Integration of Numerical and Machine Learning Protocols for Coupled Reservoir-wellbore Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Integration of Numerical and Machine Learning Protocols for Coupled Reservoir-wellbore Models PDF full book. Access full book title Integration of Numerical and Machine Learning Protocols for Coupled Reservoir-wellbore Models by Venkataramana Putcha. Download full books in PDF and EPUB format.
Author: Venkataramana Putcha
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
As the reservoir pressure declines with time, many of the wells do not have adequate bottom-hole pressure to carry the fluids to the surface. Under such circumstances, artificial lift mechanisms must be employed. Amongst various artificial lift mechanisms, a significant proportion of wells utilize the gas-lift mechanism, which is an extension of the natural flow. In gas-lift implementation, high pressure gas is injected into the wellbore through a valve, where injected gas supports production by altering the composition and reducing the density, and increasing the velocity of the produced fluids. In order to design a gas-lift system, a study of the inflow performance of the fluid from the reservoir into the wellbore, combined with the outflow performance of the fluids from the bottom of the wellbore to the surface is necessary. For this purpose, existing technologies for optimization of gas-lift systems predominantly use empirical correlations in order to reduce the computational overhead. These systems use a single-equation based inflow performance relations and black-oil outflow performance correlations that have restricted applicability in systems where the fluid composition varies spatially and temporally. The contemporary protocols consider the oil flow rate, water cut and formation gas-liquid ratio and well productivity index at a given instant of time to calculate the optimal quantity of gas lift injection. Due to this methodology, the effects of pressure decline and subsequent variations in well performance are not adequately captured. This results in a solution which determines the maximum liquid flow rate expected for a given gas lift injection rate only for the instantaneous period at which the study has been performed. This optimal gas lift injection rate may or may not provide the maximum total output of oil over the producing life of the well. As a first step, a compositional coupled numerical reservoir and wellbore hydraulics models has been developed as a part of this work. These hard-computing tools simulate the variations in composition, pressure and production profiles of a gas lift well and its associated reservoir from inception to abandonment. One more advantage of this method is that it can predict the future performance of a well with or without the details of well production history. This capability can be useful when gas lift is introduced in a well immediately after its completion post a drilling or a work-over job. Soft computing tools have gained popularity in the petroleum industry due to their speed, simplicity, wide range of applicability, capacity to identify patterns and ability to provide inverse solutions. The fully numerical coupled reservoir-wellbore simulator developed is computationally expensive. In order to develop a faster system, firstly, an ANN based wellbore hydraulics tool is developed and coupled with the numerical reservoir simulator. The data utilized for training the ANN tool was generated using the numerical wellbore hydraulics tool. Both the numerical and ANN wellbore hydraulics models were validated against cases from the field and another compositional numerical model from the literature. The average relative deviation with respect to field data was observed to be 2.2% and 2.4% respectively for the ANN and numerical wellbore hydraulics model, respectively. When compared against another compositional numerical model, the average relative deviation for the ANN based model was observed to be between 3.3% and 7.1%, while it was between 2.3% and 8.1% for the numerical model developed in this work. While the ANN based wellbore hydraulics model maintained the accuracy of the numerical model, it outperformed its counterpart the numerical model, by four orders of magnitude in terms of speed-up. The ANN based wellbore model was also coupled with the numerical reservoir simulator. This resultant model which involves a coupled numerical-ANN system is faster than the fully numerical coupled system by about 160 times. This coupled tool was used to generate a gas lift database of cumulative oil production of a well with various reservoir and wellbore operating conditions under a range of operating gas lift injection depths and flow rates. This database was used to develop an ANN based gas lift model that is capable of generating performance curves plotting total oil produced during the producing life of a well as a function of gas lift injection rate. Blind testing of the ANN gas lift model showed an average absolute error of 16.6 % with respect to the predictions of the coupled numerical-ANN reservoir wellbore model. This fully ANN based gas lift model provided a speed-up by four orders of magnitude with respect to the coupled numerical-ANN based model. Hence, a fast, robust and versatile model has been developed for maximizing total primary oil recovery using gas lift optimization through integration of numerical and neuro-simulation.
Author: Venkataramana Putcha
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
As the reservoir pressure declines with time, many of the wells do not have adequate bottom-hole pressure to carry the fluids to the surface. Under such circumstances, artificial lift mechanisms must be employed. Amongst various artificial lift mechanisms, a significant proportion of wells utilize the gas-lift mechanism, which is an extension of the natural flow. In gas-lift implementation, high pressure gas is injected into the wellbore through a valve, where injected gas supports production by altering the composition and reducing the density, and increasing the velocity of the produced fluids. In order to design a gas-lift system, a study of the inflow performance of the fluid from the reservoir into the wellbore, combined with the outflow performance of the fluids from the bottom of the wellbore to the surface is necessary. For this purpose, existing technologies for optimization of gas-lift systems predominantly use empirical correlations in order to reduce the computational overhead. These systems use a single-equation based inflow performance relations and black-oil outflow performance correlations that have restricted applicability in systems where the fluid composition varies spatially and temporally. The contemporary protocols consider the oil flow rate, water cut and formation gas-liquid ratio and well productivity index at a given instant of time to calculate the optimal quantity of gas lift injection. Due to this methodology, the effects of pressure decline and subsequent variations in well performance are not adequately captured. This results in a solution which determines the maximum liquid flow rate expected for a given gas lift injection rate only for the instantaneous period at which the study has been performed. This optimal gas lift injection rate may or may not provide the maximum total output of oil over the producing life of the well. As a first step, a compositional coupled numerical reservoir and wellbore hydraulics models has been developed as a part of this work. These hard-computing tools simulate the variations in composition, pressure and production profiles of a gas lift well and its associated reservoir from inception to abandonment. One more advantage of this method is that it can predict the future performance of a well with or without the details of well production history. This capability can be useful when gas lift is introduced in a well immediately after its completion post a drilling or a work-over job. Soft computing tools have gained popularity in the petroleum industry due to their speed, simplicity, wide range of applicability, capacity to identify patterns and ability to provide inverse solutions. The fully numerical coupled reservoir-wellbore simulator developed is computationally expensive. In order to develop a faster system, firstly, an ANN based wellbore hydraulics tool is developed and coupled with the numerical reservoir simulator. The data utilized for training the ANN tool was generated using the numerical wellbore hydraulics tool. Both the numerical and ANN wellbore hydraulics models were validated against cases from the field and another compositional numerical model from the literature. The average relative deviation with respect to field data was observed to be 2.2% and 2.4% respectively for the ANN and numerical wellbore hydraulics model, respectively. When compared against another compositional numerical model, the average relative deviation for the ANN based model was observed to be between 3.3% and 7.1%, while it was between 2.3% and 8.1% for the numerical model developed in this work. While the ANN based wellbore hydraulics model maintained the accuracy of the numerical model, it outperformed its counterpart the numerical model, by four orders of magnitude in terms of speed-up. The ANN based wellbore model was also coupled with the numerical reservoir simulator. This resultant model which involves a coupled numerical-ANN system is faster than the fully numerical coupled system by about 160 times. This coupled tool was used to generate a gas lift database of cumulative oil production of a well with various reservoir and wellbore operating conditions under a range of operating gas lift injection depths and flow rates. This database was used to develop an ANN based gas lift model that is capable of generating performance curves plotting total oil produced during the producing life of a well as a function of gas lift injection rate. Blind testing of the ANN gas lift model showed an average absolute error of 16.6 % with respect to the predictions of the coupled numerical-ANN reservoir wellbore model. This fully ANN based gas lift model provided a speed-up by four orders of magnitude with respect to the coupled numerical-ANN based model. Hence, a fast, robust and versatile model has been developed for maximizing total primary oil recovery using gas lift optimization through integration of numerical and neuro-simulation.
Author: Brandon G. Thomas
Publisher:
ISBN:
Category : Oil reservoir engineering
Languages : en
Pages : 296
Get Book Here
Book Description
Author: Shuyu Sun
Publisher: Gulf Professional Publishing
ISBN: 0128209623
Category : Science
Languages : en
Pages : 342
Get Book Here
Book Description
Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today's petroleum and reservoir engineer to optimize more complex developments. - Understand commonly used and recent progress on definitions, models, and solution methods used in reservoir simulation - World leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learning - Gain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.
Author: Mahdy Shirdel
Publisher:
ISBN:
Category :
Languages : en
Pages : 402
Get Book Here
Book Description
Two-phase flow occurs during the production of oil and gas in the wellbores. Modeling this phenomenon is important for monitoring well productivity and designing surface facilities. Since the transient time period in the wellbore is usually shorter than reservoir time steps, stabilized flow is assumed in the wellbore. As such, semi-steady state models are used for modeling wellbore flow dynamics. However, in the case that flow variations happen in a short period of time (i.e., a gas kick during drilling) the use of a transient two-phase model is crucial. Over the last few years, a number of numerical and analytical wellbore simulators have been developed to mimic wellbore-reservoir interaction. However, some issues still remain a concern in these studies. The main issues surrounding a comprehensive wellbore model consist of fluid property calculations, such as black-oil or compositional models, governing equations, such as mechanistic or correlation-based models, effect of temperature variation and non-isothermal assumption, and methods for coupling the wellbore to the reservoir. In most cases, only standalone wellbore models for blackoil have been used to simulate reservoir and wellbore dynamic interactions. Those models are based on simplified assumptions that lead to an unrealistic estimation of pressure and temperature distributions inside the well. In addition, most reservoir simulators use rough estimates for the perforation pressure as a coupling condition between the wellbore and the reservoir, neglecting pressure drops in the horizontal section. In this study, we present an implementation of a compositional, pseudo steady-state, non-isothermal, coupled wellbore-reservoir simulator for fluid flow in wellbores with a vertical section and a horizontal section embedded on the producing reservoir. In addition, we present the implementation of a pseudo-compositional, fully implicit, transient two-fluid model for two-phase flow in wellbores. In this model, we solve gas/liquid mass balance, gas/liquid momentum balance, and two-phase energy equations in order to obtain the five primary variables: liquid velocity, gas velocity, pressure, holdup and temperature. In our simulation, we compared stratified, bubbly, intermittent flow effects on pressure and temperature distributions in either a transient or steady-state condition. We found that flow geometry variation in different regimes can significantly affect the flow parameters. We also observed that there are significant differences in flow rate prediction between a coupled wellbore-reservoir simulator and a stand-alone reservoir simulator, at the early stages of production. The outcome of this research leads to a more accurate and reliable simulation of multiphase flow in the wellbore, which can be applied to surface facility design, well performance optimization, and wellbore damage estimation.
Author: Peyman Pourafshary
Publisher:
ISBN:
Category : Gas wells
Languages : en
Pages : 0
Get Book Here
Book Description
Hydrocarbon reserves are generally produced through wells drilled into reservoir pay zones. During production, gas liberation from the oil phase occurs due to pressure decline in the wellbore. Thus, we expect multiphase flow in some sections of the wellbore. As a multi-phase/multi-component gas-oil mixture flows from the reservoir to the surface, pressure, temperature, composition, and liquid holdup distributions are interrelated. Modeling these multiphase flow parameters is important to design production strategies such as artificial lift procedures. A wellbore fluid flow model can also be used for pressure transient test analysis and interpretation. Considering heat exchange in the wellbore is important to compute fluid flow parameters accurately. Modeling multiphase fluid flow in the wellbore becomes more complicated due to heat transfer between the wellbore fluids and the surrounding formations. Due to mass, momentum, and energy exchange between the wellbore and the reservoir, the wellbore model should be coupled with a numerical reservoir model to simulate fluid flow accurately. This model should be non-isothermal to consider the effect of temperature. Our research shows that, in some cases, ignoring compositional effects may lead to errors in pressure profile prediction for the wellbore. Nearly all multiphase wellbore simulations are currently performed using the "black oil" approach. The primary objective of this study was to develop a non-isothermal wellbore simulator to model transient fluid flow and temperature and couple the model to a reservoir simulator called General Purpose Adaptive Simulator (GPAS). The coupled wellbore/reservoir simulator can be applied to steady state problems, such as production from, or injection to a reservoir as well as during transient phenomena such as well tests to accurately model wellbore effects. Fluid flow in the wellbore may be modeled either using the blackoil approach or the compositional approach, as required by the complexity of the fluids. The simulation results of the new model were compared with field data for pressure gradients and temperature distribution obtained from wireline conveyed pressure recorder and acoustic fluid level measurements for a gas/oil producer well during a buildup test. The model results are in good agreement with the field data. Our simulator gave us further insights into the wellbore dynamics that occur during transient problems such as phase segregation and counter-current multiphase flow. We show that neglecting these multiphase flow dynamics would lead to unreliable results in well testing analysis.
Author: Mahdy Shirdel
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
During the production and transportation of oil and gas, flow assurance issues may occur due to the solid deposits that are formed and carried by the flowing fluid. Solid deposition may cause serious damage and possible failure to production equipment in the flow lines. The major flow assurance problems that are faced in the fields are concerned with asphaltene, wax and scale deposition, as well as hydrate formations. Hydrates, wax and asphaltene deposition are mostly addressed in deep-water environments, where fluid flows through a long path with a wide range of pressure and temperature variations (Hydrates are generated at high pressure and low temperature conditions). In fact, a large change in the thermodynamic condition of the fluid yields phase instability and triggers solid deposit formations. In contrast, scales are formed in aqueous phase when some incompatible ions are mixed. Among the different flow assurance issues in hydrocarbon reservoirs, asphaltenes are the most complicated one. In fact, the difference in the nature of these molecules with respect to other hydrocarbon components makes this distinction. Asphaltene molecules are the heaviest and the most polar compounds in the crude oils, being insoluble in light n-alkenes and readily soluble in aromatic solvents. Asphaltene is attached to similarly structured molecules, resins, to become stable in the crude oils. Changing the crude oil composition and increasing the light component fractions destabilize asphaltene molecules. For instance, in some field situations, CO2 flooding for the purpose of enhanced oil recovery destabilizes asphaltene. Other potential parameters that promote asphaltene precipitation in the crude oil streams are significant pressure and temperature variation. In fact, in such situations the entrainment of solid particulates in the flowing fluid and deposition on different zones of the flow line yields serious operational challenges and an overall decrease in production efficiency. The loss of productivity leads to a large number of costly remediation work during a well life cycle. In some cases up to $5 Million per year is the estimated cost of removing the blockage plus the production losses during downtimes. Furthermore, some of the oil and gas fields may be left abandoned prematurely, because of the significance of the damage which may cause loss about $100 Million. In this dissertation, we developed a robust wellbore model which is coupled to our in-house developed compositional reservoir model (UTCOMP). The coupled wellbore/reservoir simulator can address flow restrictions in the wellbore as well as the near-wellbore area. This simulator can be a tool not only to diagnose the potential flow assurance problems in the developments of new fields, but also as a tool to study and design an optimum solution for the reservoir development with different types of flow assurance problems. In addition, the predictive capability of this simulator can prescribe a production schedule for the wells that can never survive from flow assurance problems. In our wellbore simulator, different numerical methods such as, semi-implicit, nearly implicit, and fully implicit schemes along with blackoil and Equation-of-State compositional models are considered. The Equation-of-State is used as state relations for updating the properties and the equilibrium calculation among all the phases (oil, gas, wax, asphaltene). To handle the aqueous phase reaction for possible scales formation in the wellbore a geochemical software package (PHREEQC) is coupled to our simulator as well. The governing equations for the wellbore/reservoir model comprise mass conservation of each phase and each component, momentum conservation of liquid, and gas phase, energy conservation of mixture of fluids and fugacity equations between three phases and wax or asphaltene. The governing equations are solved using finite difference discretization methods. Our simulation results show that scale deposition is mostly initiated from the bottom of the wellbore and near-wellbore where it can extend to the upper part of the well, asphaltene deposition can start in the middle of the well and the wax deposition begins in the colder part of the well near the wellhead. In addition, our simulation studies show that asphaltene deposition is significantly affected by CO2 and the location of deposition is changed to the lower part of the well in the presence of CO2. Finally, we applied the developed model for the mechanical remediation and prevention procedures and our simulation results reveal that there is a possibility to reduce the asphaltene deposition in the wellbore by adjusting the well operation condition.
Author: Akkharachai Limpasurat
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Liquid loading in a gas well occurs when the upward gas flow rate is insufficient to lift the coproduced liquid to the surface, which results in an accumulation of liquid at the bottom of the well. The liquid column in the tubing creates backpressure on the formation, which decreases the gas production rate and may stop the well from flowing. To model these phenomena, the dynamic interaction between the reservoir and the wellbore must be characterized. Due to wellbore phase re-distribution and potential phase-reinjection into the reservoir, the boundary conditions must be able to handle changing flow direction through the connections between the two subsystems. This study presents a new formulation of the wellbore boundary condition used in reservoir simulators. The boundary condition uses the new state variable, the multiphase zero flow pressure (MPZFP, p0), to determine flow direction in the connection grid block. If the wellbore pressure is less than the p0, the connection is producing; otherwise, it is injecting. The volumetric proportion of the flow is always determined by the upstream side. The new reservoir simulator is used in coupled modeling associated with liquid loading phenomena. The metastable condition can be modeled in a simple manner without any limiting assumptions and numerical stability problems. We also applied this simulator for history matching of a gas well flowing with an intermittent production strategy. A basic transient wellbore model was developed for this purpose. The long-term tubinghead pressure (THP) history can be traced by our coupled simulation. Our modeling examples indicated that, the new wellbore boundary condition is suitable in modeling the dynamic interactions between reservoir and wellbore subsystems during liquid loading. The flow direction through the connection grid block can be automatically detected by our boundary condition without numerical difficulty during the course of the simulation. In addition, the capillary pressure can be accounted at the connection grid blocks when applying our new formulation in the reservoir simulator. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151699
Author: Knut-Andreas Lie
Publisher: Cambridge University Press
ISBN: 1108492436
Category : Business & Economics
Languages : en
Pages : 677
Get Book Here
Book Description
Presents numerical methods for reservoir simulation, with efficient implementation and examples using widely-used online open-source code, for researchers, professionals and advanced students. This title is also available as Open Access on Cambridge Core.
Author: Sathish Sankaran
Publisher:
ISBN: 9781613998205
Category :
Languages : en
Pages : 108
Get Book Here
Book Description
Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.
Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 776
Get Book Here
Book Description
