Real-Time Monitoring of Acid Stimulation Utilizing a Fiber-Optic DTS System
Abstract: This paper discusses a unique system that combines a fiber-optic distributed temperature system (DTS) to measure the distributed temperature across the entire wellbore and a molecular telemetry transmission system that provides a single-point determination of bottomhole pressure. The system has been used to perform real-time downhole monitoring of multi-stage acid-stimulation treatments performed on wells that contain multiple non-isolated pay intervals. The fiber-optic for the DTS is contained inside a length… Show more
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Cited by 27 publications
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New methods have been advanced to process, manipulate, and utilize real-time data from permanent downhole monitoring (PDM) systems such as fiber optic (F-O) pressure, temperature, and flow sensors. These interpretative methods are centered upon detailed computational fluid dynamics (CFD) simulations of the wellbore and surrounding near-wellbore region. In addition to facilitating the solution of geometrically complex flow dependencies, the CFD approach accounts for the highly coupled chemical physics inherent in multi-phase systems, particularly gas compressibility effects that impact wellbore hydraulics and heat transfer which, in turn, govern the flow, pressure, and thermal profiles in the wellbore. In the near-wellbore region, true reservoir permeabilities and porosities are honored and used to populate the CFD model. By running the model for different anticipated production scenarios, a series of production "type curve" analogues are generated that can be used to "predict" the time-dependent flow, pressure, and thermal profiles recorded by a PDM, thereby assisting interpretation of the data. Introduction Continuing improvements in quality and reliability have led to increased applications of F-O technology for downhole sensing applications in the harsh pressure and temperature environments of the oil and gas industry1. Moving beyond routine monitoring of pressure or temperature, F-O sensors increasingly are being deployed as components of "smart" or "intelligent" well completions to enable response automation, or at the very least, aid in actuation. For example, a F-O distributed temperature sensing (DTS) system might be deployed in a well with commingled flow in order to detect changes in production (e.g., gas or water breakthrough), upon which a sliding sleeve might be exercised to shut off production from an offending zone. Despite increased installations of fiber optic instrumentation as a key component of intended response systems, automated remote administration of "smart" wells is still plagued by several difficulties. Indeed, far from being automated, manual intervention and decision for actuation of "smart" features are still required at present. These hurdles stem in large part from limitations in capabilities to process and interpret sensory data; although data may be recorded in real-time, they are still processed as-needed on an indeterministic time basis. Traditional reservoir and well surveillance methods rely upon surface production data and infrequent production logging trips typically performed only during shut-in or workover and on a well-by-well basis. In contrast, a burgeoning array of fiber optic (F-O) systems promises to provide continuous acquisition of sensory data during both "normal" flowing operation as well as shut-in. However, conventional PLT analysis methods rely on multiple types of log data -- inferring flow phenomena from a combination of pressure, spinner, and temperature logs2–4. Consideration of all the data can be time consuming and laborious, typically introducing (and requiring) significant subjectivity in the analysis. This is especially difficult to extend to real-time F-O data, particularly if only one property is measured (e.g., temperature but not pressure or rate). Although F-O technology continues to advance rapidly, temperature sensing is still the primary and often single measurement recorded by F-O systems. An important question that arises, then, is how to interpret F-O thermal data and infer downhole flow phenomena. Inherently related is the need to predict the thermal profile under variable producing conditions. Many efforts to solve this problem have focused on using multi-nodal models5–9. At each node (corresponding to a different depth), a description of the macro-structure of the well and the surrounding near-wellbore region is specified. Rock properties, fluid pressures, fluid temperatures, wellbore pressures, and wellbore diameters are typically specified. Pipe-flow correlations and simple energy balances may be used between nodes to assemble the flow behavior at each node into a contiguous wellbore flow and thermal profile. The input parameters are adjusted until a match with the measured thermal profile is obtained. While the approach has the advantage of simplicity and ease of calculation, there are several inherent assumptions that render these models inapplicable for modeling more complex wellbores.
New methods have been advanced to process, manipulate, and utilize real-time data from permanent downhole monitoring (PDM) systems such as fiber optic (F-O) pressure, temperature, and flow sensors. These interpretative methods are centered upon detailed computational fluid dynamics (CFD) simulations of the wellbore and surrounding near-wellbore region. In addition to facilitating the solution of geometrically complex flow dependencies, the CFD approach accounts for the highly coupled chemical physics inherent in multi-phase systems, particularly gas compressibility effects that impact wellbore hydraulics and heat transfer which, in turn, govern the flow, pressure, and thermal profiles in the wellbore. In the near-wellbore region, true reservoir permeabilities and porosities are honored and used to populate the CFD model. By running the model for different anticipated production scenarios, a series of production "type curve" analogues are generated that can be used to "predict" the time-dependent flow, pressure, and thermal profiles recorded by a PDM, thereby assisting interpretation of the data. Introduction Continuing improvements in quality and reliability have led to increased applications of F-O technology for downhole sensing applications in the harsh pressure and temperature environments of the oil and gas industry1. Moving beyond routine monitoring of pressure or temperature, F-O sensors increasingly are being deployed as components of "smart" or "intelligent" well completions to enable response automation, or at the very least, aid in actuation. For example, a F-O distributed temperature sensing (DTS) system might be deployed in a well with commingled flow in order to detect changes in production (e.g., gas or water breakthrough), upon which a sliding sleeve might be exercised to shut off production from an offending zone. Despite increased installations of fiber optic instrumentation as a key component of intended response systems, automated remote administration of "smart" wells is still plagued by several difficulties. Indeed, far from being automated, manual intervention and decision for actuation of "smart" features are still required at present. These hurdles stem in large part from limitations in capabilities to process and interpret sensory data; although data may be recorded in real-time, they are still processed as-needed on an indeterministic time basis. Traditional reservoir and well surveillance methods rely upon surface production data and infrequent production logging trips typically performed only during shut-in or workover and on a well-by-well basis. In contrast, a burgeoning array of fiber optic (F-O) systems promises to provide continuous acquisition of sensory data during both "normal" flowing operation as well as shut-in. However, conventional PLT analysis methods rely on multiple types of log data -- inferring flow phenomena from a combination of pressure, spinner, and temperature logs2–4. Consideration of all the data can be time consuming and laborious, typically introducing (and requiring) significant subjectivity in the analysis. This is especially difficult to extend to real-time F-O data, particularly if only one property is measured (e.g., temperature but not pressure or rate). Although F-O technology continues to advance rapidly, temperature sensing is still the primary and often single measurement recorded by F-O systems. An important question that arises, then, is how to interpret F-O thermal data and infer downhole flow phenomena. Inherently related is the need to predict the thermal profile under variable producing conditions. Many efforts to solve this problem have focused on using multi-nodal models5–9. At each node (corresponding to a different depth), a description of the macro-structure of the well and the surrounding near-wellbore region is specified. Rock properties, fluid pressures, fluid temperatures, wellbore pressures, and wellbore diameters are typically specified. Pipe-flow correlations and simple energy balances may be used between nodes to assemble the flow behavior at each node into a contiguous wellbore flow and thermal profile. The input parameters are adjusted until a match with the measured thermal profile is obtained. While the approach has the advantage of simplicity and ease of calculation, there are several inherent assumptions that render these models inapplicable for modeling more complex wellbores.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIntelligent completion technology, namely permanent downhole monitoring and flow control devices that allow continuous monitoring and control of well flowing system, has become more and more popular today in oil and gas wells to assist well and reservoir performance management. The applications of intelligent well today are much beyond the original vision of the technology that measurement and control become possible without intervention and their associated costs by installing permanent downhole monitoring and flow control devices connected to the surface. In addition to these advantages, the technology has been used to optimize gas lift and ESP systems, sand control, formation damage control, water breakthrough, gas coning problems, and crossflow eliminations.This paper summarizes the current available intelligent technology, describes the production optimization workflow process from data acquisition to optimization and control, and focuses on a discussion of the effective approach of deploying the technology for different types of well completions and drainage volume characteristics. The majority of previous publications on intelligent completion technology have been in two areas: (1) identification of well production problems; and (2) control of the well flowing system to meet operational constraints and limits and/or improve existing well performance to higher levels. Many field examples showed that the monitoring function and the control function of intelligent completions have been applied independently. The efficiency of the integrated system could be greatly improved if monitoring and control systems are interactively integrated. Monitoring can directly guide control schemes and validate the results of their executions. The examples in the paper show that with simple interpretation of monitored data, control of well operation can be less time consuming and more effective. Likewise, downhole monitoring needs to be used with control systems so that the problems identified by monitoring systems can be treated accordingly. The approaches presented in the paper will help to maximize the value of intelligent completion.
Distributed temperature sensing (DTS) is a valuable tool used to understand the dynamics of oil and gas production and injection rates. This is achieved by monitoring the temperature variations caused by flow or injection rates at the reservoir entry points. Although most documentation to date has been limited to the discussions resulting from the examination of steady-state temperature profile behavior, significantly more knowledge and better assessment of "what's happening" in wells can be gained when sequences of complete wellbore temperature profiles are analyzed during rate-related or other induced thermal transient events, such as seen in pressure transient analysis. DTS technology allows complete wellbore temperature profiles to be obtained in a short period of time without the need for wire line. Discrete temperature measurement information can be obtained along the entire wellbore using optical fiber and a laser source/detector to repeatedly pulse light down the fiber and detect the back-scattered light from every depth. The back-scattered light is the result of the interaction of each laser pulse with the fiber molecules and is proportional to the temperature of the glass at a given depth. Intensity responses of repeated light pulses are averaged to obtain acceptable temperature resolution and can be improved by increasing acquisition time. This paper discusses the value added from transient temperature analysis of DTS profiles for oil and gas wells under production, injection, and treatment conditions. Data taken with DTS technology and thermal simulators from several actual field cases will be provided to demonstrate how these techniques can improve analytical capabilities and how a temperature change has a direct relationship to production/injection layer contributions, reservoir flow properties, tubular leaks, and steam breakthrough locations. The field cases also will demonstrate how a DTS-acquired transient temperature profile analysis can be applied. In addition to the improved data acquisition capabilities, the paper will also provide the cost and safety advantages gained by using DTS transient analysis techniques. Introduction The use of temperature readings to monitor producing wells was initiated over 70 years ago and has been used on numerous wells since that time. An understanding of how fluids flowing inside an oil or gas well are influenced by the geothermal gradient and internal Joule Thompson Effect (JTE) is essential to using any wellbore temperature readings in a meaningful way. Geothermal temperature gradient is the natural increase in temperature that occurs with increased depth into the Earth. This temperature change results from the geothermal heat, which has been generated at the earth's center and then conducted to the formation by rock, rock porosity, and pore space. It usually occurs in gradations varying between 0.7ºF/100 ft to 2.5ºF/100 ft. It is this gradient that provides an initial temperature profile for DTS flow-profiling interpretation. When flow from a fluid or gas enters the wellbore, rapid changes occur. This convection influences the wellbore, and depending upon the composition of the fluid, the temperature change may be positive or negative. In many cases, gasses will cool and liquids will heat when they experience pressure drop. Therefore, any temperature deviations from the geothermal gradient indicate flow entries, completion effects, near wellbore interference, or other events. The JTE is a cooling of produced gas or a warming of produced liquids due to pressure drop during flow through formation, perforations, and wellbore. In combination, geothermal gradient and JTE create a time-dependent profile that can be analyzed todetermine flow dynamics in oil and gas wells, andto evaluate water injection profiles and the effectiveness of stimulation jobs, cross-flow between zones, and flow outside the casing. Distributed Temperature Sensing As more wells are being completed to produce commingled reservoirs, better methods are being developed to determine zonal flow contributions and breakthrough location of unwanted fluids in order to optimize recovery and drive down production costs. Additionally, production optimization with high oil and gas prices requires continuous information on the production performance of each layer to design and plan prevention and remedial actions. DTS Transient Analysis is one of the methods that has been developed to enable real-time acquisition of data that can accomplish the above needs.
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