, is not correct when fluids are shear thinning, and derive an analytical expression for calculating the correct radius. The expression does not depend on any particular functional relationship between polymer-solution viscosity and velocity. We test it using the relationship described by the Meter equation (Meter and Bird 1964) and the Cannella et al. (1988) correlation. Numerical tests indicate that the solution provides a significant improvement in the accuracy of BHP calculations for conventional numerical simulation, reducing or removing the need for expensive local grid refinement around the well when simulating the injection of fluids with shear-thinningnon-Newtonianrheology.

Machine Learning Application to CO2 Foam Rheology

2021 ◽  
Author(s):  
Javad Iskandarov ◽  
George Fanourgakis ◽  
Waleed Alameri ◽  
George Froudakis ◽  
Georgios Karanikolos
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Experimental Studies ◽  
Training Data ◽  
Computational Time ◽  
Gradient Boosting ◽  
Operational Conditions ◽  
Co2 Foam ◽  
Modelling Techniques ◽  
Foam Rheology

Abstract Conventional foam modelling techniques require tuning of too many parameters and long computational time in order to provide accurate predictions. Therefore, there is a need for alternative methodologies for the efficient and reliable prediction of the foams’ performance. Foams are susceptible to various operational conditions and reservoir parameters. This research aims to apply machine learning (ML) algorithms to experimental data in order to correlate important affecting parameters to foam rheology. In this way, optimum operational conditions for CO2 foam enhanced oil recovery (EOR) can be determined. In order to achieve that, five different ML algorithms were applied to experimental rheology data from various experimental studies. It was concluded that the Gradient Boosting (GB) algorithm could successfully fit the training data and give the most accurate predictions for unknown cases.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

scholarly journals Machine learning based co-optimization of carbon dioxide sequestration and oil recovery in CO2-EOR project

2020 ◽  
Vol 260 ◽  
pp. 120866 ◽  
Author(s):  
Junyu You ◽  
William Ampomah ◽  
Qian Sun ◽  
Eusebius Junior Kutsienyo ◽  
Robert Scott Balch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Oil Recovery ◽  
Carbon Dioxide Sequestration ◽  
Co2 Eor

A Mechanistic Model for Wettability Alteration by Chemically Tuned Waterflooding in Carbonate Reservoirs

SPE Journal ◽  
2015 ◽  
Vol 20 (04) ◽  
pp. 767-783 ◽  
Author(s):  
C.. Qiao ◽  
L.. Li ◽  
R.T.. T. Johns ◽  
J.. Xu
Keyword(s):  
Experimental Data ◽  
Oil Recovery ◽  
Carbonate Rocks ◽  
Reactive Transport ◽  
Mechanistic Model ◽  
Contact Angles ◽  
Carbonate Reservoirs ◽  
Wettability Alteration ◽  
Physical Parameters ◽  
Minor Effect

Summary Injection of chemically tuned brines into carbonate reservoirs has been reported to enhance oil recovery by 5–30% original oil in place (OOIP) in coreflooding experiments and field tests. One proposed mechanism for this improved oil recovery (IOR) is wettability alteration of rock from oil-wet or mixed-wet to more-water-wet conditions. Modeling of wettability-alteration experiments, however, is challenging because of the complex interactions among ions in the brine and crude oil on the solid surface. In this research, we developed a multiphase and multicomponent reactive transport model that explicitly takes into account wettability alteration from these geochemical interactions in carbonate reservoirs. Published experimental data suggest that desorption of acidic-oil components from rock surfaces make carbonate rocks more water-wet. One widely accepted mechanism is that sulfate (SO42−) replaces the adsorbed carboxylic group from the rock surface, whereas cations (Ca2+, Mg2+) decrease the oil-surface potential. In the proposed mechanistic model, we used a reaction network that captures the competitive surface reactions among carboxylic groups, cations, and sulfate. These reactions control the wetting fractions and contact angles, which subsequently determine the capillary pressure, relative permeabilities, and residual oil saturations. The developed model was first tuned with experimental data from the Stevns Klint chalk and then used to predict oil recovery for additional untuned experiments under a variety of conditions where IOR increased by as much as 30% OOIP, depending on salinity and oil acidity. The numerical results showed that an increase in sulfate concentration can lead to an IOR of more than 40% OOIP, whereas cations such as Ca2+ have a relatively minor effect on recovery (approximately 5% OOIP). Physical parameters, including the total surface area of the rock and the diffusion coefficients, control the rate of recovery, but not the final oil recovery. The simulation results further demonstrate that the optimum brine formulations for chalk are those with relatively abundant SO42− (0.096 mol/kg water), moderate concentrations of cations, and low salinity (total ionic strength of less than 0.2 mol/kg water). These findings are consistent with the experimental data reported in the literature. The new model provides a powerful tool to predict the IOR potential of chemically tuned waterflooding in carbonate reservoirs under different scenarios. To the best of our knowledge, this is the first model that explicitly and mechanistically couples multiphase flow and multicomponent surface complexation with wettability alteration and oil recovery for carbonate rocks specifically.

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