Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Wu, Xingru
Role of Temperature on the Performance of In-Situ CO2 EOR
Hussain, Sadam (United Energy Pakistan Limited, Karachi, Pakistan) | Ahmed, Zaheer (United Energy Pakistan Limited, Karachi, Pakistan) | Zakir, Mufaddal Murtaza (United Energy Pakistan Limited, Karachi, Pakistan) | Iqbal, Muneez (United Energy Pakistan Limited, Karachi, Pakistan) | Wu, Justin J. (Purdue University, West Lafayette, Indiana, USA) | Wu, Xingru (University of Oklahoma, Norman, Oklahoma, USA)
Abstract As the temperature dramatically impacts many chemical reactions, the reservoir temperature is an essential parameter in selecting and designing chemical enhanced oil recovery (cEOR) methods. For the in-situ CO2-enhanced oil recovery (ICE), reservoir temperature directly impacts the hydrolysis rate of a CO2-generating chemical agent. Below a critical temperature, the CO2 releasing rate is too low to be effective for ICE. Furthermore, temperature affects the CO2 solubility in oil and water phases and the CO2 partition coefficient between them. When the reservoir temperature is high enough, optimizing the oil recovery and injection slug size of the CO2-generating agent is a problem to be studied in this paper. In this study, urea was injected as a CO2-generating agent. Three light, medium, and heavy hydrocarbon components were used as oils in a synthetic homogeneous 3D quarter 5-spot sector model. The injection temperature was 80 °F (300K). In the reservoir, urea hydrolysis generates CO2, which partitions into oil and water. The urea reaction kinetics used in the study are based on 1D history-matched laboratory data from previous studies. The Arrhenius model was used to calculate the urea hydrolysis reaction rate. Additionally, the Gibbs free energy of the urea hydrolysis reaction was computed to determine the critical reservoir temperature above which the hydrolysis would be favorable and spontaneous. A sensitivity study was conducted to study the temperature effect on ICE performance with an objective function of maximum recovery with constraints of a limited urea mass. Based on both Arrhenius and Gibbs models, it was observed that the urea hydrolysis reaction was prolonged, became negative, and was non-spontaneous at temperatures below 70°C (~340K). It was concluded from the kinetic analysis that the urea hydrolysis reaction would not produce any CO2, and synergetic mechanisms of oil swelling, viscosity reduction, and wettability alteration would not happen if the reservoir temperature is below 70°C. Also, increasing the reaction rate close to this critical temperature would require a catalyst, such as NaOH (Wang 2018 and Wang et al. 2019). The 3D sector model also showed that optimum oil recovery at ten wt% urea concentration would be at ~260 °F (400K); minimal impact on oil recovery was observed above this temperature. Also, an additional 4-5% recovery was obtained post-cold waterflooding. On top of that, almost 90% CO2 generated CO2 dissolved in oil, resulting in oil swelling, viscosity, and IFT reduction. Hence, to apply urea as a CO2-generating agent, one of the critical design parameters is that the reservoir temperature must be higher than 70 °C (343 K). As a promising and innovative EOR technique, ICE will be first studied for performance optimization. The study results can be used in reservoir screening and economic evaluation for the ICE actual field applications.
- North America > United States > Oklahoma (0.29)
- Asia > Middle East > UAE (0.29)
Study of Controlling Parameters of In-Situ CO2 EOR Using Numerical Simulations
Wu, Xingru (The University of Oklahoma) | Dai, Lei (Southwest Petroleum University) | Chang, Qiuhao (The University of Oklahoma) | Qiuhao, Sadam (United Energy Pakistan Limited) | Shiau, Bor Jier (The University of Oklahoma)
Abstract Laboratory experiments have demonstrated that injecting urea solution as a CO2-generating agent into an oil reservoir may significantly enhance oil recovery. When the reservoir temperature is above 50°C, urea is hydrolyzed to carbon dioxide and ammonia. This technology overcomes many supercritical CO2 problems and can be very attractive for thousands of stripper wells that produce oil on marginal economic feasibility. However, previous efforts mainly focus on laboratory tests and mechanisms study. The actual field performance of this technology is likely dependent on reservoir heterogeneity, and its economic viability is expected to be closely related to its optimization. This highly relies on numerical modeling and simulation capability. The synergic mechanisms in in-situ CO2 EOR (ICE) using urea are complex. Firstly, the decomposition of urea injected leads to CO2 and ammonia under proper reservoir conditions. The generated CO2 in brine partitions preferably into the oil phase and decreases oil viscosity while swelling the oil effectively. The co-generated product, ammonia, can potentially reduce the interfacial tension (IFT) between the oil/water phase, which moves the relative permeability (or saturation) curves and position to offer additional oil production. In the first attempt, the dominant parameters, including urea reaction kinetics, the stoichiometry of the decomposition process, the oil swelling effect, and the impact of IFT reduction on the relative permeabilities, were considered and incorporated into the numerical modeling effort. We used the chosen numerical simulations to determine the contribution of the individual mechanism by history matching the results of laboratory tests collected previously. The one-D mechanistic numerical model was then upscaled to a synthetic homogeneous 3D model by simulating a quarter of the 5-spot sector model to evaluate the feasibility and engineering design of ICE for future field scale pilot tests and potential prize of ICE EOR. After comparing the base case with urea injection, a sensitivity analysis was performed. As part of the aims, the simulation results differentiate and reveal the incremental contributions of the synergetic behaviors among several mechanisms: oil viscosity reduction, oil swelling, and IFT reduction. Data also showed that the IFT reduction plays a rather minor role in this effort, and its contribution is basically indistinguishable. The predominant recovery mechanisms are mainly controlled by oil swelling and viscosity reduction; temperature plays a key role in influencing the extent of reaction kinetics of urea. In the 1D simulation, the temperature significantly impacted the production performance as the core cooled down quickly. In a 3D or field-scale scenario, the waterflooding does not change the in-depth reservoir temperature as the temperature gradient moves at a much slower rate (about two times slower) than the injected urea solution slug. However, the duration of water flooding should be considered for field project design as it may alter the temperature profile in the reservoir.
- North America > United States (0.47)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- North America > Canada > Saskatchewan > Williston Basin > Bakken Shale Formation (0.99)
- North America > Canada > Manitoba > Williston Basin > Bakken Shale Formation (0.99)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
Abstract Produced water is commonly co-produced with oil and gas production and requires safe disposal in subsurface reservoirs. Knowing the amount of produced water that can be safely injected into the reservoir is important for disposal operations. While the methods of reservoir hydrocarbon storage are abundant in literature, the granularity of handling water injection capacity is rare, probably due to the misconception that production is similar to injection into a formation. The knowledge of hydrocarbon reservoir petrophysics shines some light on the problem. However, it is far from sufficient to make an economically viable decision as injecting water into the reservoir dramatically differs from producing it. Many practitioners take for granted that knowing the pore volume of the target formation would be sufficient to determine the storage volume. The actual water injection capacity is the product of the pore volume of the formation, total compressibility of the system, and maximum allowable pressure difference. Since the stress-strain relationship of a porous medium depends on the frequency and magnitude of the loading and unloading process, the total compressibility would be different. The maximum allowable injection pressure and reservoir pressure are functions of in-situ stresses, injection temperature and pressure, and reservoir geomechanical parameters, all of which have significant uncertainty. In practical design, we must consider all parameters and their respective uncertainties. This paper presents a procedure for determining the injection capacity through an in-depth discussion of involved parameters and their associated uncertainties from estimation and measurements. This paper will also demonstrate our practice using a field example as our case study to show our suggested approach.
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Texas > Anadarko Basin (0.99)
- North America > United States > Oklahoma > Anadarko Basin (0.99)
- North America > United States > Kansas > Anadarko Basin (0.99)
- (25 more...)
Abstract Generating production-type curves for new horizontal wells in unconventional reservoirs is an evolving process that requires continuous calibration to maintain the most accurate forecast over time. History matching production alone is no longer sufficient to maintain such models. Obstacles to creating production type curves are attributed to the complexities in heterogeneous reservoir properties, improved drilling and completion techniques, and evolving production and operation procedures. This paper will highlight improvements to a proposed machine-learning algorithm to generate production type curves for new wells in oil and gas unconventional reservoirs. The algorithm utilizes dimensionless groups created from the raw data in different categories and scales, thus reducing the dimension of the problem, decreasing the processing time, and improving the efficiency of the machine-learning model. The dimensionless groups are developed using inspectional and dimensional analysis depending on the data available for feature inputs. Many of the dimensionless groups have physical meanings and can be upscaled. We advanced the ability of the previously developed algorithm utilizing production, completion, and petrophysical data from both oil and gas reservoirs to generate new type curves by using the "engineering" code that was laid out in our previous case study. The algorithm incorporates physics into the machine learning (ML) process supporting the outputs with math and science. When using multiple reservoirs from different formations in the data, the algorithm utilizes logic in the code to determine between oil and gas wells. The quality of the results is impacted when using data from reservoirs with phase envelopes that are not similar, for example, a heavy oil and a dry gas reservoir. The algorithm is updated to include logic that can determine the major phase to predict oil and gas production more accurately. The quantity of oil and gas production is more accurately predicted using cumulative production rates rather than over time. The machine learning model maintains an R >= 0.8 when cross-validating both cumulative oil and gas production. The algorithm consistently predicts cumulative production over time on test data with R >=0.8. The predicted rates for new type curves are compared to conventional production type curves, thus validating the quality and goodness of fit for production rates, decline profile, and ultimate recovery. The results demonstrate how late-time production can be either extrapolated using the machine learning algorithm or combining traditional methods by utilizing hyperbolic and exponential declines where training data is unavailable for the machine learning model to perform late-time forecasting. The algorithm of the ML model is proving to be a supplementary tool when generating new production type curves. The speed and efficiency provide support to the DCA generated type curves. It is versatile in its ability to combine data from multiple formations and discern between the major phase, thus providing production type curves we have confidence. The scalability of the dimensionless input parameters can account for changes in completions and reservoir properties within minutes of updating the database hence providing insight in near real-time for engineers.
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.89)
- North America > United States > New Mexico > Permian Basin > Wolfcamp Formation (0.89)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.89)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.89)
Casing Deformation and Controlling Methods During Hydraulic Fracturing Shale Gas Reservoirs in China: What We Known and Tried
Han, Lihong (CNPC Tubular Goods Research Institute, State Key Laboratory for Performance and Structural Safety of Petroleum Tubular Goods and Equipment Materials, Chang'an University) | Yang, Shangyu (CNPC Tubular Goods Research Institute, State Key Laboratory for Performance and Structural Safety of Petroleum Tubular Goods and Equipment Materials) | Dai, Lei (Southwest University of Petroleum) | Cao, Jing (CNPC Tubular Goods Research Institute, State Key Laboratory for Performance and Structural Safety of Petroleum Tubular Goods and Equipment Materials) | Mou, Yisheng (CNPC Tubular Goods Research Institute, State Key Laboratory for Performance and Structural Safety of Petroleum Tubular Goods and Equipment Materials) | Wu, Xingru (University of Oklahoma)
Abstract When operators develop shale gas reservoirs in the southern Sichuan basin in China, they encountered numerous occurrences of casing deformations (CD) and even failures. The high frequency and severity of CD have led to significant financial loss. Since then, a considerable amount of research has been conducted with some field trials. Some research findings have been implemented in fields. The purpose of this paper is to present what we know and the trial results. We observed that casing deformation/failure were mainly in shearing failure and collapse modes. In the early stage of the development, most of the failure was due to shearing deformation caused by pre-existing geological features such as faults and weak interfaces. With the depletion of the reservoir and pressure decrease, casing collapses during the hydraulic fracture with extended length have become more and more popular in the later development stage. Laboratory tests on casing material and cementing material have shad lights on possible solutions. Increasing the casing wall thickness and cement thickness seems a viable solution for casing collapse, but the application of these recommendations yielded little effectiveness in mitigating casing deformation. Current operators redesigned a cementing material with high-strength beads which would collapse when stresses are above the designed threshold, which would "absorb" the formation displacement and reduce the severity of casing deformation caused by the aforementioned mechanisms. This paper summarizes the main research results from implementing numerical modeling and simulation. Based on that, we designed a special cementing with hollow high-strength particles in the cement slurry. In the later stage of fracturing, when the stress is above a threshold, the particles would burst and allow the casing to nudge slightly so that the deformation severity would be much less and more graduate. We implemented the new technology on 14 wells, and so far eight wells have been successfully completed without losses of horizontal segments. This new technology certainly brings hope for future study and provides field cases for future simulation work and laboratory studies for improvement.
- Overview > Innovation (0.54)
- Research Report > New Finding (0.34)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.92)
- Asia > China > Sichuan > Sichuan Basin > Weirong Field (0.99)
- Asia > China > Sichuan > Sichuan Basin > Fuling Field (0.99)
Summary The microscopic displacement efficiency of supercritical carbon dioxide (CO2)-based enhanced oil recovery (EOR) depends critically on the phase behavior of CO2 and residual oil. Traditionally, we assume that the main drive mechanisms of supercritical CO2 EOR are attributed to oil swelling and reduced oil viscosity, and research focuses on how the supercritical CO2 interacts with remaining oil under the reservoir conditions. However, our recent study finds that once the CO2 is introduced into the reservoir, CO2 partitions into the aqueous and oil phases, reducing the interfacial tension (IFT) between the oil and water. This is particularly important when CO2 is generated through a series of chemical reactions for in-situ CO2 EOR. In this paper, we used molecular dynamics (MD) simulations to study the interfacial properties between water and oil with different mole fractions of CO2 in pressures below the minimum miscibility pressure. Simulation results show that with the increase in CO2 mole fraction, rather than evenly distributed in phases, CO2 molecules are prone to concentrate in the water/oil interface region, which decreases IFT between the aqueous phase and oil. Furthermore, the effect of CO2 orientation on the water/oil IFT reduction was observed. The change of CO2 concentration affects CO2 orientation near the interface, which in return dominates the IFT change.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Forecasting production from unconventional reservoirs is a slippery slope that could lead to unrealistic results even though a model has been history matched with production history and vetted by experienced engineers. The reasons for failing conventional decline curves lie in convoluting and heterogeneous reservoir properties, advancing drilling and completion techniques, and dynamic production and operations management. However, the initial rates, declining rate, and ultimate recovery of a well can be viewed as relatively static and predetermined properties of a declining profile. This paper will propose a machine learning-based framework to determine these properties for unconventional reservoir development. In the proposed algorithm, instead of directly data mining on the raw data from different categories and scales, we propose to convert these data into dimensionless variable groups to reduce the dimension of the problem. The dimensionless variables are developed using inspection analysis; most have physical meanings and are easy to upscale. In the case study, we used the production, completion, and petrophysical data to generate new type curves and developed a step-by-step process to explain the aspect of "engineering" code that incorporates physics into the machine learning (ML) process. Dimensionless variables are used in the machine learning process giving physical meaning and reducing the number of predictors, thus improving the speed and efficiency of the code. The results show that the quantity of cumulative oil production over time can be determined using machine learning models with R >= 0.90 for individual wells and R>=0.80 for cross-validated cumulative production forecasts. We can use these determined values to assess the quality of initial rates, declining rates, and ultimate recovery to derive new type curves that incorporate physics and engineering practices. The work emphasizes the importance of accounting for completion parameters, fluid properties and rock quality, thus improving the confidence in results obtained through traditional engineering methods. The machine learning model results provide credibility and support to rates and recoveries for DCA forecasted wells. When modeling hundreds if not thousands of wells, this work shows the importance of utilizing machine learning to harness the power of the data that has been collected on them. The machine-learning-based declining profile is a promising technique and has some advantages over the classical methods based on averaging historical data. First, the determining parameters are highly scalable for newly drilled wells as the main input parameters are dimensionless variables derived from reservoir properties and well completions. Secondly, this algorithm explores not only the production data but also reservoir properties and completion data to capitalize on the advancing techniques.
- North America > United States > Texas (0.50)
- North America > United States > New Mexico (0.50)
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
Summary The microscopic displacement efficiency of supercritical carbon dioxide (CO2)-based enhanced oil recovery (EOR) depends critically on the phase behavior of CO2 and residual oil. Traditionally, we assume that the main drive mechanisms of supercritical CO2 EOR are attributed to oil swelling and reduced oil viscosity, and research focuses on how the supercritical CO2 interacts with remaining oil under the reservoir conditions. However, our recent study finds that once the CO2 is introduced into the reservoir, CO2 partitions into the aqueous and oil phases, reducing the interfacial tension (IFT) between the oil and water. This is particularly important when CO2 is generated through a series of chemical reactions for in-situ CO2 EOR. In this paper, we used molecular dynamics (MD) simulations to study the interfacial properties between water and oil with different mole fractions of CO2 in pressures below the minimum miscibility pressure. Simulation results show that with the increase in CO2 mole fraction, rather than evenly distributed in phases, CO2 molecules are prone to concentrate in the water/oil interface region, which decreases IFT between the aqueous phase and oil. Furthermore, the effect of CO2 orientation on the water/oil IFT reduction was observed. The change of CO2 concentration affects CO2 orientation near the interface, which in return dominates the IFT change.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract In this study, molecular dynamics simulations have been performed to study the interfacial properties between water and oil with different mole fractions of CO2 under 8 MPa and 345 K. Simulation results show that with the increase of CO2 mole fraction, more CO2 got adsorbed in the water-oil interface region. Such CO2 increase weakened water and oil interactions at the interface, resulting in a decrease of the interfacial tension (IFT). Moreover, the water-oil IFT decreased significantly from 0 to 0.40 CO2 mole fractions. But the change was small for higher CO2 mole fractions of 0.40 to 0.80. From those calculations, we conclude that in the CO2-EOR, the volume of injected CO2 needs to be at least more than 0.4 mole fraction (to the oil) to achieve a decent reduction of the water-oil IFT. This study can provide a molecular level reference for implementing the CO2-EOR in the oil field under a low-pressure condition.
Abstract The success of supercritical CO2 Enhanced Oil Recovery (EOR) cannot be duplicated if the cost of CO2 transposition and processing becomes prohibitive. Research results of the in-situ CO2 EOR (ICE) approach offered a potential technology for many waterflooded stripper wells that lack access to affordable CO2 sources. Previously the ICE synergetic mechanisms were only qualitatively attributed to oil swelling and viscosity reduction due to the preferential partition of CO2 into the oleic phase. This study aims to quantify the contributions to recovery factors from several plausible mechanisms with numerical modeling and simulation. First, the urea reaction was modeled as the CO2 generating chemical decomposing to CO2 and ammonia in the reservoir conditions. The CO2 partitions into oil, which leads to the reaction continuation to generate more CO2. The resulting ammonia largely left in water may further react with certain crudes to generate surfactants, thus, decrease the oil/water interfacial tension (IFT). It is expected that the oil containing CO2 also has a lower IFT with water. The reaction kinetics under different temperatures were incorporated into the numerical model. A numerical model featuring the synergetic mechanisms was built including stoichiometry and kinetics of urea reaction, oil swelling effect, oil viscosity reduction, and IFT reduction effect on the relative permeabilities. The laboratory experiments, pore volume injection versus oil saturation were history matched for three different oils including Dodecane, Earlsboro oil, and DeepStar oil. The phase behavior was modeled with the equation of state (EOS) under different mole fractions of CO2. The reaction kinetics were also modified to history match the laboratory experiment. The estimated reduction of oil viscosity was calculated, 76% for Earlsboro oil, 91% in DeepStar oil, and 75% in dodecane oil. The oil swelling factors ranged from 1.60% to 19% in the three lab models, which translates to the recovery factor of oil. The endpoints of relative permeability were modified to account for the recovery contribution to the IFT and viscosity reduction. The impact of reaction kinetics on oil swelling and recovery factor was also determined, and they are not numerically close to reaction kinetics used in the lab cases. The matched reaction kinetics, activation energy and reaction frequency factor for the dodecane laboratory experiment were 91.80 kJ/gmol and 6.5E+09 min. The study concluded that the incremental recovery due to oil swelling ranges between 3.16% and 18.30%, and then from 12.91% to 41.59% is due to IFT reduction for all the cases. The relative permeability and urea reaction kinetics remained the most uncertain parameters during history matching and modeling the ICE synergetic mechanisms.