DK201700702A1 - PROCEDURE FOR INTERPRETING DISTRIBUTED TEMPERATURE SENSORS DURING FIRE TREATMENT - Google Patents

Abstract

A method for determining flow distribution in a formation with a well formed therein comprises the steps of placing a sensor in the well, wherein the sensor generates a feedback signal representing at least one of the temperature and pressure measured by the sensor, injecting a fluid into the well and in at least a portion of the formation near the sensor, to close the well for a predetermined closure period, to produce a simulated model representing at least one of simulated temperature characteristics and simulated pressure characteristics of the formation during the closure period, to produce a data model representing at least one of the current temperature characteristics and current pressure characteristics of the formation during the closing period when the data model is derived from the feedback signal, comparing the data model with the simulated model and adjusting the parameters of the simulated model to substantially match the data model.

Description

(12) ϋθ> DENMARK

( ^1θ ) DK 2017 00702 A1

PATENT APPLICATION

Patent and

Trademark Office (51) Int.CI: E21 B 47/10 (2012.01) (21) Application number: PA 2017 00702 (22) Filing date: 2017-12-11 (24) Running day: 2015-05-11 (41) Aim. available: 2017-12-11 (62) Stock Application No: PA 2012 00798 (71) Applicant: Schlumberger Technology BV, Parkstraat 83-89, NL-2514 The Hague, The Netherlands (72) Inventor: Xiaowei Weng, 22318 David's Crest Court, 77450 Katy, Texas, USA Doug Pipchuk, 86, Tuscany Glen Place, T3L-ZA Calgary, Alberta, Canada Rex Burgos, 21314 Shawnee Park Dr., 77469 Richmond, Texas, USA Phillippe MJ Tardy, 7 Quai Adrian, F-03800 Gannat , France (74) Plenipotentiary: Larsen & Birkeholm A / S Scandinavian Patent Office, Banegårdsplatsen 1.11570 Copenhagen V, Denmark (54) Name: PROCEDURE FOR INTERPRETING DISTRIBUTED TEMPERATURE SENSORS DURING FIRE TREATMENT (57)

A method for determining flow distribution in a formation with a well formed therein includes the steps of placing a sensor in the well, wherein the sensor generates a feedback signal representing at least one of the temperature and pressure measured by the sensor, injecting a fluid into the well and in at least a portion of the formation near the sensor, to close the well for a predetermined closure period, to produce a simulated model representing at least one of simulated temperature characteristics and simulated pressure characteristics of the formation during the closure period, to produce a data model representing at least one of the current temperature characteristics and current pressure characteristics of the formation during the closing period when the data model is derived from the feedback signal, comparing the data model with the simulated model and adjusting the parameters of the simulated model to substantially match the data model.

DK 2017 00702 A1

DK 2017 00702 A1

PROCEDURE FOR INTERPRETING DISTRIBUTED TEMPERATURE SENSORS UNDER FIRE TREATMENT

BACKGROUND OF THE INVENTION

The disclosures in this section provide background information on the present invention and do not necessarily constitute prior art.

The present invention relates generally to drilling treatment and development of a reservoir and, in particular, to a method for determining flow distribution in a well during treatment.

Hydrofracturing, matrix acid treatment and other forms of stimulation treatments are routinely performed in oil and gas wells to increase coal hydrogen production. The wells that are stimulated often include a large perforated casing section or one. open borehole with significant variation in petrophysical and mechanical rock properties. As a result, a treatment fluid pumped into the well may not flow to all hydrocarbon-bearing layers that need stimulation. To achieve effective stimulation, the treatments often involve the use of conductive agents in the treatment fluid, such as chemically-owned particulate matter tail, to help reduce the flow to the more permeable layers that no longer need stimulation and to increase the flow to the lower permeability layers.

One method includes performing the treatment via a cyan tube which can be placed in the well to direct the fluid immediately close to layers to be plugged by pumping a conductive agent and close to layers in need of stimulation by pumping stimulating fluid. However, the tubing technique requires an operator at which layers need treatment with a diversion agent and which layers need treatment with a pacing fluid. In a well with long perforated or open intervals at extremes

DK 2017 00702 A1 disparate and unknown bedrock properties typical of horizontal wells require efficient treatment knowledge of the flow distribution in the treated interval.

Conventional flow measurement in a well is typically done by production monitoring using a flow meter to measure the hydrocarbon production rate or the injection rate in the well as a function of depth. On the basis of the logged well flow rate, the output from or the injection rate in each formation depth interval is determined on the basis of a measured axial flow rate over that interval. Traditional flow measurement is valid as long as the flow distribution in the well does not change over the time period when logging is performed.

However, during a stimulation treatment, the flow distribution in a well may change rapidly either due to stimulation of the formation layers to increase their flow capacity or temporary reduction of flow capacity as a result of diversion agents. To determine the efficiency of stimulation or diversion in the well, an instantaneous measurement is provided that provides a snapshot of the flow distribution in a well. Unfortunately, only a few such nods are available.

One technique for substantially instantaneous measurement is the fiber optically distributed temperature sensing (DTS) technology. The DTS typically includes an optical fiber located in the well (e.g., via a permanent optical fiber line cemented into the casing, an optical fiber line deployed using a tubing tube, or a siickline unit). The optical fiber measures a temperature distribution along a length thereof. based on an optical time domain (eg optical time domain reflectometry (OTDR) widely used in the telecommunications industry).

An advantage of the DTS technology is the ability to file within a short time interval

DK 2017 00702 A1 acquire the temperature distribution along the well without having to move the sensor as with traditional well logging, which can be time consuming. The DTS technology effectively provides a 'snapshot' of the temperature profile in the well. The DTS technology has been used to measure temperature changes in a well following a stimulation injection, from which qualitative estimation of a fluid distribution of an Injected fluid can be estimated. The discharge of flow distribution is typically based on the extent of warm back during a closing period following injection of a fluid into the well and surrounding portions of the formation. The injected fluid is typically colder than the format temperature, and a formation layer that receives a larger fluid flow rate during injection has a longer "reheat time" compared to a layer or zone of formation that receives a relatively less embarrassed fluid.

As a non-limiting example, FIG. 1 is a graphical representation 2 of a number of simulated temperature profiles 4 of a layered formation 6 during a reheat period of six hours according to the prior art. As shown, the X-axis 8 in the graph 2 represents temperature in Kelvin (K), and the Y-axis 9 in the graph 2 represents a depth in meters (m) measured from a predetermined surface level. As shown, a permeability of each layer of the layered formation 6 is estimated in miliidarcy units (mD). The 6 layers of formation with a relatively high permeability receive more fluid during injection, and a time period for '' reheating '' is relatively long (ie, after a given time period, the temperature change is less than a temperature change of the layers with a lower permeability). The 8 layers of the formation with a relatively low permeability receive less fluid during injection and a time period for reheating ”is relatively short (i.e., after a given period of time, a temperature change is greater than a temperature change of the layers with a higher permeability).

By obtaining and analyzing several DTS temperature traces during the batch 2017 00702 A1 period, one can determine the injection rate distribution among different formation layers. However, current DTS thickening techniques and methods are based on visualization of the temperature change in the DTS catalog and are at best qualitative in nature. The present interpretation methods are further complicated by applications where a reactive fluid, such as acid, is pumped into the well where the reactive fluid reacts with the formation rock and can affect a temperature of the formation leading to misinterpretation. To achieve effective stimulation, more accurate DTS interpretation methods are required to help technicians determine the flow distribution in the well and make adjustments to the treatment accordingly.

The invention proposes several methods for quantitatively determining the flow distribution from DTS measurement. These methods are described in more detail below.

An embodiment of a method for determining flow distribution in a formation with a well formed therein comprises the steps of: placing a sensor in the well, wherein the sensor produces a feedback signal representing at least one of a temperature or pressure measured by the sensor; injecting a fluid into the well and at least a portion of the formation near the sensor; closing the well for a predetermined closing period; to produce a simulated model representing at least one of simulated temperature characteristics and simulated pressure characteristics of the formation during the closure period; generating a data model representing at least one of the current temperature characteristics and current pressure characteristics of the formation during the closing period, wherein the data model is derived from the feedback signal; comparing the data model with the simulated model; and adjusting the parameters of the simulated model to substantially match the data model.

DK 2017 00702 A1

In one embodiment, a method for determining flow distribution in a formation with a well formed therein comprises the steps of: placing a sensor in the well, wherein the sensor provides a substantially continuous temperature monitoring along a predetermined range, and the sensor generates a feedback signal which represents the temperature measured by the sensor; injecting a fluid into the well I at least a portion of the formation near the interval: closing the well for a predetermined closure period: producing a simulated model representing simulated thermal characteristics of at least one subsection of the interval during the closure period; generating a data model representing current thermal characteristics for at least the subsection of the interval where the data model is derived from the feedback signal; to compare the data model with the simulated model: and to adjust the parameters of the simulated model to substantially match the data model.

In one embodiment, a method of determining flow distribution in a formation with a well formed therein comprises the steps of: a) placing a distributed temperature sensor on a fiber extending along an interval of the well, the distributed temperature sensor providing substantially continuous temperature monitoring along the interval, and wherein the sensor produces a feedback signal representing temperature measured by the sensor; b) injecting a fluid into the well and at least a portion of the formation near the interval; c) closing the well for a predetermined closing period; d) generating a simulated model representing simulated thermal characteristics of a subsection of the Interval during the closing period; e) generating a data model representing current thermal characteristics of the interval sub-section, wherein the data model is derived from the feedback signal; f) comparing the data model with the simulated model; g) adjusting the parameters of the simulated model to substantially match the data model; and h) repeating steps d) to g) for each of the number of subsections defining the interval in the well to produce a flow profile that reoradDK 2017 00702 A1 centers the entire interval.

Brief description of the drawings

These and other features and advantages of the present invention will be better understood by reference to the following detailed description when read in conjunction with the accompanying drawings, in which:

FIG. 1 is a graphical representation of a number of simulated temperature profiles of a layered formation during a reheat period of six hours according to the prior art:

Fig. 2 is a schematic diagram of an embodiment of a well treatment system;

FIG. 3 is a graph showing an embodiment of a simulated temperature profile and a current measured temperature profile of a well treatment at a first time period;

FIG. 4 is a graph showing a simulated temperature profile and a current measured temperature profile of the one shown in FIG. 3 well treatment taken at a different time period;

FIG. 5 is a schematic view showing an embodiment of a plurality of measured temperature profiles, each of the measured temperature profiles being taken at a discrete time period during a closure period for a well treatment;

FIG. 6 is a graph of temperature versus time for a subinterviews of the one shown in FIG. 5;

FIG. 7 is a graphical representation of an interpreted flow profile of the one shown in FIG. 5

DK 2017 00702 A1 *> · i

drilling;

FIG. 8A is a graphical representation of a malted temperature profile of the layered formation of FIG. 1;

FIG. 8B is a graphical representation of an interpreted temperature of a fluid prior to injection into the layered formation of FIG. 1;

FIG. 8C is a graphical representation of an interpreted temperature of the layered formation of FIG. 1 prior to an injection procedure; and FIG. 8D is a graphical representation of an interpreted volume of fluid injected into the aforementioned formation of FIG. 1 at various depths of this one.

Detailed description of the invention

Referring now to FIG. 2, there is shown an embodiment of a well treatment system according to the invention, generally indicated by 10. As shown, system 10 includes a fluid injector (fluid injectors) 12, a sensor 14 and a processor 16. It will be understood that the system 10 may include additional components. .

The fluid injector 12 is typically a tubing tube which can be placed in a well formed in a formation to selectively direct a fluid to a particular depth or layer in the formation. The fluid injector 12 may e.g. directing a diverter immediately near a layer of the formation to plug the layer and minimize the permeability of the layer. As a further example, the fluid injector 12 may direct a pacing fluid near a pacing layer. It will be appreciated that other means can be used for routing fluids to various depths and layers, as will be understood by one of skill in the art of well treatment. Furthermore, it will be understood that treatment fluids, derivatives and

DK 2017 00702 A1 stimulation fluids for the treatment of different layers in a special formation.

The sensor 14 is typically of distributed temperature sensing technology (DTS) and includes an optical fiber 18 disposed in the well (e.g. via a permanent optical fiber line cemented into the casing, an optical fiber line deployed using a tubing tube , or a slickline device). The optical fiber 18 measures the temperature distribution along a length thereof on the basis of optical time domain (e.g. optical time domain reflectometry) in certain embodiments, the sensor 14 includes a pressure measuring device 19 for measuring a pressure distribution in the well and the surrounding formation. In certain embodiments, the sensor 14 corresponds to the DTS technology disclosed in U.S. Patent No. 7,055,604 B2.

The processor 16 is in data communication with the sensor 14 for receiving data15 signals (e.g., a feedback signal) therefrom and analyzing the signals on the basis of e.g. a predetermined algorithm, mathematical process or equation. As shown in FIG. 2, the processor 16 analyzes and evaluates data received on the basis of an instruction set 20. The instruction set 20, which may be executed in a computer-readable medium, includes processor-derived instructions for configuring the processor 16 to perform a variety of tasks and calculations. As a non-limiting example, instruction set 20 may include a comprehensive series of equations controlling a physical fluid flow phenomenon in the formation, a fluid flow in the well, a fluid / formation interaction (e.g., rock) in the case of a reactive pacing fluid, a fluid flow a fracture and its deformation in the case of hydrofracturing and a heat transfer in the well and in the formation. As a further non-limiting example, instruction set 20 includes a comprehensive numerical model for carboxylic acid treatment, as described in Society of Petroleum Engineers (SPE) Paper 107854 entitled An Experiment30 tally Validated Wormhole Mode for Self-Diverting and Conventional Acids in Carbonate Rocks Under Radial Flow Conditions by P. Tardy, E3. Lecerf and

DK 2017 00702 A1

Y. Christanti. Man we! understand that any equations can be used to model a fluid flow and heat transfer in the well and the adjacent formation, as will be understood by one of skill in the art of well treatment. It will further be understood that processor 16 can perform a variety of functions such as e.g. controlling various settings of the sensor 14 and the fluid injector 12.

As a non-limiting example, processor 16 includes a storage device 22. The storage device 22 may be a single storage device or may be multiple storage devices. The storage device 22 may furthermore be a solid state storage system, a magnetic storage system, an optical storage system or any other suitable storage system or device. It will be appreciated that the storage device 22 is adapted to store the instruction set 20. In certain embodiments, data retrieved from the sensor 14 is stored in the storage device 22, such as e.g. a temperature measurement and a pressure measurement and a history of previous measurements and calculations. Other data and information may be stored in the storage device 22, such as e.g. the parameters calculated by the processor 16, and a database of petrophysical and mechanical properties of various formations. It is further understood that certain known parameters and numerical models for various formations and fluids can be stored in the storage device and retrieved by the processor 16.

As a further non-limiting example, the processor 16 includes a programmable device or component 24. It will be appreciated that the programmable device or component 24 may be associated with any other component of the system 10, such as e.g. fluid injector 12 and sensor 14. In certain embodiments, programmable component 24 is adapted to control and manage the processing functions of processor 16. Specifically, the programmable component 24 is arranged to control the analysis of the data signals (e.g., the feedback signal generated by the sensor 14) received by the processor 16. It will be stated forDK 2017 00702 A1 that the programmable component 24 can be adapted to storing data and information in the storage device 22 and retrieving data and information from the storage device 22.

In certain embodiments, a user interface 26 is either directly or indirectly associated with at least one of the fluid injector 12, the sensor 14 and the processor 16 such that it is possible for the user to selectively interact with it. As a non-limiting example, the user interface 26 is a human-machine interface that allows a user to selectively and manually modify the parameters of a computer model generated by the processor 16.

In use, a fluid is injected into a formation (e.g., a stratified rock formation) to remove or circumvent a nearby well injury that may be caused by drilling mud invasion or other mechanisms, or to create a hydraulic fracture extending hundreds of meters into the formation to increase the well flow capacity. Typically, a temperature of the injected fluid is lower than a temperature of each of the formation layers. During the entire injection period, the colder fluid removes thermal energy from the well and the surrounding areas of the formation. Typically, the greater the inflow velocity in the formation, the greater the volume of injected fluid (i.e., its depth of penetration into the formation) and the greater the cooled area. In the case of hydrofracturing, the injected fluid enters the produced hydraulic fracture and cools the area near the fracture surface. When pumping ceases, the heat conduit from the reservoir gradually heats the fluid in the well. If a portion of the formation does not receive inflow during Injection, it will be reheated faster due to a less cooled area, while the formation receiving larger inflow will be reheated more slowly.

FIG. 3 shows a graphical image 28 showing a simulated room temperature profile

DK 2017 00702 A1 and a current ground temperature profile 32 for a well treatment (eg an acid treatment in a horizontal well in a carbonate feed) at a first time period. As a non-limiting example, the first time period immediately after the closure procedure (i.e., cessation of the well treatment and cessation of fluid flow into the formation or the like) has been initiated. As shown, the X-axis 34 in the graph 28 represents temperature in degrees centigrade (° C), and the Y-axis 36 in the graph 28 represents a depth ar formation in meters (m), measured from a predetermined surface level. In certain embodiments, the simulated temperature profile 30 is based on at least one of the estimated petrophysical, mechanical, and thermal properties of the formation, thermal properties (e.g., thermal conductivity and heat capacity) of the injected fluid, and violet properties of the injected fluid and formation. As a non-limiting example, the estimated properties of the formation can be provided manually by a user. As a further non-limiting example, the estimated properties can be generated by the processor 16 on the basis of stored data and known or estimated formation information. It will be appreciated that a simulated pressure profile (not shown) can be generated by the processor 16 based on the estimated properties of the formation. The current measured temperature profile 32 is based on data acquired by the sensor 14 during closure after a period of fluid injection.

FIG. 4 Illustrates a graphical representation 38 showing a simulated temperature profile 40 and a current measured temperature profile 42 for a well treatment (e.g., an acid treatment in a horizontal well in a carbonate feed) at a different time period. As a non-limiting example, the second time period is approximately four hours after the first time period. It will be understood that any period of time may be used. As shown, the X-axis 44 in the graph 38 represents temperature in degrees centigrade (° C), and the Y-axis 46 in the graph 38 represents a depth of the formation in meters (m), measured from a predetermined surface level. By

In certain embodiments, the simulated temperature profile 40 is based on at least one of the estimated petrophysical, mechanical, and thermal properties of the formation, thermal properties (e.g., thermal conductivity and heat capacity) of the injection fluid, and violet properties of the injection fluid and formation. As a Non-limiting example, the estimated properties of the formation can be provided manually by a user. As a further non-limiting example, the estimated properties can be generated by the processor 16 on the basis of stored data and known information about a location of the formation. It will be appreciated that a simulated pressure profile (not shown) can be generated by the processor 16 based on the estimated properties of the formation. The actual measured temperature 32 is based on data acquired by the sensor 14 during the closure after the period of fluid injection.

As an illustrative example, a layer of the formation at a particular depth is estimated to have a first set of petrophysical properties with a particular permeability, and the simulated temperature profiles 30, 40 are generated based on a model of the formation's estimated properties (i.e., forward model simulation). however, current measured temperatures 32, 42 do not align with the simulated temperature profiles 30, 40, the user modifies at least one of the estimated properties of the formation and parameters of the model used to generate the simulated temperature profiles 30, 40 so that the simulated temperature profiles 30, 40 substantially match the current measured temperatures 32, 42. The model used to generate the simulated temperature profiles 30, 40 is updated in this way on the basis of the current measurements of the sensor 14. It will be appreciated that the updated model can be used as a base model for future applications on the same or similar formation. Furthermore, it will be understood that the flow distribution in the formation can be quantitatively determined from the updated model.

FIG.

5-7 illustrate a method for determining a flow distribution i

DK 2017 00702 A1 a formation according to another embodiment of the present invention. As a non-limiting example, the flow distribution in the formation is determined using a numerical inversion algorithm. As a further non-limiting example, a simulated temperature curve (i.e., simulated model) is generated for a given violet velocity. an injection fluid temperature and an initial formation temperature for any given depth by solving a numerical finite difference transfer model for modeling a convective flow of a cooling fluid in a permeable formation, as will be understood by one skilled in the art.

FIG. 5 Illustrates a schematic view 47 showing a plurality of measured temperature profiles 48, each of the measured temperature profiles 48 being taken at a discrete time period ti, t2,13, 14 during the closing period after an injection. As shown, the X-axis 49 in the graph 47 represents temperature, and the Y-axis 50 in the graph 47 represents a depth of the formation measured from a predetermined surface level. In certain embodiments, a well interval of interest 52 is divided into a number of subsections 54 of predetermined cross-sectional length. For each of the subsect 54, the measured temperature profile is plotted for time as shown in FIG. 6th

FIG. 6 specifically illustrates a graphical representation 56 showing a plurality of discrete temperature measurements 58 of the sensor 14, each of the measurements being taken at discrete time periods 10, 12, 13, 14, respectively. A theoretical temperature curve 60 (i.e., simulated model) is modeled to cut the discrete measurements 58. As shown, the X-axis 62 in the graphical representation 56 represents time and the Yaksen 64 in the graphical representation 56 represents a temperature.

In particular, the temperature measurements 58 for a particular one of the subsections 54 are compared with the theoretical temperature curve 60. In certain embodiments, a numerical optimization algorithm is used on the measured temperature measurements 58 and the theoretical temperature curve 60 to find a "best"

DK 2017 00702 A1 match ”and to minimize a difference in error between them. The numerical optimization algorithm is e.g. a sum of the squares of the difference between the data values of the temperature measurements 58 and corresponding points along the theoretical temperature curve 60. A number of input parameters for generating the theoretical temperature curve 60 (ie simulated model) are modified as an additional example to obtain a best match between the theoretical In some embodiments, the input parameters include e.g. a flow rate during injection, a fluid temperature, an initial formation temperature, and a flow rate during closure. It will be appreciated that a number of discrete combinations of the input parameters can produce the same theoretical temperature curve. As such, an average of the Input parameters can be used for the fit procedure between the theoretical tern temperature curve 60 and the temperature measurements 58.

When the theoretical temperature curve 60 is adapted to the temperature measurements 58, the modified input parameters of the theoretical temperature curve 60 represent the average flow rate, the fluid temperature and the initial formation temperature. A flow profile (i.e., the profile of the fluid volume injected during the injection period) can be obtained by repeating the above-described comparison and fitting process for the rest of the subsections 54. By way of example, illustrate in FIG. 7, the X-axis 67 of the graphic image 65 represents a volume of injected fluid, and the Y-axis 68 of the graphic image 65 represents a depth of the formation measured from a predetermined surface level.

Figs. 8A-8D illustrate an example of applying a numerical inversion algorithm to the synthetic data generated by a numerical simulator as shown in Fig. 1. Figs. 8A illustrates, in particular, a graph 69 showing a first measured temperature profile 70 taken at a first time period and a second measured temperature profile 72 taken at a second time period. As

DK 2017 00702 A1 non-limiting example is the first time period immediately after a closure procedure is initiated and the second time period is six hours after the first time period. It will be understood that any period of time may be used. As shown, the X-axis 74 in the graph 69 represents temperature in Kelvin (K), and the Y-axis 76 in the graph 69 represents a depth of the formation in meters (m), measured from a predetermined emo ve rf let eni ve and u.

In operation, a theoretical temperature curve (i.e., simulated model) is generated on the basis of a numerical finite differential heat transfer mode for modeling a convective flow of a cooling fluid into a permeable formation, as will be understood by one skilled in the art. As a non-limiting example, the heat transfer model input parameters include estimates of a flow rate during injection, a fluid temperature, an initial formation temperature, and a flow rate during closure. The temperature profiles 70, 72 are compared with the theoretical curve in a manner similar to that shown in FIG. 6, In certain embodiments, a numerical optimization algorithm is used on the measured temperature profiles 70, 72 and the theoretical curve to automatically find a '' best match '' and to minimize an error difference between the temperature profiles 70, 72 and the theoretical curve. As a Non-limiting example, the input parameters are modified so that the resulting theoretical temperature curve fits substantially with an appropriate one of the temperature profiles 70, 72, when the theoretical curve is fitted ”to the appropriate one of the temperature profiles.

70, 72, the modified input parameters of the theoretical curve represent the average flow rate, fluid temperature and initial formation temperature as shown in FIG. 8B, 8C; and 8D. It is understood that a number of discrete combinations ar the input parameters can produce the same theoretical temperature curve. As such, an average of the input parameters of the adaptation procedure between the theoretical temperature curve and the temperature profiles 70, 72 can be used.

DK 2017 00702 A1

Specifically, FIG. 8B is a graphical representation 78 showing an inverted (i.e. interpreted from the inversion algorithm) temperature curve 80 for the injected fluid. As shown, the X-axis 82 in the graph 78 represents temperature in Kelvin (K) and the Y-axis 84 in the graph 78 represents a depth of the formation in meters (m), measured from a predetermined surface level. FIG. 8C is a graphical representation 86 showing an average temperature profile 88 of the formation prior to receiving the injected fluid (with a standard deviation shown as a shaded area). As shown, the X-axis 90 in the graphical representation 86 represents temperature in Kelvin (K), and the Y-axis 92 in the graphical representation 86 represents a depth of the formation in meters (m), measured from a predetermined surface level. FIG. 8D is a graphical representation 94 showing a simulated mean volume curve 96 for the injected fluid (with a standard deviation shown as a shaded area). As shown, the X-axis 98 in the graph depicts 94 volumes in cubic meters of fluid injected into one meter of the formation (mYm), and the Y-axis 100 in the graphical representation 94 represents a depth of the formation I meter (m), measured from a predetermined surface level. As such, the temperature curve 80, the temperature profile 88, and the volume curve 96 provide an accurate flow distribution profile for the formation that can be used in subsequent processing processes.

In one embodiment, temperature data measured by sensor 14 are compared against a set of pre-generated theoretical curves called type curves. The type curves are typically in dimensionless form, where dimensionless variables are expressed as a combination of physical variables. The temperature data received from the sensor 14 is pretreated to be displayed in dimensionless form and to be above the theoretical type curves. By shifting the measured temperature data to. finding a type curve with best match one can determine the physical parameters corresponding to the matched type curve, here3C below the flow velocity into the formation. By performing the same procedure for all depths, a flow profile along the well can be constructed

DK 2017 00702 A1 by the previous methods. An example of type curve techniques for DTS interpretation is given in US Patent Application No. 2009/0216456.

Several DTS interpretation methods have been described herein.

The methods involve the use of a mathematical model (simulated model) to predict the expected temperature response and compare the prediction with current measurements (measured data model). By adjusting the simulated model parameters to fit the measured data model, a flow distribution is deduced in the well. For those skilled in the art, different temperature models may be used, or different techniques could be used to obtain matched with the measured DTS data. However, such variations fall within the spirit of this invention.

The interpreted flow profile provides in-field stimulation technicians with detailed real-time decision making to tailor the stimulation operation to maximize stimulation efficiency. The stimulation operations may include the following activities: placement of tubing tubes in a zone that has not been effectively stimulated to maximize stimulation fluid contact / inflow in that zone; positioning tubing in a zone that has been fully stimulated to spot a distracting agent for temporarily plugging the zone so that the subsequent stimulation fluid can flow into other zones that need further stimulation rather than wasting fluid In the already stimulated zone; changing a treatment fluid if it becomes ineffective: changing a diverter if it becomes ineffective; and placing a temporary plug or other mechanical barrier in the well for isolating the already stimulated zones to allow for separate treatment of the other zones. Other operations may depend on the flow profile generated by embodiments of the methods described herein.

To maximize the stimulation efficiency can be a stimulation operation

DK 2017 00702 A1 is designed to consist of several injection cycles followed by closing periods in which DTS data is acquired. The DTS data is analyzed immediately to inform the field operator of the flow distribution in the well that can be used to make adjustments to the subsequent treatment plan, if necessary, to maximize mucosal efficacy. Thus, well production can be maximized as a result of the optimized stimulation.

The above description is given with reference to presently preferred embodiments of the invention. Those of skill in the art and the technology to which this invention relates will understand that changes and variations of the described designs and operating procedures may be made without departing from the principle and scope of this invention. The above description should therefore not be read as relating to just the constructions described and shown in the accompanying drawings, but should be read as being in accordance with and supporting the subsequent requirements which should have their widest and most reasonable scope.

DK 2017 00702 A1

Claims (15)

patent claims A method for determining flow distribution in a formation with a well formed therein, comprising: placing a sensor in the well, the sensor generating a feedback signal representing at least one of a temperature and pressure measured by the sensor; injecting a fluid into the well and into at least a portion of the formation near the sensor; to Suck the well for a predetermined closing period; to produce a simulated model representing at least one of simulated temperature characteristics and simulated pressure characteristics of the formation during the closure period; generating a data model representing at least one of the current temperature characteristics and current pressure characteristics of the formation during the closing period, wherein the data model is derived from the feedback signal; comparing the data model with the simulated model; and adjusting the parameters of the simulated model to substantially match the data model; determining an interval of interest in the well; determining a volume flow profile for the closure period by dividing the well interval of interest into a number of subsections; generating a simulated model for each of the number of subsections representing at least one of the simulated temperature characteristics and simulated pressure characteristics of DK 2017 00702 A1 formation during the closing period; generating a data model for each of the number of subsections representing at least one of the current temperature characteristics and current pressure characteristics of the formation during the closing period, wherein the data model is derived from the feedback signal; comparing the data model for each of the number of subsections with the simulated model for each of the number of subsections; and adjusting the parameters of the simulated model for each of the number of subsections to substantially match the data model for each of the number of subsections; and determining a volume flow profile for each of the subsets of the entire well interval readily of interest. The method of claim 1, wherein determining a volume flow profile comprises determining a volume of injected fluid to a depth of the well. The method of claim 1, wherein the subdivision comprises dividing the number of subsections into sub-sections of predetermined cross-sectional lengths. The method of claim 1, wherein the fluid is at least one of a derivative and a stimulating fluid. The method of claim 1, wherein the step of adjusting the parameters of the simulated model to it. essential to fit with the data model is performed automatically via a numerical optimization algorithm. DK 2017 00702 A1 The method of claim 1, wherein positioning the sensor comprises positioning the sensor with tubing. The method of claim 1, wherein positioning the sensor comprises positioning a sensor comprising distributed temperature sensing technology and comprising an optical fiber disposed within the well. A method for determining flow distribution in a formation with a well formed therein, comprising: positioning a sensor in the well, wherein the sensor provides a substantially continuous temperature monitoring along a predetermined range of the well, and wherein the sensor generates a feedback signal representing temperature measured by the sensor; injecting a fluid into the well and into at least a portion of the formation near the interval; suctioning the well for a predetermined closing period; dividing the predetermined well interval into a number of subsections; generating a simulated model representing simulated thermal characteristics of each of the subsections of the interval during the sack period; generating a data model representing current thermal characteristics of each of the subsets of the well interval, wherein the data model is derived from the feedback signal; comparing the data model with the simulated model; and adjusting the parameters of the simulated model to substantially match the data model; DK 2017 00702 A1 determination of the flow distribution for the entire well interval; and applying the particular flow distribution for a subsequent treatment process. The method of claim 8, wherein division comprises dividing the number of subsections into subsections with predetermined cross-sectional lengths. The method of claim 8, wherein the sensor includes distributed temperature measurement technology with an optical fiber located along the interval in the well. The method of claim 8, wherein the fluid is at least one of a derivative and a stimulating fluid. The method of claim 8, wherein the step of adjusting the simulated model's parameters to substantially match the data model is performed automatically via a numerical optimization algorithm. The method of claim 8, wherein determining a volume flow profile comprises determining a volume of injected fluid to a depth of the well. A method for determining flow distribution in a formation with a well formed therein, comprising: a) placing a distributed temperature sensor on a fiber extending along a range of the well, wherein the distributed temperature sensor provides substantially continuous temperature monitoring along the range and wherein the sensor generates a feedback signal representing temperature measured by the sensor; b) injecting a fluid into the well and into at least a portion of the formation near the interval; c) closing the well for a predetermined closing period; DK 2017 00702 A1 d) generating a simulated model representing thermal characteristics of a sub-section of the interval during the closing period; e) generating a data model representing current thermal characteristics of the sub-sequence of the interval, wherein the data model is derived from the feedback signal; f) comparing the data model with the simulated model; g) adjusting the parameters of the simulated model substantially to fit the data model; and h) to repeat steps d) to g) for each one. number of subsections defining the interval in the well to produce a flow profile representing the entire interval. The method of claim 14, wherein the step of adjusting the simulated model's parameters to substantially match the data model is performed automatically via a numerical optimization algorithm.