Patent Application
20070234789
It is to be understood that the various embodiments of the present invention described herein may be utilized in various orientations, such as inclined, inverted, horizontal, vertical, etc., and in various configurations, without departing from the principles of the present invention. The embodiments are described merely as examples of useful applications of the principles of the invention, which is not limited to any specific details of these embodiments.
In the following description of the representative embodiments of the invention, directional terms, such as “above”, “below”, “upper”, “lower”, etc., are used for convenience in referring to the accompanying drawings. In general, “above”, “upper”, “upward” and similar terms refer to a direction toward the earth's surface along a wellbore, and “below”, “lower”, “downward” and similar terms refer to a direction away from the earth's surface along the wellbore.
Representatively illustrated in
Eventually, the fluid 12 flows into a formation, strata or zone 24 via perforations 26. If desired, the fluid 12 may also be flowed into another formation, strata or zone 28 via separate perforations 30. The zones 24, 28 could be isolated from each other in the wellbore 14 by a packer set in the casing string 22, if desired.
In this manner, a portion 34 of the fluid 12 flows into the upper zone 24, and another portion 36 flows into the lower zone 28. One problem solved by the method 10, as described more fully below, is how to determine in real time the flow rate of the fluid 12 as it flows through the wellbore 14 and into each of the zones 24, 28.
Another problem solved by the method 10 and described more fully below is how to optimize the distribution of the fluid 12 in the zones 24, 28 in real time during the operation. Fluid distribution is the extent to which fluid penetrates a formation or zone versus depth along a wellbore. Graphic examples of desired, predicted and actual fluid distributions are depicted in
In the past, DTS systems utilizing an optical conductor 38 (such as an optical fiber in a small diameter tube, or incorporated into a cable, etc.) have been used to produce a temperature profile along the wellbore 14. After the injection operation, the temperature profile from before the operation would be compared to the temperature profile from during the operation, in order to determine where the fluid 12 entered the various zones 24, 28 and how much of the fluid entered each zone. However, these past methods do not allow the distribution of the fluid 12 to be determined in real time, so that the injection operation can be evaluated and optimized during the operation.
At this point it should be pointed out that the invention is not limited in any way by the details of the method 10 described herein or the configuration of the well as illustrated in
The invention may be used to monitor conditions in a wellbore prior to a treatment, for example, to determine where water is being produced and where a treatment gel should be placed. The invention may be used to place resins for sand control, to repair gravel packing screens, etc.
The invention is not necessarily used only in cased wellbores, since it may also be used in uncased wellbores. The invention is not necessarily used only where multiple zones have fluid transfer with a wellbore. A coiled tubing string could be used to transfer fluid to or from a wellbore. It is not necessary for an optical conductor to be used to monitor temperature along a wellbore.
Therefore, it should be clearly understood that the method 10 is described and illustrated herein as merely one example of an application of the principles of the invention, which is not limited at all to the details of the described method.
Referring additionally now to
Actual treatment parameters, such as injection rate, fluid type and schedules for these, well geometry, reservoir properties, etc. may be input to the model 40, so that the predicted pressure, fluid, injectivity and temperature distributions are based on the actual parameters. Initial fluid distribution (and reaction parameters, if desired) and pressure and temperature distributions input to the model 40 may be manually adjusted to obtain a match between measured and predicted responses versus time. Examples of models are described in “Field Validation of Acidizing Wormhole Models,” SPE 94695 (2005), the entire disclosure of which is incorporated herein by this reference.
Calibration of the model can be conducted based on measured temperature distribution and one or more measured pressures by adjusting the reservoir or other relevant properties. This may require several iterations, and can be automated.
The downhole pressures may be measured using any type of pressure sensor, such as optical pressure sensors coupled to the optical conductor 38. The sensors may be temporary sensors (e.g., installed only for the term of the operation) or permanent sensors (e.g., installed for long term use over the life of the well).
Note that the optical conductor 38 may be retrievably deployed, for example, in fracturing or injection operations, without strapping the optical conductor to the tubing string 18. However, the optical conductor 38 could be permanently deployed or strapped to the tubing string 18, if desired.
Periodically (for example, approximately each minute), a current measured temperature distribution is available from the DTS system using the optical conductor 38. An acceptable DTS system for use in providing the measured temperature distribution is the OPTOLOG® DTS system available from Halliburton Energy Services of Houston, Tex. USA.
Referring additionally now to
In an initial step 44, the well geometry and planned treatment schedule with fluid types/properties and other data are input to the model 40. Possible inputs include reservoir properties, such as permeability, porosity, mineralogy, acid reactivity, skin damage, and permeability contrast. Well geometry may include height of the layers, wellbore tubulars, friction pressures, etc.
In step 46, the model 40 is initialized with an initial fluid distribution versus depth. This initial fluid distribution may be based on well logs and/or core data or other relevant data.
In step 48, the model 40 is initialized with initial data, such as pressure and temperature versus depth. The DTS system may be used to supply this data.
In step 50, the model 40 is used to predict pressure and fluid distribution versus time. Alternatively, these parameters may be predicted for a certain future time.
In step 52, the resulting temperature distribution is predicted. In step 54, the actual temperature distribution is determined in real time, for example, using the DTS system.
As described in the copending patent application entitled TRACKING FLUID DISPLACEMENT ALONG A WELLBORE USING REAL TIME TEMPERATURE MEASUREMENTS, attorney docket no. 2005-IP-019088 U1 USA, the fluid properties and injection rate may be modified and/or chemical reactions may be initiated to enhance detection of temperature gradient differences in the wellbore 14. This technique can enable more accurate determinations of fluid distribution along the wellbore. The entire disclosure of this copending patent application is incorporated herein by this reference.
In step 56, the actual pressure at one or more known locations is determined. An optical conductor with optical sensors, or any other type of pressure sensors may be used in this step for measuring the actual pressure(s) in real time, either as part of the DTS system or separate therefrom.
In step 58, the fluid distribution input to the model 40 is modified, based on the actual temperature and pressure distributions from steps 54 & 56.
In step 60, the pressure distribution and temperature distribution versus time are again predicted using the model 40. In step 62, the predicted pressure and temperature distribution are compared to the actual pressure and temperature distribution to determine whether a match is obtained.
If a match is obtained, then a solution is indicated in step 64, i.e., the fluid distribution input to the model 40 is correct. If a match is not obtained in step 62, then steps 58 & 60 are repeated until a match is obtained.
When additional data becomes available (such as when updated temperature distribution data is provided by the DTS system and/or when pressure measurements become available), this process is performed again. In this manner, the fluid distribution predicted by the model 40 is periodically updated or “calibrated” as the additional data becomes available. In order to optimize the fluid distribution, the planned treatment schedule may be modified based on the calibrated fluid distribution predicted by the model 40.
It should be clearly understood that, although certain inputs have been described above for the model 40, the invention is not limited to only these inputs. Other inputs, and other combinations of inputs, could be used for the model in keeping with the principles of the invention. Thus, it will be appreciated that the model 40 and technique 42 described above may be modified in any manner without departing from the principles of the invention.
Furthermore, although fluid distribution is described above as being predicted and optimized using the model 40 and technique 42, it is not necessary for fluids to be injected, for example, the fluids could instead be produced. Flow rate or injectivity distribution integrated over time yields fluid distribution, and so the above described steps wherein fluid distribution is predicted, determined, etc. may be considered to include prediction, determination, etc. of flow rate or injectivity distribution, as well.
Referring additionally now to
In the depicted example, the operation is planned to include injection of 10,000 gallons of preflush 66, 10,000 gallons of mainflush 67 and 10,000 gallons of overflush 68. This schedule should result in a fluid front of the preflush 66 at approximately 135 inches penetration, a fluid front of the mainflush 67 at approximately 110 inches penetration, a fluid front of the overflush 68 at approximately 75 inches penetration and a live acid edge 69 at approximately 45 inches penetration. These should be fairly consistent along the wellbore between 4900 and 5000 feet as illustrated in
Thus, a comparison between the predicted fluid distribution (as depicted in
Another beneficial feature of the methods and techniques described herein is that the model used to predict fluid distribution may be modified as the operation progresses, so that the model will more accurately predict fluid distribution during the operation. Thus, in the present example, a comparison between the actual fluid distribution as depicted in
In the present example, the remedial action to be taken includes injection of a diverter midway between two halves of the originally planned schedule.
In the past, the original schedule of fluids would have been injected and then, after an analysis of temperature distribution and other data, it may have been determined that remedial action including injection of a diverter should be taken. The diverter and an additional schedule of treatment fluids would have then been injected in an attempt to achieved the desired fluid distribution. It will be readily appreciated by those skilled in the art that the new methods and techniques described herein result in a far more timely, economical and accurate operation being performed.
Referring additionally now to
Inputs to the model 72 include (but are not limited to) pressure and temperature distributions PTD (these may be the same as or similar to the pressure and temperature distributions described above as being input in the technique 42 in steps 48 and 54), geothermal gradient GG (this is similar to the initial temperature distribution described above as being input in the technique 42 in step 48), injection rate IR, fluid type FT (including density, specific heat, etc. of the fluid; these may be the same as or similar to the fluid properties/schedule described above as being input in the technique 42 in step 44), well geometry WG (such as diameters and lengths of tubular strings, deviation, etc.; these may be the same as or similar to the well geometry parameters described above as being input in the technique 42 in step 44), reservoir properties RP (such as rock properties, porosity, permeability, intrinsic fluids, etc.; these may be the same as or similar to the reservoir properties described above as being input in the technique 42 in step 44), and control inputs CI (such as surface pressure, choke position, etc.). The model 72 outputs a predicted fluid distribution PFD along the wellbore 14 at an incremental future time (t+n).
An error evaluation 74 compares the predicted fluid distribution PFD to the current fluid distribution at present time (t). Note that the current fluid distribution FD(t) may be provided by the technique 42 described above and depicted in
Any error determined in the error evaluation 74 is used to modify the model 72, so that future predictions of fluid distribution FD are more accurate. It will be appreciated that this technique 70 of continuously predicting the fluid distribution FD, comparing the predicted fluid distribution PFD to the fluid distribution determined using the real time temperature and pressure measurements in the technique 42, and modifying the model 72 to minimize errors in the predictions enables highly accurate determinations of the fluid distribution in the wellbore 14 to be available in real time during the course of the operation.
In another feature of the technique 70, the predicted fluid distribution PFD(t+n) is input to an optimization device 76 for a determination of how various aspects of the operations should be modified to achieve a desired fluid distribution. The desired fluid distribution is determined prior to the operation, for example, to deliver certain volumes of stimulation fluid to particular zones or intervals, etc.
The optimization device 76 compares the predicted fluid distribution PFD(t+n) to the desired fluid distribution and determines whether certain aspects of the operation should be modified in order to achieve the desired fluid distribution. Of course, if the predicted fluid distribution is the same as the desired fluid distribution, then no modifications will be needed.
As depicted in
Referring additionally now to
For example, the predictive device 82 may include a neural network, an artificial intelligence device, a floating point processing device, an adaptive model, a nonlinear function which generalizes for real systems and/or a genetic algorithm. The predictive device 82 may perform a regression analysis, perform regression on a nonlinear function and may utilize granular computing. An output of a first principle model may be input to the predictive device 82 and/or a first principle model may be included in the predictive device.
Inputs to the neural network 82 include (but are not limited to) measured temperature distribution or profile MTP (this may be the same as or similar to the temperature distribution described above), geothermal gradient GG (this is similar to the initial temperature distribution described above as being input in the technique 42 in step 48), injection rate IR, properties of the fluids PF (such as density, specific heat, etc.; these may be the same as or similar to the fluid types/schedule described above), properties of the wellbore PWB (such as diameters and lengths of tubular strings, deviation, etc.; these may be the same as or similar to the well geometry parameters described above), properties of the intersected zones PZ (such as rock properties, porosity, permeability, intrinsic fluids, etc.; these may be the same or similar to the reservoir properties described above as being input in the technique 42 in step 44), and control inputs CI (such as surface pressure, choke position, etc.). Any of these inputs may be the same as or similar to the corresponding inputs described above for the technique 70.
The neural network 82 outputs a predicted injectivity or flow rate distribution PFRD along the wellbore 14 at an incremental future time (t+n). As discussed above, flow rate or injectivity distribution integrated over time yields fluid distribution, and so it should be understood that prediction or determination of flow rate or injectivity distribution over time also provides predicted or determined fluid distribution, as well.
An error evaluation 84 compares the predicted flow rate distribution PFRD to the current flow rate distribution at present time (t). Note that the current flow rate distribution FRD(t) may be provided by the technique 42 described above and depicted in
Any error determined in the error evaluation 84 is used to modify the neural network 82, so that future predictions of flow rate distribution PFRD are more accurate. It will be appreciated that this technique 80 of continuously predicting the flow rate distribution FRD, comparing the predicted flow rate distribution PFRD to the flow rate distribution determined using the real time temperature measurements in the technique 42, and modifying the neural network 82 to minimize errors in the predictions enables highly accurate determinations of the flow rate distribution in the wellbore 14 to be available in real time during the course of the operation.
In another feature of the technique 80, the predicted flow rate distribution PFRD(t+n) is input to an optimization device 86 for a determination of how various aspects of the operations should be modified to achieve a desired flow rate distribution. The desired flow rate distribution is determined prior to the operation, for example, to deliver certain volumes of stimulation fluid to particular zones or intervals over a certain time, etc.
The optimization device 86 compares the predicted flow rate distribution PFRD(t+n) to the desired flow rate distribution and determines whether certain aspects of the operation should be modified in order to achieve the desired flow rate distribution. Of course, if the predicted flow rate distribution is the same as the desired flow rate distribution, then no modifications will be needed.
As depicted in
As discussed above, the principles of the invention are useful in operations other than injection operations. For example, in production operations the input injection rate IR in the techniques 42, 70, 80 could be replaced with production rate. Similar modifications may be used for other types of operations, as well.
Of course, a person skilled in the art would, upon a careful consideration of the above description of representative embodiments of the invention, readily appreciate that many modifications, additions, substitutions, deletions, and other changes may be made to these specific embodiments, and such changes are within the scope of the principles of the present invention. Accordingly, the foregoing detailed description is to be clearly understood as being given by way of illustration and example only, the spirit and scope of the present invention being limited solely by the appended claims and their equivalents.
