Patent Application
20080065362
The foregoing and other advantages of the present technique may become apparent upon reading the following detailed description and upon reference to the drawings in which:
In the following detailed description, the specific embodiments of the present invention will be described in connection with its preferred embodiments. However, to the extent that the following description is specific to a particular embodiment or a particular use of the present invention, this is intended to be illustrative only and merely provides a concise description of the exemplary embodiments. Accordingly, the invention is not limited to the specific embodiments described below, but rather, the invention includes all alternatives, modifications, and equivalents falling within the true scope of the appended claims.
The present technique is directed to a method for managing and modeling wellbore completions to evaluate, analyze and assist in the production of hydrocarbons from subsurface formations. In particular, the present techniques describe the application of computational fluid dynamics (CFD) modeling methods in analyzing and interpreting temperature, pressure, and flow data measured on water, oil, liquid and/or gas flowstreams in wells, which may be used with real-time data to enhance hydrocarbon recovery. The present techniques may be utilized to predict the thermal profile under varying producing conditions. Further, the methods described herein may facilitate rapid responses to changes in well production and improve diagnosis of flow behavior in the wellbore through the use of holistic models that incorporate the macro-structure of the well and the surrounding near-wellbore region (e.g. region surrounding the well) as well as the finer details of in-wellbore instrumentation, thereby accounting for the coupled effects these mechanical, geometrical, and chemical factors may have on the resulting flow profile. The modeling may be used both for design of new wells (e.g. for uncertainty analysis) and production history matching of existing wells (e.g. for reservoir evaluation and forward modeling comparison with sensory data).
For instance, under the present techniques, a method and apparatus to evaluate the temperature and flow characteristics in complex well completions is described. In this method, the Wellbore completion, including both the inner wellbore instrumentation as well as the surrounding near wellbore region, may be discretized into a two-dimensional or three-dimensional computer model of the wellbore completion. The resulting mesh may include valves or tubing geometry that result in countercurrent flow between the annulus and tubing. Axial and radial resolution in the model may be user defined by varying the fineness or coarseness in the mesh. Navier-Stokes equations of fluid dynamics, which nodal correlations only approximate, may then be solved in each cell of the mesh. Also, energy equations may account for convective and conductive heat transfer both radially as well as axially in both the wellbore as well as the surrounding near wellbore region.
Further, the present techniques provide a process to manipulate and utilize real-time data from permanent downhole monitoring (PDM) systems, which may include fiber optic (F-O) pressure, temperature, velocity and/or flow sensors. These interpretative methods are centered upon detailed computational fluid dynamics (CFD) simulations of the wellbore and surrounding near-wellbore region. That is, in addition to complex geometric flow dependencies, the CFD model facilitates solution of the highly coupled physics present in multi-phase systems, such as gas compressibility effects that impact heat transfer and the thermal profile of the wellbore. In the near-wellbore region, log derived reservoir permeabilities and porosities are honored and used to populate the CFD model of the wellbore completion. By running the CFD model for different anticipated production scenarios, a series of thermal “type curves” can be generated that can be used to “predict” the time-dependent flow, pressure, and/or thermal profiles recorded by the PDM system. These may be used to assess the utility of a thermal sensor in light of anticipated reservoir and wellbore behavior or reduce the time involved for post-measurement analysis for real-time sensory data.
In contrast to other approaches, the “forward modeling” involves obtaining initial production profiles to predictively and proactively assess the form of a flow, pressure, velocity and/or thermal profile. Other approaches, such as multi-nodal approaches, may be suited for taking measured flow or thermal profiles and matching the profiles by adjustment of a plurality of wellbore or reservoir parameters. For instance, with other approaches, such as an “inverse modeling” approach, limited reservoir or formation data from well logs or well tests may be the only data available and utilized in the analysis. Yet, with continuous well surveillance, well-developed characterizations of fluid properties and reservoir properties may be utilized to constrain and enhance the input parameters of the wellbore inflow models with the present techniques. That is, the present techniques, which may be correlated to reservoir simulation data, may predictively assess the impact of reservoir depletion on fluid properties and wellbore drawdown, and thus, the ensuing flow rate, pressure, velocity and thermal profiles in the wellbore.
In particular, with complex completions, such as those with commingled or countercurrent flows in opposite directions in the tubing and annulus, other approaches may not account for variations within the wellbore, rendering interpretation of sensory data inaccurate. While other approaches may be adequate for simple completions (e.g. single contributing inflow zone and unidirectional flow), they may fail when applied to systems where flow and thermal profiles show significant radial variation within the wellbore. Indeed, the pipe flow correlations used between nodes in typical multi-nodal approaches only approximate the Navier-Stokes solutions, and may not be applicable in some of the complex completions described above. However, these variations may result from unique flow patterns around valves, chokes, sleeves, and other downhole instrumentation, or from countercurrent heat exchange between the tubing and annulus, for example. Accordingly, the use of CFD modeling may provide a numerical solution to the full set of Navier-Stokes equations of fluid dynamics, which are an exact characterization of the flow behavior.
Generally, CFD modeling is not utilized for flow through a wellbore and the surrounding near wellbore region. While CFD may be applied to short length/scale studies, such as flow around a valve or foil, or development of turbulent eddies and their attachment and detachment to walls, the CFD modeling is considered to be too computational expensive for other applications. As such, the long asymmetry of deep wells is not typically an application of CFD modeling. However, as noted above, the use of the CFD modeling and coupling of a porous media model of the reservoir to a wellbore model, wherein the full Navier-Stokes equations are solved, is beneficial and may enhance well operations.
Turning now to the drawings, and referring initially to
The flow chart begins at block 102. At block 104, a wellbore model may be constructed. The construction of the wellbore model may include designing a wellbore completion for a well. This process, which is described further in
Once the completion is installed, the well operations may be performed as shown in blocks 108-118. At block 108, the well is operated. The operation of the well may involve operating the well based on the analysis of the wellbore model to produce hydrocarbons from a subsurface formation. At block 110, sensory data may be obtained from measurement devices, such as sensors, gauges and/or meters, within the well. The measurement devices may include fiber optic (F-O) distributed temperature sensing (DTS) system that may be strapped to portions of the completion, F-O sensors that collect thermal data, flow rate data, velocity data, pressure data, and the like. The sensory data may include data collected over a specific period of time. The sensory data may then be examined to determine if production conditions have changed. The production conditions may include reservoir pressures, bottom hole pressures, drawdown and the like, as is known by those skilled in the art. If the production conditions have changed, then a measured production profile may be generated from the sensory data. The measured production profile, which includes sensory data may be formatted similar to the simulated production profile, may include sensory data of pressures associated to depth, temperatures associated to depth, flow rates associated to depth, fluid flow velocities associated to depth, and any combination thereof. Then, a determination is made whether the production profile has changed, as shown in block 112. The determination may include comparing the measured production profile to the one of the simulated production profiles to determine whether a modification should be made to the completion. This determination may be based on experience or specific thresholds set for operating the well. If the measured production profile has not changed, the well may continue to be operated as discussed in block 110.
However, if the production profile has changed, the well operation may be modified as shown in block 114. The modification of the well operation may include adjusting or exercising downhole or completion equipment, such as exercising a valve, adjusting the location or completion equipment, and shutting the well for reconfiguration of the completion, for example. At block 116, a determination is made whether the modification provided the desired response. The desired response may be to reduce water intake by the completion, or another action to maintain production or enhance well operations. If the desired response is not produced, the wellbore model and sensory data may be reassessed, as shown in block 118. The reassessment may include re-evaluating the wellbore model inputs (e.g. rock data and boundary conditions) and simulating additional simulation production profiles based on the revised wellbore model. Then, the well may be operated as discussed in block 110. However, if the desired response is produced, the process may continue by revising the stimulated production profiles with the sensory data, maintain the simulated production profiles without revision, and/or the process may end, as shown in block 120.
Beneficially, the present techniques may be utilized to reduce the time utilized to response to changes in production conditions. To facilitate these changes, the simulated and measured production profiles may be used to provide a better understanding of the well's performance and guide personnel in reviewing the sensory data.
In
The flow chart begins at block 202. At block 204, a design of a completion for a well is created. The design may be a computer model of the wellbore geometry created using a well schematic or completion design as a basis. The computer model may include both the macro-scale geometry of the casing and tubing dimensions, as well as the details of the downhole instrumentation, such as packers, mandrels, valves, control lines, and perforations. Then, the mesh for the wellbore model may be constructed in block 206. The construction of the mesh for the wellbore model may include discretizing and meshing the geometrical representation of the wellbore completion at a resolution fine enough to capture the details of the flow profile. The level of resolution may vary from several feet of tubing in the axial direction to a few inches or less around perforations, valves, and orifices where turbulent eddy effects may be expected to impact the local flow characteristics or induce pressure losses. Coupled to the wellbore is the surrounding near-wellbore region, modeled as a porous medium. The radial extent of the near-wellbore region may be modeled from a few feet to hundreds of feet as required. The radial extent may be adjusted by varying the mesh geometry and resolution within the wellbore and near wellbore regions. If the wellbore model is a CFD model, the creation and solution of the CFD model of the wellbore and surrounding near-wellbore region may be accomplished through the use of available codes and algorithms, such as FLUENT and CFX, for solving the Navier-Stokes equations, which may also include solver algorithms for coupled nonlinear equations. Furthermore, historical visualization and mesh creation may be mitigated by development of graphical user interfaces for pre-processing and computer-aided design (CAD) software that semi-automate mesh creation for CFD applications, such as GAMBIT by Fluent, Inc. Any number of these CAD/CFD software packages may be sufficient to replicate the inner instrumentation of the wellbore. Although computational power limitations are mitigated by ever-increasing advances in processor technology, numerical solution of the Navier-Stokes equations over the long length scales of completion intervals (e.g. hundreds to thousands of feet) may still involve access to significant computational resources. As a result, the resolution may be limited by the simulation time required for different scales of resolution.
Once constructed, the mesh may be populated with rock data, as shown in block 208. The rock data may be specified manually in modeling software or stored in a computer readable data file. Once the mesh is populated, a determination is made whether the rock data is consistent with other rock data, as shown in block 210. In populating the near-wellbore region of the wellbore model, input properties for the porous medium may be derived from geological characterization of log data and/or geological modeling data. In the absence of log data, well test data can also be used albeit with some loss of resolution. Regardless, the ability to populate each cell of the mesh with permeability, porosity, and conductivity data enables heterogeneities in the data and their impact on flow to be honored. If the rock data is not consistent, the mesh of the wellbore model may be revised based on the obtained rock data, as shown in block 212.
However, if the rock data is consistent, the boundary conditions may be obtained in block 214. For instance, the reservoir pay zones, which include hydrocarbons, may be defined in the near-wellbore region. Sandstone formations, shale layers may be explicitly modeled as impermeable flow boundaries (e.g. axial heat transfer is still allowed). Varying degrees of formation connectivity may be specified for carbonates. Fluid pressure, temperature, and compositional boundary conditions are specified for each pay zone at the radial extent of the near-wellbore region. These may be specified with simple linearized functions or more complicated depth-correlated relationships, depending on the reservoir. At block 216, a determination is made whether the boundary conditions are consistent. To determine if the boundary conditions are consistent, the wellbore model may be compared to reservoir simulator, modular dynamic tester data and/or well tests. For example, the pressure and temperature boundary conditions may be updated from a reservoir simulator to account for changes in production with time. Fluid compositions are varied to match the evolution in gas-oil ratio and water cut with production changes, such as coning or water flood encroachment. If the boundary conditions are not consistent, the boundary conditions may be revised based on the obtained boundary conditions, as shown in block 218.
If the boundary conditions are consistent, production scenarios with the wellbore model may be simulated, as shown in block 220. The simulations may generate various type curve analogues, which are shown in greater detail in
Beneficially, the use of rock data (from log data and/or geological modeling data) and reservoir data (from well test data, modular dynamic tester data, and/or reservoir simulator data) to constrain the input values into the wellbore model provides an advantage for applying the CFD model predictively through time. The reservoir simulator data provides an expectation of reservoir performance that can be correlated with well performance to support the model results. Preferably deployed as representative “type curves,” forward modeling forecasts of simulated production profiles for wellbore flow rates, pressures, velocities and thermal profiles provides operation personnel with an understanding of the predicted evolution of the production profile.
Further, by having a qualitative understanding of how the shape of the flow, pressure, velocity and/or thermal profile correlates with anticipated production scenarios, the potentially time-consuming process of post-measurement analysis of data may be circumvented. For instance, the inverse modeling approach first requires acquisition of data, then a certain amount of lag-time for interpretation. This lag-time may be further lengthened with iterative discussions between personnel operating and personnel servicing the well. The forward modeling of the present techniques may enhance operations by reducing response time in the field in comparison to other approaches, such as the inverse modeling approach. These changes may include determining that a change in the thermal profile is due to water onset and deciding to exercise a sliding sleeve to shut off water production from a specific zone. If, however, a measured pressure, thermal, velocity or flow profile is anticipated by a characteristic type curve analogue, even a rough onsite correlation may prove sufficient to progress an operating decision or response. Optimally, these two approaches complement each other to provide enhanced well performance with minimal downtime.
Moreover, the use of the present techniques may utilize CFD methods to provide detailed and qualitative data on velocity, flow rates, temperature and pressure gradients/profiles along the wellbore and downhole instrumentation. As noted above, CFD methods have typically been applied to short length scale studies, such as flow around a valve or foil, or attachment and detachment of turbulent eddies to walls because of computational expense. The use of CFD models with the long asymmetry of deep wells is generally not utilized because of the computational resources. However, coupling a porous media model of the reservoir with a wellbore model having Navier-Stokes equations provides heat transfer aspects of the wellbore.
As an example,
In this example, the thermal profile of the wellbore 306 is characterized by the fluid flow paths 318 and 320 being a countercurrent flow. These fluid flow paths 318 and 320 flow down the annulus formed between the casing string 308 and production tubing string 310 and up through openings in the production tubing string 310. As a result, radial heat is transferred across the production tubing string 310. A nodal approach is not amenable to analysis of the radial temperature distribution across the wellbore 306 because the localized heat transfer coefficient varies with flow rate, time and space. Thus, countercurrent heat exchange renders interpretation of a fiber optic temperature sensor strapped to the outside of the tubing extremely difficult.
Analysis of the above countercurrent flow may be further complicated if the flow profile (e.g. fluid flow paths 318 and 320) is impacted by valves, packers, or other equipment used to control or isolate production. Additional complexities result if the production fluid is commingled from multiple pay zones, or if multi-phasic interactions between gas, condensate, and water are present. Using a single phase mixture with a single set of averaged bulk properties is unsatisfactory if the fiber optic DTS system 316 is intended to thermally monitor for the onset of gas or water production. Thus, if a two-phase gas-water system is modeled, single phase gas simulations may be performed for the cases of “early” and “middle” life production when water is not present.
As another example, thermal profiles for a gas well may be simulated using a CFD model encompassing 1,800 feet of the wellbore associated with a completion interval. The completion may include downhole inflow valves and packers along with perforated intervals, which are incorporated into the mesh of the wellbore model. The perforated intervals may be modeled as an open hole basis for a first approximation. An assumed geothermal gradient may be used as the temperature boundary condition. Further, the solutions of the transport equations, such as Navier-Stokes equations, and energy equations may provide a quantitative characterization of the pressure, temperature, and velocity profiles within the wellbore. If a F-O DTS system is assumed to be strapped to the tubing of the completion, profile contours may be generated at different radial positions within the wellbore to examine the impact of fiber placement.
In addition to anticipating changes in the thermal profile over the entire completion interval or a portion of the completion interval, the simulations of the wellbore model may also indicate that water breakthrough in lower production intervals or zones may impact flow rates in upper production intervals or zones because of changes in fluid density and wellbore hydraulics. As a result, the simulation data may be used to enhance design of the completion, to optimize fiber placement outside the tubing, and/or to combine with sensory data in an operational well to proactively provide insight into potential problems.
Although the example above pertains specifically to analysis of fiber optic thermal data, the general applicability of the process may be applied to a variety of other approaches because transport equations are solved in addition to the energy equation. That is, the flow and pressure profiles may be determined, as well. Ultimately, as confidence in a match between simulation and measured data increases, the CFD simulation results may be used to allocate zonal contributions to flow. Additionally, the simulations may also be used to evaluate short-term flow conditions, such as those resulting from stimulation and cleanup operations.
Despite the complexity in creating CFD models to capture the underlying physics of the reservoir and wellbore, the results of the simulations may be deployed in an efficient manner. For example, the processes described above may be implemented in a modeling system. Different elements and components of the modeling system may be utilized to display and provide the results of the simulations (e.g. simulated production profiles). The modeling system may include a processor, one or more applications or set of computer readable instructions, data and memory. As an example, the modeling system may include computers, servers, databases and/or a combination of these types of systems, which may also include monitors, keyboards, mouses and other user interfaces for interacting with a user. The applications or set of computer readable instructions may include the modeling software or code configured to perform the methods described above, while the data may include reservoir data, sensory data, simulation data, or other information utilized in the methods described above. Of course, the memory may be any conventional type of computer readable storage used for storing applications, which may include hard disk drives, floppy disks, CD-ROMs and other optical media, magnetic tape, and the like.
Further, because the modeling system may be utilized to communicate with other devices, such as tools associated with the wellbore, the modeling system may include one or more communication components that exchange data with devices located in different geographic locations, such as different offices, buildings, cities, or countries. The network, which may include different devices, such as routers, switches, bridges, for example, may include one or more local area networks, wide area networks, server area networks, or metropolitan area networks, or combination of these different types of networks. The connectivity and use of the network by the devices and the modeling system is understood by those skilled in the art.
As an example of the use of the present techniques, a completion may be constructed and modeled for CFD simulations. These CFD simulations may relate to a completion that is designed, modeled, completed and installed for a well. As the well is operated to produce hydrocarbons, sensory data acquisition may be performed by a F-O sensing system that is installed partially within the wellbore. The F-O sensing system may be used to detect a change in the temperature, pressure, or flow profile, which may be due to onset of water production. From the sensory data, personnel may infer factors associated with the observed changes in sensory data by comparing and/or matching the measured temperature, pressure, or flow profile to one of those given in the wellbore model. From this comparison, the personnel may enact a response to the change in production by closing a valve, initiating a workover, reducing drawdown, and/or other suitable modifications to the well. Then, the personnel may perform a more rigorous post-response evaluation of the sensory data to affirm the understanding that the response or modification to the completion is appropriate. Feedback may also be provided to revise and further calibrate the CFD model to improve the forward modeling and prediction for real-time sensory data.
To operate the modeling system, an end user may run the modeling application via graphical user interfaces (GUIs), which are provided in various screen views discussed below in
On another tab 410, which is labeled “CFD Model,” fluid phases and properties are specified. Material properties of the hardware (e.g. equipment modeled as steel or other composite materials) and rock are also given because these material properties may impact axial and radial heat transfer of the CFD model. A user-specified formation description may also include rock properties (e.g., permeability, porosity) in the near wellbore region. These rock properties or rock data may also be validated or derived from log data and any geologic models of the formation, as noted above. Further, pay locations (e.g. production zones having hydrocarbons) are also specified as flow entry zones. When complete, these properties, which include fluid and reservoir properties, are formatted into a format utilized by the CFD model (e.g. wellbore model created in the CFD modeling system).
On yet another tab 412, which is labeled “Sensory Data,” F-O sensory data may displayed in this menu. This screen view may include some data filtering capabilities to process the data with respect to time, depth, and frequency. The filtered data and sensory data may be used for visual comparison against the CFD simulations shown in the Early, Middle, and Late life tabs, describe below.
For the tabs 414, 416 and 418 which are labeled “Early,” “Middle” and “Late,” the CFD simulations of the wellbore at different stages of well life are provided. Early life typically corresponds to reservoir pressures and relative permeabilities at initial completion, when the well is initially operated. Middle life may correspond to reservoir pressure and relative permeabilities after some prolonged period of production, and may reflect workover or changes to the well completion, such as abandoned/inactive perforations and depleted pay zones. Late life may reflect low reservoir pressures after prolonged field depletion, and the potential impact of water or gas breakthrough on the production profile of the well.
In each screen views associated with the tabs 416-418, contours of the pressure, flow rate, axial velocity, and temperature profiles as taken from the CFD simulation and plotted for visual reference. This data that may presented as type curve analogues in that, like type curves, provide graphical images of the data to assist in interpretation of wellbore behavior. An example of the “Late” tab 418 is shown in greater detail in the screen view 402 of
Further, it should be noted that while the present techniques described above provide examples of the analysis of fiber optic thermal sensory data, this sensory data is provided only as an example and does not limit the application of the present techniques. For example, forward modeling may be utilized to optimize placement of the fiber optic sensor by predicting flow profiles as a function of the radial position in the wellbore and to design complex completions by accounting for geometrical impacts to flow. Further, other examples may include simulating clean-up operations and/or stimulation operations.
Accordingly, holistic CFD modeling of the wellbore and near-wellbore region is useful for evaluating complex flow paths and heat transfer. Axial and radial dependencies can be probed to optimize wellbore completion design, and sensitivities may be run to forecast the impact of production changes and production profiles. The enhancements in both computational speed and grid resolution may improve application of CFD modeling to the design of new wells and production history matching for existing wells.
Further, for real-time asset management, the high data sampling frequency and automated response associated with the wellbore models may enhance evaluation of desired response to production targets. Deployment of CFD simulation results as type curve analogues may further enhance well operations by facilitating rapid responses to changes in well production and production profiles, while reducing post-measurement analysis time. This type of application may be particularly beneficial for well completions that utilize F-O sensors. As such, the above described processes provide a mechanism to evaluate pressure, temperature, and/or flow profiles within the wellbore to assist interpretation.
While the present techniques of the invention may be susceptible to various modifications and alternative forms, the exemplary embodiments discussed above have been shown by way of example. However, it should again be understood that the invention is not intended to be limited to the particular embodiments disclosed herein. Indeed, the present techniques of the invention are to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the following appended claims.
This application claims the benefit of U.S. Provisional Application No. 60/843,446, filed Sep. 8, 2006.
| Number | Date | Country | |
|---|---|---|---|
| 60843446 | Sep 2006 | US |
