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dc.contributor.authorShafiei, Ali 14:03:35 (GMT) 14:03:35 (GMT)
dc.description.abstractA significant amount of Viscous Oil (e.g., heavy oil, extra heavy oil, and bitumen) is trapped in Naturally Fractured Carbonate Reservoirs also known as NFCRs. The word VO endowment in NFCRs is estimated at ~ 2 Trillion barrels mostly reported in Canada, the USA, Russia, and the Middle East. To date, contributions to the world daily oil production from this immense energy resource remains negligible mainly due to the lack of appropriate production technologies. Implementation of a VO production technology such as steam injection is expensive (high capital investment), time-consuming, and people-intensive. Hence, before selecting a production technology for detailed economic analysis, use of cursory or broad screening tools or guides is a convenient means of gaining a quick overview of the technical feasibility of the various possible production technologies applied to a particular reservoir. Technical screening tools are only available for the purpose of evaluation of the reservoir performance parameters in oil sands for various thermal VO exploitation technologies such as Steam Assisted Gravity Drainage (SAGD), Cyclic Steam Stimulation (CSS), Horizontal well Cyclic steam Stimulation (HCS), and so on. Nevertheless, such tools are not applicable for VO NFCRs assessment without considerable modifications due to the different nature of these two reservoir types (e.g., presence and effects of fracture network on reservoir behavior, wettability, lithology, fabric, pore structure, and so on) and also different mechanisms of energy and mass transport. Considering the lack of robust and rapid technical reservoir screening tools for the purpose of quick assessment and performance prediction for VO NFCRs under thermal stimulation (e.g., steamflooding), developing such fast and precise tools seems inevitable and desirable. In this dissertation, an attempt was made to develop new screening tools for the purpose of reservoir performance prediction in VO NFCRs using all the field and laboratory available data on a particular thermal technology (vertical well steamflooding). Considering the complex and heterogeneous nature of the NFCRs, there is great uncertainty associated with the geological nature of the NFCRs such as fracture and porosity distribution in the reservoir which will affect any modeling tasks aiming at modeling of processes involved in thermal VO production from these types of technically difficult and economically unattractive reservoirs. Therefore, several modeling and analyses technqiues were used in order to understand the main parameters controlling the steamflooding process in NFCRs and also cope with the uncertainties associated with the nature of geologic, reservoir and fluid properties data. Thermal geomechanics effects are well-known in VO production from oil sands using thermal technologies such as SAGD and cyclic steam processes. Hence, possible impacts of thermal processes on VO NFCRs performance was studied despite the lack of adequate field data. This dissertation makes the following contributions to the literature and the oil industry: Two new statistical correlations were developed, introduced, and examined which can be utilized for the purpose of estimation of Cumulative Steam to Oil Ratio (CSOR) and Recovery Factor (RF) as measures of process performance and technical viability during vertical well steamflooding in VO Naturally Fractured Carbonate Reservoirs (NFCRs). The proposed correlations include vital parameters such as in situ fluid and reservoir properties. The data used are taken from experimental studies and also field trials of vertical well steamflooding pilots in viscous oil NFCRs reported in the literature. The error percentage for the proposed correlations is < 10% for the worst case and contains fewer empirical constants compared with existing correlations for oil sands. The interactions between the parameters were also considered. The initial oil saturation and oil viscosity are the most important predictive factors. The proposed correlations successfully predicted steam/oil ratios and recovery factors in two heavy oil NFCRs. These correlations are reported for the first time in the literature for this type of VO reservoirs. A 3-D mathematical model was developed, presented, and examined in this research work, investigating various parameters and mechanisms affecting VO recovery from NFCRs using vertical well steamflooding. The governing equations are written for the matrix and fractured medium, separately. Uncertainties associated with the shape factor for the communication between the matrix and fracture is eliminated through setting a continuity boundary condition at the interface. Using this boundary condition, the solution method employed differs from the most of the modeling simulations reported in the literature. A Newton-Raphson approach was also used for solving mass and energy balance equations. RF and CSOR were obtained as a function of steam injection rate and temperature and characteristics of the fractured media such as matrix size and permeability. The numerical solution clearly shows that fractures play an important role in better conduction of heat into the matrix part. It was also concluded that the matrix block size and total permeability are the most important parameters affecting the dependent variables involved in steamflooding. A hybrid Artificial Neural Network model optimized by co-implementation of a Particle Swarm Optimization method (ANN-PSO) was developed, presented, and tested in this research work for the purpose of estimation of the CSOR and RF during vertical well steamflooding in VO NFCRs. The developed PSO-ANN model, conventional ANN models, and statistical correlations were examined using field data. Comparison of the predictions and field data implies superiority of the proposed PSO-ANN model with an absolute average error percentage < 6.5% , a determination coefficient (R2) > 0.98, and Mean Squared Error (MSE) < 0.06, a substantial improvement in comparison with conventional ANN model and empirical correlations for prediction of RF and CSOR. This indicates excellent potential for application of hybrid PSO-ANN models to screen VO NFCRs for steamflooding. This is the first time that the ANN technique has been applied for the purpose of performance prediction of steamflooding in VO NFCRs and also reported in the literature. The predictive PSO-ANN model and statistical correlations have strong potentials to be merged with heavy oil recovery modeling softwares available for thermal methods. This combination is expected to speed up their performance, reduce their uncertainty, and enhance their prediction and modeling capabilities. An integrated geological-geophysical-geomechanical approach was designed, presented, and applied in the case of a NFCR for the purpose of fracture and in situ stresses characterization in NFCRs. The proposed methodology can be applied for fracture and in situ stresses characterization which is beneficial to various aspects of asset development such as well placement, drilling, production, thermal reservoir modeling incorporating geomechanics effects, technology assessment and so on. A conceptual study was also conducted on geomechanics effects in VO NFCRs during steamflooding which is not yet well understood and still requires further field, laboratory, and theoretical studies. This can be considered as a small step forward in this area identifying positive potential of such knowledge to the design of large scale thermal operations in VO NFCRs.en
dc.publisherUniversity of Waterlooen
dc.subjectheavy oilen
dc.subjectextra heavy oilen
dc.subjectnaturally fractured reservoirsen
dc.subjectcarbonate reservoirsen
dc.subjectthermal heavy oil recoveryen
dc.subjectvertical well steamfloodingen
dc.subjectnumerical modelingen
dc.subjectthermal reservoir simulationen
dc.subjectfractured porous mediaen
dc.subjectheat transferen
dc.subjectmass transferen
dc.subjectreservoir geomechanicsen
dc.subjecttechnical screening criteriaen
dc.subjectreservoir screening toolsen
dc.subjectmathematical modelingen
dc.subjectstatistical correlationsen
dc.subjectartificial neural networksen
dc.titleMathematical and Statistical Investigation of Steamflooding in Naturally Fractured Carbonate Heavy Oil Reservoirsen
dc.typeDoctoral Thesisen
dc.subject.programEarth Sciencesen Sciencesen
uws-etd.degreeDoctor of Philosophyen

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