PRISM :: Browsing by Author "Chen, Shengnan (Nancy)"

Browsing by Author "Chen, Shengnan (Nancy)"

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Open Access
A Holistic Data-Driven Framework for Forecasting and Characterization of Tight Reservoirs
(2023-12-25) Alimohammadi, Hamzeh; Chen, Shengnan (Nancy); Gates, Ian; Shor, Roman; Hassanzadeh, Hassan; Leung, Juliana
In recent years, the need for robust computational techniques in the oil and gas industry, particularly for unconventional reservoirs, has become increasingly evident. This thesis offers a comprehensive exploration into the application of statistical and machine learning (ML), especially deep learning (DL) techniques, with a focus on production forecasting and reservoir classification. The research journey begins by tackling the crucial issue of outlier detection in time-series of production transient data. Analyzing 17 different outlier detection techniques, the study reveals that machine learning-based methods, specifically the k-nearest neighbor and Fulford-Blasingame, outperform other techniques. This not only establishes a reliable data cleaning workflow but also questions the efficacy of traditional statistical methods in dealing with complex, time-dependent datasets. Building upon a clean dataset, the focus then shifts to the limitations of traditional Decline Curve Analysis (DCA) in unconventional tight and shale reservoirs. To address this, the thesis introduces probability density function (PDF)-based models as an alternative to conventional DCA models. These PDF-based models, particularly the Dagum and Beta prime models, have been found to offer superior performance in long-term production forecasting, thus providing a robust toolset for long-term production forecasting and informed decision-making in the industry. In the quest for even more accurate and adaptable solutions, the research leverages deep learning for forecasting long-term well performance. By systematically evaluating a range of recurrent neural networks, the study successfully introduces a versatile model. The proposed Recurrent Neural Networks model for production time series forecasting uses the strengths of RNNs in understanding sequences and trends over time. By using the RNN to understand how these features evolve, the model becomes adept at recognizing complex temporal patterns in time series data, providing a comprehensive framework for accurate forecasting. Finally, the thesis concludes with the introduction of a high-accuracy hybrid deep learning model for reservoir classification. This model, which effectively combines CNNs and Long Short-term Memory (LSTM), has shown an impressive 98% accuracy rate on validation data. It offers a new approach for the automated and accurate classification of unconventional reservoirs, thus setting the stage for its potential as a new tool for reservoir engineers and geoscientist. Throughout this research, several key contributions have been made from establishing a systematic approach for reliable outlier detection to developing high-performance PDF-based and deep learning models for production forecasting and reservoir classification. However, the study also opens avenues for future work, such as the development of labeled real-world datasets, the exploration of PDF-based models across other types of reservoirs, and the incorporation of additional variables and features in deep learning models for even more nuanced analyses. In summary, this thesis serves as a pivotal contribution to both academic research and industrial practice. It not only challenges traditional methodologies but also introduces innovative computational techniques that promise to set a new standard in the field of oil and gas production forecasting and reservoir classification of tight and shale reservoirs.
Embargo
Application of Nanoparticles in Regular and Foamed Cement-Based Systems
(2024-01-19) Mehairi, Ahmed; Husein, Maen; Khoshnazar, Rahil; Aguilera, Roberto; Chen, Shengnan (Nancy); Hassanzadeh, Hassan; Torabi, Farshid
Nano-modification of cement-based materials (CBMs) has the potential to enhance the mechanical properties of conventional CBMs and provide sustainable and energy-efficient solutions to mitigate the environmental footprint of cement manufacturing. Over the past years, the addition of nanoparticles (NPs) into cement paste, mortar, and concrete has shown outstanding enhancements in their mechanical properties and durability. Large-scale application of NPs in CBMs still, however, faces challenges such as improper dispersion, poor economics due to the cost of NPs, and potential health concerns associated with NPs handling. This work attempts to tackle these barriers by proposing inexpensive methods of NPs incorporation into oil well cement slurry and foamed concrete (FC). For oil well cement slurry, an easily scalable approach of NPs synthesis during cement slurry mixing is developed. Three methods for preparing in situ Fe(OH)3 NPs are presented. At 0.7 wt% of dry oil well cement, in situ prepared Fe(OH)3 NPs increases the 1-day compressive strength of the cement slurry by up to 90% and 38% at 25 oC and 80 oC, respectively, outperforming commercial NPs. Significant reductions in porosity (up to 48%) and permeability (up to 93%) are also achieved. Moreover, cement slurries with Fe(OH)3 NPs exhibit high resistance to fatigue from repeated compression cycles. In addition to oil well cement slurry, incorporation of NPs into FC through NP-stabilized preformed foams has been shown to overcome major FC drawbacks such as slurry instability and poor durability. In this study, the formulation of a stable in-house CaCO3 NPs/ hexadecyltrimethylammonium bromide (CTAB) dispersion is achieved. In the presence of pure N2 and a 2:1 CO2/N2 gas mixture, foams produced from this dispersion have half-lives of 5 – 6 h compared to 5 – 7 mins for CTAB alone. The presence of CaCO3 NPs also reduces the average bubble size by 67% and enhances foam thermal stability. The utilization of CaCO3 NPs/CTAB aqueous foam in FC improves slurry stability and leads to a narrower and more uniform pore size distribution than FC made with CTAB alone. CaCO3 NPs also accelerate the formation of hydration products and promote the formation of a denser solid matrix. These combined effects contribute to a less connected pore structure, reduction in atmospheric carbonation, and improved heat transfer and fire resistance properties.
Open Access
Application of PC-SAFT Equation of State to Bitumen/Solvent Systems
(2015-06-26) Ma, Mingxu; Chen, Shengnan (Nancy); Abedi, Jalal
Solvent-assisted process is promising to recovery Canada's bitumen recourses, but the prediction techniques for the solubility, density and viscosity of bitumen and solvent mixtures in reservoir simulation are not well developed. This thesis works to develop the application methodology of the simplified Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) to bitumen and solvent mixtures. New binary interaction coefficient correlation and bitumen characterization method are developed to model the solubility, density and viscosity of such mixtures. These results are also integrated into the reservoir simulation of solvent-assisted recovery. Using the developed method, the average absolute relative deviations (AARDs) are 6.6% for the solubility, 2.3% for the density and 14.6% for the viscosity. In addition, the reservoir simulation results indicate good agreement with the sandpack production data using solvents. This thesis has developed the methodology to model the phase behavior and recovery process of bitumen and solvent using PC-SAFT.
Open Access
Catalytic In-reservoir Upgrading: Effect of Porous Media and Deposition of Nanocatalyst
(2017) Rodriguez, Victor Manuel; Pereira-Alamo, Pedro; Chen, Shengnan (Nancy); Nassar, Nashaat; Lines, Laurence R.; Semagina, Natalia V.
Technologies merging enhanced oil recovery with In-situ upgrading can significantly increase the economic and environmentally efficiency of unconventional oil exploitation. The development of Nanocatalytic In-situ Upgrading via Dense Hot Fluid Injection is a promising approach that takes advantage of upgrading lowest value bitumen fractions while promoting additional recovery of oil in place. This research addresses some important aspects related to the development of this novel technology, focusing on the until now unattended relevance of thermal kinetics (absence of catalyst particles), the precise range of particles sizes produced using the nano-catalyst manufacturing unit prototype built by the research group and the interaction rock-nano-particles relevant for the targeted catalyst deposition. The main limitation facing in-situ thermal upgrading (in porous media) of Athabasca bitumen was found to be product stability. Vacuum residue conversions above 32% result in unstable products, and although high viscosity reductions and moderate improvement of other properties are obtained they are not sufficient to produce transportable products. Additionally coke precursors are significantly retained by the porous media that further limiting the process. The kinetic modeling of thermal upgrading in porous media demonstrated the catalytic behavior of the sand pack reflected in an increase of the apparent reaction order for the vacuum residue to a second-order-of reaction. The use of Nanoparticle Tracking Analysis (NTA) for size determination of nano-catalyst dispersed in bitumen or heavy oil fractions was successfully developed. The produced catalyst particle size was found to be 111 nm (mode) with 80% of the particles in the range 57-176 nm. The effect of the main operating variables over the nano-particle retention and deposition was studied. Over 95% of particles retention was obtained, with no observable effect on the sandpack’s oil permeability. Concentration profiles along the porous media are similar for all tested conditions, with around 30% of nanoparticles at the entrance. Correlations for the profile and cumulative concentration are proposed. The morphological study of the resulting deposition showed particles deposited as large agglomerates for low temperature deposition tests, while high temperatures produced individually deposited particles near the entrance of the porous media.
Open Access
Discovering Relationships between Reservoir Properties and Production Data for CHOPS Using Data Mining Methods
(2016-01-15) Wang, Xi; Mahinpey, Nader; Wang, Xin; Dong, Mingzhe; Chen, Shengnan (Nancy)
Cold Heavy Oil Production with Sand (CHOPS) produces sand, and greatly contributes to primary oil recovery. It’s generally believed that wormholes, resulting from sand flow, enhance oil recovery in this process. However, due to complexity and variability, it’s difficult for wormhole models to precisely describe how wormholes develop within the formation. In this study, we regard wormholes as an integral black box. We apply data mining methods to explore how the reservoir attributes influence the CHOPS wells production. Gain ratio is used to rank and select the most important attributes for oil production. For overall oil production performance, cumulative porosity, cumulative oil saturation, effective thickness, and average shale content are the most important and relevant attributes. Decision trees constructed by C4.5 algorithm provide details of how to classify oil production instances according to reservoir attributes. All the correctly classified rates are over 55%, which is reliable accuracy in our results.
Open Access
Effects of Nanoparticles on Friction Reduction in Fluid Flow
(2023-08-07) Mohitian, Kimia; Bryant, Steven; Kantzas, Apostolos; Chen, Shengnan (Nancy)
The interaction between a flowing fluid and the solid surfaces over which it flows is fundamental to determining the resistance to flow offered by the solid. This interaction is commonly described as friction or drag in fluid mechanics and is the basis for the no-slip boundary condition in fluid dynamics. The slip behaviour of the fluid near the solid boundary, commonly quantified as the "slip length," is often used to quantify friction reduction. Nanoparticles (NPs) have been proposed to influence the fluid flow in reservoirs through several mechanisms, including modifying the slip behaviour of a fluid. This thesis investigates whether coating the surface of channels in glass micromodels with silica NPs affects the slip length of oil flow and water flow. Silica NPs with different shapes, surface coating and charges were tested to understand how the nature of these nanomaterials can affect friction. In-line coating, immersion, and spin coating were evaluated to determine how effectively each method coated the surface of the channel with NPs. Particle deposition was evaluated by water droplet contact angle measurement, scanning electron microscope (SEM) imaging, and elemental analysis. A uniform, crack-free, and stable distribution of NPs on the surface was observed using spin coating. Hydrophilic silica NP coating affected water differently from oil, causing a reduction in friction while oil flooding but an increase in friction for water. On the other hand, partially hydrophobic silica NPs reduced the friction for both water and oil flooding. The fundamental understanding of how NPs can be used as friction reducers for oil production will open new opportunities for designing low-energy and more sustainable oil production methods.
Open Access
Experimental and Modeling Phase Behaviour Studies of Water-Solvent-Bitumen System
(2016-01-13) Zirrahi, Mohsen; Hassanzadeh, Hassan; Abedi, Jalal; Henni, Amr; Maini, Brij; Moore, Robert Gordon; Chen, Shengnan (Nancy); Mohamad, Abdulmajeed
Solvent-aided thermal recovery methods for production of bitumen and heavy oils have recently gained interest. In these methods, mixture of saturated steam and solvent is co-injected into bitumen and heavy oil reservoirs. Measurement and thermodynamic modeling of phase behaviour and thermo-physical properties are necessary for finding better understandings of complex systems of water-solvent-bitumen and more accurate numerical simulation and optimization of solvent-aided thermal recovery methods. In this work, binary systems of water-solvent, water-bitumen and solvent-bitumen were studied. For each binary system, a specified experimental apparatus was designed and fabricated. Water solubility in bitumen was measured at temperature ranges up to 493 K. Density and viscosity of bitumen rich phase were measured to investigate effect of water dissolution on thermo-physical properties of bitumen phase. For solvent-water system, water content of methane, ethane and propane were measured at high temperatures and moderate pressures. Solubility of methane, ethane, propane and butane in MacKay River bitumen were measured as well as density and viscosity of solvent-saturated bitumen phase. Cubic-plus-association equation of state (CPA-EoS) was used to model the phase behaviour of the binary systems. Soave-Redlich-Kwong (SRK) equation of state combined with Wertheim’s first-order thermodynamic perturbation theory (TPT-1) were used to treat the physical and association interactions, respectively. The results showed that CPA-EoS accurately reproduces the solubility data of water content of n-alkane solvents, water solubility in bitumen, and solvent solubility in bitumen accurately. Ternary system of water-solvent-bitumen was studied by measurement of solvent solubility in bitumen as well as density and viscosity of bitumen rich phase in the presence and absence of water. The results showed that the presence of water decreases the solvent solubility in bitumen phase. CPA-EoS tuned using the experimental solubility data of the binary systems was found capable in representing the phase behaviour of the ternary system. Correlations were developed for density and viscosity of bitumen rich phase using the experimental data of the binary systems.
Open Access
Geothermal Energy and Carbon Dioxide Sequestration
(2023-04-26) Shi, Guangyu; Gates, Ian Donald; Chen, Shengnan (Nancy); Hu, Jinguang; Innanen, Kristopher A.H.; Zhao, Gang
Under the climate change crisis evolving from excessive greenhouse gas emissions, society is seeking ways to answer the call to produce clean energy. Carbon capture, utilization, and storage (CCUS) and geothermal energy are two major options to reach carbon neutrality. The research documented here examined four ways to lower emissions. In the first study, an enhanced geothermal system (EGS) in the Basal Cambrian Sandstone Unit in Alberta, Canada is explored. The second study examines the potential to combine underground CO2 sequestration and geothermal energy harvesting. The third study explores CO2-Enhanced Gas Recovery (CO2-EGR) in an offshore natural gas field located in the South China Sea. In the last study, the first China offshore CO2 sequestration operation in a shallow subsea feldspar-quartz sandstone formation is examined. The results demonstrate that open-loop EGS realizes an energy produced to energy invested ratio from 4 to 9 depending on operating rate and suggest that hydraulic fracturing accelerates energy harvesting and energy efficiency over the early process stages but the greater the injection rate, the smaller is the benefit of hydraulic fracturing. Second, combining both CO2 sequestration and geothermal operations is possible with commercial value and environmental benefits. Third, CO2-EGR demonstrates greater natural gas production together with CO2 sequestration and that there is potential that the process could be carbon neutral or negative. Fourth, offshore CO2 sequestration in a feldspar-quartz sandstone formation is possible and showcases that a dynamic behaviour occurs at the CO2 plume front where a relatively small amount of carbonate mineral precipitates which is subsequently dissolved when the acidified water in the plume passes the prior front location. The results showcase contributions for both CCUS and geothermal energy towards carbon emissions reduction.
Open Access
Hydraulic Fracturing and Flow-back Simulation in Unconventional Tight Reservoirs
(2016) Lin, Menglu; Chen, Shengnan (Nancy); Chen, Zhangxing (John); Clarkson, Christopher; Hejazi, Hossein; Lines, Laurence
At present, combination of the multistage hydraulic fracturing and horizontal wells has become a widely used technology in stimulating unconventional tight reservoirs in Western Canadian Sedimentary Basin (WCSB). It is important to understand hydraulic fracture propagation mechanism, effects of their properties and controlling factors affecting flow-back recovery. In this thesis, based on tight reservoir models in WCSB, firstly we examine different fracture geometry distributions and further discuss their effects on well productions. Then reservoir simulation coupled with rock geomechanics is employed to perform dynamic hydraulic fracturing for predicting hydraulic fracture dimensions and simulating fracturing liquid distribution. At last, Design of Experiments and response surface methodology are conducted to explore well operational parameters affecting flow-back recovery and net present value (NPV). This study provides new insights on the hydraulic fracturing and can be a reference for fracturing treatments in unconventional tight reservoirs.
Open Access
In Situ Consolidation of Tailings Muds
(2017) Famakinwa, Temilola; Gates, Ian; Chen, Shengnan (Nancy); Lu, Qingye (Gemma)
Oil sands tailings inventory is rapidly growing and current tailings treatment technologies are too costly or unable to handle the high solids content of the waste. The aim of the research documented here was to develop a new in-situ process for treating tailings that would cut costs by eliminating the need for tailings transport and re-deposition. The proof of concept of the new in-situ technology is given in this thesis. The process was able to increase the solids content of mature fine tailings by 2.7 times or (178 %). Furthermore, the process performed better than a treatment method that used the same solution chemistry as the new process, but in an ideal mixing environment. It was found that the performance of the in-situ method could be further improved by using carbon dioxide over air as mixing media. The process improves the consolidation and de-watering of mature fine tailings by two mechanisms. It chemically alters the tailings water to decrease the electronic double layer of clay fines and facilitates coagulation. Secondly, the vigorous bubbling of gas creates micro-fractures in the tailings deposit through which water flows to the surface. To optimize the design of the new process, a two-level factorial experiment was conducted to determine the treatment solution chemistry that produces the most strengthened tailings product.
Open Access
An integrated approach to characterize hydraulic fracturing-induced seismicity in shale reservoirs
(2021-10) Gang, Hui; Chen, Shengnan (Nancy); Chen, Zhangxing; Gates, Ian Donald; Shor, Roman J; Pedersen, Per Kent; Meybodi, Hamid Emami
In this study, an integrated approach of geology, geophysics, geomechanics and hydrodynamics is developed to characterize the hydraulic fracturing-induced seismicity in unconventional shale reservoirs. Firstly, a structural model including the faults and surfaces is developed by the multi-component 3D seismic interpretation. The local structure attributes analysis, and ant-tracking technique are then applied to identify the pre-existing faults and fractures distribution, where their distributions are calibrated by focal mechanism inversion of the mainshock events. Subsequently, a 3D geomechanical model is built, which incorporates the rock mechanics and in-situ stress regime into the structure model. Additionally, the hydraulic fracturing processes are simulated and hydraulic fractures geometry and fluid pressure distribution within the hydraulic fractures are estimated by history matching the net pressure. Finally, the fluid flow in hydraulic fractures is coupled with the geomechanical model to characterize the pore pressure diffusion and poroelastic stress perturbation that causes the fault to slip. As the field cases, the Mw 3.6 and Mw 4.1 induced seismicity near the Crooked Lake region are investigated to evaluate the applicability of the integrated approach. Moreover, the Mw 3.2 case and Mw 4.18 cases are analyzed to explore the controlling factors of hydraulic fracturing-induced seismicity in Western Canada. Based on eight field cases in Fox Creek, the susceptibility of hydraulic fracturing-induced seismicity towards fracturing stimulations are evaluated and potential mitigation strategies are proposed to reduce future seismicity risks. Finally, a comprehensive machine-learning approach is proposed to evaluate the susceptibility and mitigate the risks of hydraulically induced seismicity, as well as forecast the shale gas production via the integration of geological, geomechanical and operational factors in Fox Creek.
Open Access
Investigation of Single Phase NanoCellulose Transport Through Porous Media
(2017) Dziuba, Carter; Gates, Ian; Bryant, Steven; Maini, Brij; Chen, Shengnan (Nancy)
The application of nanotechnology to the petroleum industry has seen many recent advancements. Nanocellulose is an emerging nanoparticle at the forefront of research. Before nanocellulose can be injected into petroleum reservoirs, further understanding is needed as to the retention mechanisms that occur during nanocellulose transport through porous media. A series of unconsolidated sandpack floods were preformed with nanocellulose and the resulting retention and permeability reduction were measured. The experimental variables include nanocellulose type, sand grain size, flowrate, and salinity. It was found that all types of nanocellulose tested showed significantly different transport properties. Retention and permeability reduction increased as grain size decreased or flowrate decreased. As a general trend, the larger the size of aggregates in bulk solution, the greater the retention and permeability reduction. Salinity was found to be the primary parameter affecting transport. Increased salinity caused additional aggregation which resulted in increased straining and filter cake formation.
Open Access
Investigation of the Interaction between Nanoparticles, Asphaltenes, and Silica Surfaces for Inhibition and Remediation of Formation Damage
(2021-07-19) Montoya, Leidy Tatiana; Nassar, Nashaat N.; Chen, Shengnan (Nancy); Hassanzadeh, Hassan; Moore, Robert Gordon; Khoshnazar, Rahil; Dejam, Morteza
World population growth, increment in industrialization and motorization of the world, increment in technical development and living standards are some factors that keep contributing to the increasing of the global energy demand. Therefore, it is necessary to find alternative sources to meet these demands. Considering renewable and non-renewable energies, there is still an interest in enhancing the oil and gas recovery, because its reserves are considerable in terms of the energy supply. Nevertheless, there are several challenges facing the oil production related with asphaltenes, and it requires a knowledge on the deposition mechanism of this fraction of oil and the factors influencing it, since they are important in many parts of the production processes, and refinery catalyst deactivation, causing significant production losses. Accordingly, appropriate mitigation techniques, for surfaces exposed to asphaltenes or operating conditions, can be identified. It has been demonstrated that the use of nanoparticles may improve the mobility of oil. This is because nanoparticles may enhance wettability alteration or disaggregation of asphaltene aggregates. Accordingly, this study will help to understand the interactions between asphaltenes and nanoparticles, at the beginning using computational modeling and model molecules for resins and asphaltenes. It is important to consider that asphaltenes are not the only component in the oil and the adsorbent affinity is affected for it. Then, naturally derived silicate-based nanoparticles were used to investigate their performance on wettability alteration and what is the mechanism involved in continuous flow over pre-adsorbed/deposited asphaltene SiO2 sensors; this was achieved using a QCM-D, contact angle measurements and AFM images. The results showed that depending on the asphaltenes aggregation stage, the nanoparticles interact differently with them. Finally, basic silicate-based nanofluids were tested at reservoir conditions. The main results indicated that low salinity was the most promising formulation for inhibiting/remediating formation damage caused by asphaltene precipitation/deposition. Relative permeability curves showed a shift to right after the injection of nanoparticles, confirming the role of nanoparticles on wettability alteration. Oil recovery factor was also increased when using nanoparticles to inhibit/remediate the damage. Therefore, silicate-based nanoparticles are good candidates to use as treatment for asphaltene formation damage.
Open Access
Machine Learning Based Techno-economic Assessment and Optimization of an Enhanced Geothermal System
(2024-06-21) Xue, Zhenqian; Chen, Zhangxing (John); Shor, Roman; Aguilera, Roberto; Chen, Shengnan (Nancy); Hou, Bin; Chen, Zhangxing (John)
In the emergency to achieve decarbonization goals, transitioning from traditional fossil fuels to renewable energy sources is paramount within the energy sector. Geothermal energy, particularly through the utilization of Enhanced Geothermal Systems (EGS), is recognized as a promising low-carbon alternative for future energy supply, offering lower carbon intensity in electricity production. Despite its potential, conventional EGS operations face significant technical and economic challenges, compounded by the lack of an accurate and efficient optimization tool to enhance EGS profitability. This thesis addresses these challenges through three comprehensive studies. The first study introduces a novel CO2-water-EGS framework, applied to the Qiabuqia geothermal field in China, to explore the technical feasibility of overcoming the limitations of traditional EGSs (water-EGS and CO2-EGS). An integrated analysis, encompassing heat extraction and carbon storage, evaluates the impacts of seven operational factors. Key findings in this part are: (1) CO2-water-EGSs can recover more geothermal energy than water-EGSs and CO2-EGSs, whereas CO2-EGSs can store the largest volume of CO2 in the reservoir; (2) horizontal-well-EGSs generally offer superior heat mining capabilities over vertical-well-EGSs inspite of increased thermal breakthrough risks; (3) investigated technical factors show their complex correlations with technical performance, thereby necessitating the development of a high-performance optimization tool to maximize profitability. Secondly, an evaluation framework from technical and economic perspectives, incorporating the impact of carbon credit, is introduced for the first time to compare six EGS configurations. Sensitivity analyses explore the influence of various technical and economic variables. Results indicate that: (1) NPV (net present value) is a more effective metric than LCOE (levelized cost of electricity) for evaluating EGS economic viability; (2) CO2-water-EGSs are the most profitable options among all cases, with CO2-EGS showing an undesirable NPV due to excessive CO2 usage; (3) vertical-well-EGSs are economically superior to their horizontal counterparts; (4) economic outcomes are predominantly influenced by carbon credit rates, electricity market prices, and CO2 purchase prices, alongside the differential impacts of seven technical parameters. The third study develops an optimization framework incorporating machine learning and Differential Evolution (DE) algorithms, using NPV as the economic indicator for the Qiabuqia EGS. An optimal surrogate model determined through a comprehensive comparison of four machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN), are integrated with a DE-based optimization process to identify a strategy yielding optimal NPV under operational constraints. Results show: (1) the ANN is more recommended to generate a surrogate model for the Qiabuqia EGS, which demonstrates a promising prediction accuracy with a R^2 value of 97.3%; (2) the ANN-based DE optimization method identifies an operational strategy resulting in the highest NPV of 39.8 M$, surpassing over 3,000 randomly generated numerical cases; (3) this optimization tool significantly reduces computational time, illustrating over a 100,000 times decrease compared to conventional numerical simulation.
Open Access
Modelling of Non-Equilibrium Heavy Oil-Solvent Behaviour
(2021-12-17) Tai, Nan; Gates, Ian; Hejazi, Hossein; Chen, Shengnan (Nancy)
A multi-component kinetic model is proposed to simulate non-equilibrium solvent ex-solution and back dissolution processes in the heavy oil bulk environment. The model consists of micro bubbles, which inherit physical properties from the solution gas and is treated as a gas-like liquid, to simulate foamy oil behavior during volume expansion processes. Free gas is considered as being able to transfer directly to solution gas to simulate the hysteresis which happens in the dissolution process when volume pressure increases. The kinetic model comprises three pseudo-chemical reactions with seven parameters which were calibrated against experimental data. The model was validated by using a thermal reservoir simulator and the results were capable of predicting the oil-gas system volume changes for two different solvent-heavy oil systems accurately under various pressure variation rates at both 15 and 75°C. Four reaction orders and three reaction frequency factors were tuned and it indicated that in a solvent-heavy oil system with solvent CH4 or C2H6: (1) stronger foamy oil behavior exists with solvent of C2H6 instead of CH4 during pressure depletion processes for heavy oil with the similar solvent concentration; (2) for both solvents, micro bubble release rates are larger at higher temperature and the rates tend to increase with pressure decline regardless of temperature; and (3) in the solvent dissolution process, for both CH4 and C2H6, relatively high pressure and temperature are both significant elements for promoting solvent dissolution back into heavy oil.
Open Access
Modelling Water-Hydrocarbon Mutual Solubility in Multiphase Equilibrium Calculations
(2016) Yu, Hongbo; Chen, Zhangxing (John); Abedi, Jalal; Chen, Shengnan (Nancy)
Since the 1980s, multiphase equilibrium calculations have been well developed. Usually, water is excluded from the calculations although the amount of dissolved hydrocarbons and CO2 in water can be substantial. There are several published four-phase flash calculation methods using a single cubic EOS to model both hydrocarbon and aqueous phases, but the predicted gas solubility in water modeled from an EOS is orders of magnitude lower than experimental data. In this thesis, a generalized multiphase flash calculation algorithm is developed to address both the multiple phase behavior of a CO2/crude oil system and water-hydrocarbon mutual solubility simultaneously. The hydrocarbon phases are modeled with a cubic EOS, and the water phase is modelled with Henry’s law constants. Our results are compared with experimental data and calculation results from commercial software to validate the algorithm in different types of equilibria.
Open Access
Oil Sands Technology Pathway Evaluation Using Life Cycle Assessment and Mathematical Optimization
(2022-03-03) Dadashi Forshomi, Zainab; Bergerson, Joule A.; Gates, Ian D.; Mahinpey, Nader; Chen, Shengnan (Nancy); Mohamad, Abdulmajeed; Elkamel, Ali
Oil sands producers must improve the environmental performance of their operations to remain competitive in the energy sector in a carbon constrained world. These improvements include both incremental changes in the existing operation to make it more efficient (e.g., by applying process integration techniques) or fundamental changes to the operation (e.g., by adopting emerging technologies). However, an individual company will consider regulatory requirements and economic feasibility prior to making decisions about investments in these technologies. This thesis investigates the potential improvements in oil sands operations through both incremental efficiency improvements (i.e., lower energy consumption per unit of energy produced) and fundamental changes in their operations. In the first part of the thesis, cost and energy savings opportunities in Steam Assisted Gravity Drainage (SAGD) (an oil sands extraction and recovery process) are assessed by applying process integration techniques through the sequential application of a water treatment system optimization followed by conventional energy pinch analysis (incremental improvement). In the second part of the thesis, the focus is on exploring fundamental improvements in the oil sands sector and identifying the optimal technology pathways for oil sands production and processing with respect to economic and environmental objectives. A comprehensive techno-economic framework is developed that considers all technological and economic input parameters that affect the performance of the oil sands supply chain in terms of total cost, total energy consumption and GHG emissions. This framework is used to: 1) find the technical, economic and policy conditions under which emerging oil sands technologies become competitive alternatives in global crude oil markets, and 2) investigate the prospect of reaching Canada’s climate goals (as it relates to the oil sands sector) by implementing available emission reduction solutions while maintaining oil sands production capacity at the current or increased level in the next three decades. The results of this study help oil sands producers to better understand the long-term effects associated with the use of existing and emerging oil sands technologies. In addition, the results inform short- and long-term investment decision making in oil sands sector under various scenarios with different combinations of input parameters.
Open Access
Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches
(2024-04-25) Wang, Hai; Chen, Shengnan (Nancy); Chen, Zhangxing; Gates, Ian Donald; Shor, Roman J; Zendehboudi, Sohrab
The development of unconventional tight/shale gas reservoirs has undergone a revolutionary transformation, primarily fueled by advancements in horizontal drilling and multi-stage hydraulic fracturing technologies. These innovations have enabled the economical extraction of hydrocarbons from formations characterized by low permeability and complex fracture networks, positioning tight/shale gas as a pivotal component of the global energy mix. The accurate prediction of well production dynamics in these complex formations is a formidable challenge. Traditional empirical and numerical approaches, often fall short due to their inherent simplifications or computational demands. With the surge in data availability from unconventional reservoir developments, leveraging data-driven models for predicting well performance has become increasingly feasible and necessary. This thesis presents a comprehensive suite of machine learning frameworks to predict and enhance well production dynamics in tight/shale gas reservoirs. Initial efforts focus on predicting the first-year cumulative production of infill wells and optimizing their placement and stimulation design. Then, the study delves into the prediction of long-term production dynamics of shale gas wells using a dual-stage attention-based sequence-to-sequence model with some hard physics constraints. By encoding both tabular and time-series data, this model demonstrates superior accuracy and robustness in forecasting well production, outperforming traditional machine learning approaches. Subsequently, a novel physics-informed neural network approach is introduced to deduce the governing partial differential equation for shale gas production decline characterization, integrating Caputo fractional derivatives to capture the heavy-tailed phenomena in production series, thus offering a balance between interpretability and predictive capability. Further, the thesis explores the competitive adsorption of CH4/CO2 in shale formations using a Genetic Algorithm pruned Neural Network. This model robustly predicts the adsorption capacities, offering critical insights for CO2-enhanced shale gas recovery strategies and contributing to carbon capture and storage efforts.
Open Access
Quantitative Analysis of Multi-Phase Flowback From Multi-Fractured Horizontal Wells
(2017) Williams-Kovacs, Jesse Daniel; Clarkson, Christopher R.; Chen, Shengnan (Nancy); Hejazi, Hossein
Due to a decline in conventional reserves, particularly in North America, recent development has focused on low to ultra-low permeability unconventional reservoirs. Due to the low permeability of these plays, extensive hydraulic fracturing is required for commercial production. As a result of this growing trend, operators are looking for new methods to characterize hydraulic fractures, especially early in the well life. Several authors have identified high frequency flowback data as an early-time method for fracture characterization. This dissertation starts with, and builds on, two publications by Clarkson and Williams-Kovacs (2013a) and Clarkson and Williams-Kovacs (2013b), which set the ground work for quantitatively analyzing flowback from multi-fractured horizontal wells (MFHWs) completed in shale gas and light tight oil (LTO) reservoirs respectively. First a new shale gas model was developed to better capture the physics of the flowback problem. The tool was built using a similar conceptual model to that assumed by Clarkson and Williams-Kovacs (2013b) for LTO applications, although significant modifications were required to account for the complexities of shale gas reservoirs. A focus was also placed on stress-dependant porosity/permeability as a result of fracture closure during flowback. Although this new model yielded comparable results to the model developed by Clarkson and Williams-Kovacs (2013a) in the case study presented herein, by better capturing the physics of the problem the new model is applicable to more cases and creates a much improved platform for further development. Secondly, the LTO model developed by Clarkson and Williams-Kovacs (2013b) was expanded to account more complex problems, such as stage-by-stage flowback, multi-well analysis and multi-layer flowback for wells contacting multiple productive intervals. These advances of the base model greatly broaden the applicability of the developed methods to many of the complexities faced by operators in unconventional formations, particularly with advancements in completion technology and development strategies currently being employed in these formations. Thirdly, stochastic simulation and several assisted history-matching techniques were applied to an LTO data set to quantify the uncertainty in key fracture parameters and to optimize the history-match for the most accurate estimation of key fracture parameters. Application of stochastic simulation allows the operator to determine realistic bounds for future potential well performance while assisted history-matching leads to significantly improved results. Fourthly, several LTO case studies were conducted to address topics such as assessment of the potential economic value of conducting flowback analysis, and development of a modified model for analyzing flowback from LTO wells completed with an oil-based fracture fluid. Numerical simulation was also conducted to confirm the sequence of flow-regimes interpreted from field data. These case studies validate the simple analytical models developed, quantify the value to operators for applying such methods as well as demonstrate extensions which are required to analyze flowback from many modern completions. Lastly, a salinity model was developed to compliment the flow models primarily to confirm fracture surface area and volume. It is possible that this model could be applied in place of detailed flow modeling if enough of the key transport parameters can be accurately estimated. The flow simulation was successfully validated using the developed salinity model.
Open Access
Rate-Transient Analysis of Tight Oil and Gas Reservoirs
(2017) Qanbari, Farhad; Clarkson, Christopher R.; Pooladi-Darvish, Mehran; Hejazi, Hossein; Chen, Shengnan (Nancy); Lines, Laurence; Callard, Jeffrey G.
Horizontal wells completed in multiple hydraulic fracturing stages (multi-fractured horizontal wells or MFHWs) have been critical technologies applied to low-permeability (tight) oil and gas reservoirs in recent decades, resulting in commercial production. For each stage in a MFHW, the formation is fractured by injecting water and sand at high pressure. The resulting hydraulic fracture system enhances production from tight reservoirs by increasing the effective area for flow of the reservoir fluids. Therefore, fracture conductivity and total fracture area are key parameters affecting MFHW performance. A powerful tool for characterization of MFHWs is rate transient analysis (RTA); RTA models are commonly based on analytical solutions to fluid flow equations describing flow through the rock matrix and hydraulically-induced fractures to MFHWs. In addition to MFHW characterization, RTA is used for short- and long-term production forecasting (or estimation of ultimate recovery) and estimation of fluid-in-place. In order to obtain analytical solutions to the flow equations (for RTA purposes), simplifying assumptions have been made by practitioners such as constant formation permeability, constant properties of oil, constant hydraulic diffusivity of gas, and single-phase flow of the primary hydrocarbon phase. In this thesis, each of these assumptions are relaxed and corresponding analytical/semi-analytical solutions are developed for tight oil and gas reservoirs. Three methods are proposed for incorporation of the aforementioned nonlinearities into RTA. The methods utilize 1) the transformation of nonlinearity approach, 2) the iterative integral approach, and 3) the dynamic drainage area concept. The results of the proposed methods are compared against numerical simulation for validation, and are applied to field cases to demonstrate practical utility. Importantly, it is demonstrated that failure to incorporate corrections for the aforementioned nonlinearities into RTA can lead to significant errors in derived parameters, such as the linear flow parameter derived from transient linear flow analysis. The RTA methods developed in this thesis are intended to provide practitioners with more robust tools for analysis of tight oil and gas reservoirs.