Low frequency distributed acoustic sensing hydraulic fracture geometry

Abstract

Monitoring and diagnosing completion during hydraulic fracturing operations provides insights into the fracture geometry, inter-well frac hits and connectivity. Conventional monitoring methods (microseismic, borehole gauges, tracers, etc.) can provide a range of information about the stimulated rock volume but may often be limited in detail or clouded by uncertainty. Utilization of DAS as a fracture monitoring tool is growing, however most of the applications have been limited to acoustic frequency bands of the DAS recorded signal. Low-frequency Distributed Acoustic Sensing (DAS) signal is used to constrain hydraulic fracture geometry. Events like fracture opening and closing, stress shadow creation and relaxation, ball seat and plug isolation can be clearly identified.

Description

FEDERALLY SPONSORED RESEARCH STATEMENT

Not applicable.

REFERENCE TO MICROFICHE APPENDIX

Not applicable.

FIELD OF THE DISCLOSURE

The disclosure relates generally to hydraulic fracturing. In particular, a data acquisition program using core, image log, microseismic, Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing (DAS), and pressure data is used to monitor stimulation operations.

BACKGROUND OF THE DISCLOSURE

Unconventional reservoirs include reservoirs such as tight-gas sands, gas and oil shales, coalbed methane, heavy oil and tar sands, and gas-hydrate deposits. These reservoirs have little to no porosity, thus the hydrocarbons may be trapped within fractures and pore spaces of the formation. Additionally, the hydrocarbons may be adsorbed onto organic material of a e.g. shale formation. Therefore, such reservoirs require special recovery operations outside the conventional operating practices in order to mobilize and produce the oil.

The rapid development of extracting hydrocarbons from these unconventional reservoirs can be tied to the combination of horizontal drilling and induced fracturing (call “hydraulic fracturing” or simply “fracking”) of the formations. Horizontal drilling has allowed for drilling along and within hydrocarbon reservoirs of a formation to better capture the hydrocarbons trapped within the reservoirs. Additionally, increasing the number of fractures in the formation and/or increasing the size of existing fractures through fracking increases hydrocarbon recovery.

In a typical hydraulic fracturing treatment, fracturing treatment fluid is pumped downhole into the formation at a pressure sufficiently high enough to cause new fractures or to enlarge existing fractures in the reservoir. Next, frack fluid plus a proppant, such as sand, is pumped downhole. The proppant material remains in the fracture after the treatment is completed, where it serves to hold the fracture open, thereby enhancing the ability of fluids to migrate from the formation to the well bore through the fracture. The spacing between fractures as well as the ability to stimulate fractures naturally present in the rock may be major factors in the success of horizontal completions in unconventional hydrocarbon reservoirs.

While there are a great many fracking techniques, one useful technique is “plug-and-perf” fracking. Plug-and-perf completions are extremely flexible multistage well completion techniques for cased hole wells. Each stage can be perforated and treated optimally because the fracture plan options can be modified in each stage. The engineer can apply knowledge from each previous stage to optimize treatment of the current frack stage.

The process consists of pumping a plug-and-perforating gun to a given depth. The plug is set, the zone perforated, and the tools removed from the well. A ball is pumped downhole to isolate the zones below the plug and the fracture stimulation treatment is then pumped in, although washing, etching, and other treatments may occur first depending on downhole conditions. The ball-activated plug diverts fracture fluids through the perforations into the formation. After the fracture stage is completed, the next plug and set of perforations are initiated, and the process is repeated moving further up the well.

Improvements in recovery using fracking depend on fracture trajectories, net pressures, and spacing. Thus, the ability to monitor the geometry of the induced fractures to obtain optimal placement and stimulation is paramount. An induced fracture may be divided into three different regions (hydraulic, propped, and effective), but out of the three fracture dimensions, only the last one is relevant to a reservoir model, and may be used to forecast future production.

Many techniques have been developed to map hydraulic fractures, including microseimic monitoring (Fisher et al. 2004), electromagnetic imaging (Haustveit et al. 2017), seismic time lapse (Atkinson and Davis 2011), pressure monitoring in offset wells (Dawson and Kampfer 2016), fluid and proppant tracers, and others. All these methods have their own strengths and limitations.

Distributed Acoustic Sensing (DAS) is an emerging fiber optic based technology that has become available for the oil industry only in recent years. The method requires an optical fiber attached to the wellbore to guide the laser energy into the reservoir. Each piece of the fiber naturally scatters a small portion of the laser energy back to the surface sensing unit. The sensing unit then uses interferometry techniques to measure the strain change along the fiber. The DAS data are usually sampled at a very high rate (5000-10000 Hz) with a spatial resolution between 1-10 m. This high position accuracy provides critical spatial data for detecting near well bore changes both in the well undergoing stimulation and in an offset monitor well.

The raw DAS data are usually delivered in the form of optical phase, which ranges from −π to +π. The optical phase is defined by the interference pattern of the laser energy back-scattered at two fiber locations separated by a certain length. The separation length is usually referred as gauge length. The phase varies linearly with small length change between two locations, which can be approximated as the axial strain change of the fiber between the locations. Depending on the sensing unit provider, the data deliverable is sometimes a time differential of the measured optical phase. In this case, the DAS data can be treated as a linear-scaled strain rate.

DAS data have been used to monitor hydraulic fracturing operations in many studies. The applications include injection fluid allocation (Boone et al. 2015), hydraulic fracture detection (Webster, Cox, and Molenaar 2013), microseismic monitoring (Webster et al. 2013), and others. However, most of these applications focus on the high frequency bands (>1 Hz) of the DAS data, and some applications only use the “intensity” of the signal, which is obtained through amplitude averaging processing. In this study, we demonstrate that DAS data in the low-frequency band (<0.05 Hz) contain information that can provide critical constrains on hydraulic fracture geometry.

Thus, what is needed in the art are improved methods of evaluating the hydraulic fracturing for every well being hydraulically stimulated. Optimizing completions parameters are critical to improving hydraulic fracturing efficiency and unconventional production performance. Monitoring and diagnosing any potential completion issues during the stimulation operation is important to find the factors that affect well production. Therefore, understanding the geometry of hydraulic fractures is critical for designing unconventional reservoir completions. The length and height of fractures influence the optimization of well spacing and job size (e.g., injection volume), the width is important parameter for proppant transportation and fracture permeability, and the density can be used to optimize cluster spacing. All these completion parameters contribute towards reduced cost-of-supply and improved efficiency for unconventional reservoir development.

Although hydraulic fracturing is quite successful, even incremental improvements in technology can mean the difference between cost effective production and reserves that are uneconomical to produce.

SUMMARY OF THE DISCLOSURE

A new method that utilizes the fiber optic sensing technology to detect and characterize hydraulic fractures is described.

A method of determining strain response in a hydrocarbon-containing reservoir where an operation well and a monitor well with a distributed acoustic sensing (DAS) fiber are used to monitor a DAS signal while fracturing the operation well in a fracture stimulation to form a set of fractures; determining the far-field fracture length, height, width and density of said fractures.

A method of recovering hydrocarbons from a hydrocarbon-containing reservoir comprising: drilling at least one operation well; drilling at least one monitor well; installing a distributed acoustic sensing (DAS) fiber in said monitor well; obtaining DAS data from said monitor while fracturing at least one operation well to form a set of fractures; characterizing the far-field fracture length, height, width and density of said fractures; updating said pre-determined fracturing parameters based on said characterizing step; and, performing a second fracturing stimulation stage; and, producing hydrocarbons.

A computer-implemented method for modeling the stimulated reservoir volume (SRV) of a hydrocarbon-containing reservoir, the method comprising: drilling at least one operation well into an area of said reservoir to be stimulated; drilling at least one monitoring well in said reservoir; installing at least one distributed acoustic sensing (DAS) fiber in at least one monitoring well; obtaining DAS data while fracturing at least one operation well in a first fracture stimulation stage according to pre-determine fracturing parameters to form a set of fractures; identifying said set of fractures formed in said fracturing step; and, characterizing the complexity, length, branching, or density of said fractures.

The monitor well may be a horizontal well or a vertical well. The monitor well can collect data from one or more adjacent operation wells. The data can be used to characterize and model the stress shadow, displacement, fracture propagation velocity, or a combination features. This process may be used to modify the fracturing for subsequent stages and may be repeated for multiple fractures in the hydrocarbon reservoir.

DAS is the measure of Rayleigh scatter distributed along the fiber optic cable. In use, a coherent laser pulse from an interrogator is sent along the optic fiber and scattering sites within the fiber itself causes the fiber to act as a distributed interferometer with a pre-set gauge length. Thus, interactions between the light and material of the fiber can cause a small amount of light to backscatter and return to the input end, where it is detected and analyzed. Acoustic waves, when interacting with the materials that comprise the optical fiber, create small changes in the refractive index of the fiber optic cable. These changes affect the backscatter characteristics, thus becoming detectable events. Using time-domain techniques, event location is precisely determined, providing fully distributed sensing with resolution of 1 meter or less.

Applicant has previously used DAS in a variety of fracturing monitoring methods described in detail in U.S. Ser. Nos. 15/453,650, 15/453,216, 15/453,584, 15/453,434, 15/453,730, 15/453,044, all of which are incorporated herein for all purposes.

The programs and methods described utilize non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, performs the steps of the method(s) described herein.

Due to the nature of the data pre- and post-transform, parallel computing and data storage infrastructure created for data intensive projects, like seismic data processing, are used because they can easily handle the complete dataset. Hardware for implementing the inventive methods may preferably include massively parallel and distributed Linux clusters, which utilize both CPU and GPU architectures. Alternatively, the hardware may use a LINUX OS, XML universal interface run with supercomputing facilities provided by Linux Networx, including the next-generation Clusterworx Advanced cluster management system. Another system is the Microsoft Windows 7 Enterprise or Ultimate Edition (64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAM memory with Fast rotational speed hard disk (10,000-15,000 rpm) or solid state drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiple high resolution monitors. Alternatively, many-cores can be used in the computing. A Linux based multi-core cluster has been used to process the data in the examples described herein.

The disclosed methods include any one or more of the below embodiments in any combination(s) thereof:

Any method described herein, including the further step of printing, displaying or saving the initial, intermediate or final (or both) datasets of the method to a non-transitory computer readable memory.

Any method described herein, further including the step of using the final datasets in a reservoir modeling program to predict reservoir performance characteristics, such as fracturing, production rates, total production levels, rock failures, faults, wellbore failure, and the like.

Any method described herein, further including the step of using said final datasets to design, implement, or update a hydraulic fracturing program in a similar reservoir, in a similar producer well, or in subsequent fracturing stages of said reservoir.

Any method described herein, further including the step of producing hydrocarbon by said reservoir.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

“Fracking”, as used herein, may refer to any human process used to initiate and propagate a fracture in a rock formation, but excludes natural processes that fracture formation, such as natural seismic events. The fracture may be an existing fracture in the formation, or may be initiated using a variety of techniques known in the art. “Hydraulic Fracking” means that pressure was applied via a fluid.

As used herein, “fracture parameters” refers to characteristics of fractures made using hydraulic fracking and includes fracture growth, fracture height, fracture geometry, isolation conditions between stages, stress shadows and relaxation, fracture spacing, perforation cluster spacing, number of perforation clusters/stage, well spacing, job size, pumping pressure, heel pressure, proppant concentration, fluid and proppant distribution between perforation clusters, pumping volume, pumping rate and the like.

As used herein, a “fracture model” refers to a software program that inputs well, rock and fracturing parameters and simulates fracturing results in a model reservoir. Several such packages are available in the art, including SCHLUMBERGERS® PETREL® E&P, FRACCADE® or MANGROVE® software, STIMPLAN™, tNAVIGATOR™, SEEMYFRAC™, TERRAFRAC™, ENERFRAC®, PROP®, FRACPRO™, and the like. Add GOHFER® (Barree & Associates LLC) For shale reservoirs, FRACMAN™ and MSHALE™ may be preferred. These models can be used with appropriate plugins or modifications needed to practice the claimed methods.

By “fracture pattern”, we refer to the order in which the frack zones are fractured.

The term “fracture complexity” refers to the degree of entanglement (or lack thereof) in the induced fractures. Fractures can range from simple planar fractures to complex planar fractures and network fracture behavior. Further, the fracture complexity can change from near-well, mid-field, and far-field regions.

As used herein, the “Gaussian Kernel” or “radial basis function kernel” aka “RBF kernel” is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification.

As used herein, a “monitoring” well is a well nearby a producer that is used to monitor a producer. It produces samples and data for control purposes.

The term “many-core” as used herein denotes a computer architectural design whose cores include CPUs and GPUs. Generally, the term “cores” has been applied to measure how many CPUs are on a giving computer chip. However, graphic cores are now being used to offset the work of CPUs. Essentially, many-core processors use both computer and graphic processing units as cores.

The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims or the specification means one or more than one, unless the context dictates otherwise.

The term “about” means the stated value plus or minus the margin of error of measurement or plus or minus 10% if no method of measurement is indicated.

The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or if the alternatives are mutually exclusive.

The terms “comprise”, “have”, “include” and “contain” (and their variants) are open-ended linking verbs and allow the addition of other elements when used in a claim.

The phrase “consisting of” is closed, and excludes all additional elements.

The phrase “consisting essentially of” excludes additional material elements, but allows the inclusions of non-material elements that do not substantially change the nature of the invention.

The following abbreviations are used herein:

ABBREVIATION TERM
SRV          simulated rock volume
DAS          Distributed Acoustic Sensing
G            gauges
bbl          oil barrel
P            producer well
S            data well
CT           Computer tomography
TVD          True vertical depth
NE           Northeast
SW           Southwest

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A) is a standard industry waterfall visualization. FIG. 1B) is a process low frequency DAS signal.

FIG. 2: Well geometry for the horizontal monitor well example. The operation well and monitor well are shown as blue and green lines, respectively. The two wells are about 700 ft away at this stage. Large solid dots (light green) indicate the perforation location, small dots show the microseismic events.

FIG. 3: Low-frequency DAS response at an offset well during hydraulic fracturing operation.

FIG. 4: Interpreted fracture connections between the operation well and monitor well. Solid dots are perforation clusters colored by stage. Thick solid lines are interpreted fracture connections from perf cluster to fracture hit, colored by stage as well. Thin lines are well paths.

FIG. 5A) data in FIG. 3 is integrated in time to obtain accumulative strain variation. The color contour indicates compression and extension compared to time 0. FIG. 5B) data in a) is integrated in depth to obtain the displacement. The lower panels show the injection rate.

FIG. 6: Well geometries for the vertical monitor well example. The blue and green lines are vertical monitor well and horizontal operation well, respectively. Large green dots are perforation clusters of the stage, brown dots are microseismic events. The large blue disks are co-located pressure and temperature gauge pairs.

FIG. 7: Low-frequency DAS response (color plot) co-plotted with temperature and pressure differential measured by gauges at the vertical well. Red and blue curves are temperature and pressure differential, respectively. Dashed gray lines indicate the gauge pair locations. Solid purple dots are microseismic events that are less than 100 feet away from the monitor well.

FIG. 8A: Temperature gauge response during a fracture hit from FIG. 7. FIG. 8B: Pressure gauge response during a fracture hit from FIG. 7.

DETAILED DESCRIPTION

The invention provides a novel data acquisition program or method for monitoring hydraulic fracturing and sampling stimulation rock volume (SRV).

Data Acquisition

Because DAS is a strain rate sensor and the fiber is mechanically coupled with the formation, strain from the process of hydraulic fracturing can be detected. The DAS data of two example stages shown in this study are recorded at an offset monitor well during stimulation of an adjacent well. The fiber-optic cables are installed outside the casing and cemented in place. The raw data are sampled at 10 kHz continuously at more than 6000 locations along the wellbore, with 1 m spatial sampling and 5 m gauge length. The recorded optical phase is differentiated in time, hence the DAS data are linearly correlated with the strain rate along the fiber.

Data Processing

The raw DAS data are down-sampled to 1 s after a low-pass anti-aliasing filter (0-0.5 Hz) is applied. The data are then median filtered to remove any spiky noise. Another low-pass filter with a corner frequency of 0.05 Hz is then applied. A DC drift with an amplitude around 0.1 rad/s is removed from the data as well. The DC drift was channel invariant and does not vary significantly with time. The drift noise is most likely associated with interrogator noise. We estimate the DC drift by calculating the median value of the channels that are out of the zone of interest at each time interval. Compared to the industry standard waterfall visualizations, the low-frequency processing not only increases the signal-to-noise ratio of the signal, but also preserves the strain rate polarity (FIG. 1) which is important for our interpretations. The strain change recorded by DAS at this frequency band can be caused by thermal variation and/or mechanic strain perturbation.

Hydraulic Fracture Monitoring

Horizontal Well Measurement

The propagation of hydraulic fractures is associated with strain perturbation in the surrounding formation. For a simple planar fracture model, the stress component in the direction perpendicular to the fracture plane can be characterized by two zones: the extensional zone in front of fracture tip, and the compressional stress shadow on both sides of the fracture (Grechka 2005).

Cementation of the fiber in place outside the casing in a horizontal offset well makes it well-suited for measurement of the strain induced by fracture propagation since the fiber is mechanically coupled with the surrounding formation. The monitor well is also usually parallel to the operation well, which is typically at a high angle to the fracture plane, thereby maximizing the strain response along the fiber.

FIG. 2 shows an example of using an instrumented horizontal well to monitor hydraulic fractures. In this example, the monitor well is about 700 feet away from the operation well. The microseismic events show the fractures extended to the monitor well. The fiber in the monitor well is installed outside the casing and cemented in place. The DAS data are recorded continuously during the entire fracturing operation. At this stage, 5 perforation clusters were stimulated simultaneously.

FIG. 3 shows the low-frequency DAS data during the hydraulic fracturing operation as recorded in the monitor well. In this figure, blue and red indicate the fiber compressing and extending, respectively. If a section of the fiber is in the path of a fracture, this part of the fiber will be extended. If the section is on either side of the opening fracture, then it will be compressed (also known as stress shadowing). Individual fracture hits can be identified at several separated fiber locations, where the fiber is extended during the pumping and compressed afterwards, corresponding to the opening and closing of fractures. The length of fiber on the monitor well that records the fracture-hits is comparable to the stage length in the operation well, indicating near-parallel growing fractures. However, deviations from near-parallel fracture patterns that could be related to geology or other factors have also been observed. The extension zone at the tip of the fractures can also be observed as a heart-shape extending pattern at the beginning of the fracture hit signal (marked as ‘new fracture opening’ in FIG. 3). Three fracture intersections can be easily recognized in this stage, with the minimal distance between the fracture hits close to the cluster spacing. Based on this observation, we conclude that 3 of 5 perforation clusters have created a fracture that propagates at least 700 feet horizontally. The velocity of each fracture's propagation can also be estimated by the delay time between the ball seat event in the operation well and the fracture hit arrived at the monitor well. It is also worth noting that due to the spatial resolution (1 m) of DAS, we are unable to distinguish whether each fracture hit represent a single fracture or multiple fractures. In this study, we assume that each individual fracture hit is created by a single fracture.

After locating the fracture hits on the monitor well for all stages, a fracture connection map was created to illustrate the fracture network between the two wells (FIG. 4). Interpretation based on the local maximum stress direction and the fracture hit spacing is made to connect the fracture hits at the monitor well to the perforation clusters in the operation well. This fracture map provides constrain on fracture length, far-field fracture density, as well as cluster efficiency of the operation well.

After the injection stopped (shown by the pump curve in FIG. 3), the signal of extending in the DAS data at the fracture hit locations immediately changes to a signal of compressing, indicating that fractures start to close. The fracture closing signal can last for hours. In FIG. 3, fracture hits from previous stage are still visible before the pumping started at current stage. These previously created fractures opened again in the beginning of the injection. We interpret this to be an indication that the plug was not effective to create isolation between stages now. After the ball seated in the plug, the previous fractures quickly closed, while new fractures were created and started to open. This strain response at the monitor well can also be used to diagnose plug isolation in the operation well.

Stress Shadow

The stress shadow can be clearly identified as the growing compressing (blue) zones on both sides of the fracture zone during the injection in FIG. 3. The stress shadow effect can be observed to extend several stage lengths. As soon as injection stopped, the signal of compressing switched to one of extending, indicating the stress shadow started to relax immediately after the job.

Another way to utilize the stress shadow signal is to integrate the DAS strain rate measurement to produce strain. FIG. 5a shows the integration of the data from FIG. 3 in time. As more fluid is injected, the size and magnitude of the stress shadow increases. By knowing the gauge length, laser frequency, and photoelastic coefficient of the fiber, we can convert the DAS measured optical phase into strain. With the elastic properties of the formation rock, the strain measurement can be compared with local structural stress field to evaluate the stress shadow effect on fracture propagation (not discussed in this paper). In this example, the data also indicates that after the injection stopped, the stress shadow is relaxed in a fairly short period of time.

The strain measurement can be further integrated in space to get displacement, as shown in FIG. 5b. This attribute estimates the amount of displacement that the formation must deform to create the space for the opening fractures, which is an indirect estimation of the fracture width. In FIG. 5b, the displacement is integrated from heel to toe, and maximizes just above the fracture zone before the injection stopped. If the formation and the fiber are well-coupled, the displacement should become positive (toe-wards) after the fracture zone and eventually gets back to zero towards the toe. However, we observed an increase of heel-wards displacement at the toe. This non-physical observation is due to the mechanical decoupling between the fiber and formation in the fracture zone, which is expected because the fiber was not broken while the formation was fractured.

The extension strain measured by the fiber in the fracture zone should be much smaller than the actual strain happened inside and near the fractures. The hypothesis for that argument is that the mechanical decoupling is likely to happen between the formation rock and the cement in a form of slipping movement. Another cautionary note for using the strain and displacement estimation from DAS quantitatively is the mechanical coupling condition within the fiber cable. For better protection, optical fiber installed in borehole condition is usually surrounded by a layer of viscous gel, which decouple the fiber with outside casing mechanically. Although we still can observe the clear formation strain variation in the data, the signal magnitude may be significantly dampened due to the gel layer. Further laboratory investigation is required to calibrate this effect.

It is also important to mention that the DAS monitor well strain measurement in horizontal well should be acquired before the monitor well is hydraulically fractured. Once the well is completed and the borehole is opened to the formation, strain perturbation from other well's operation can produce cross flows between the perforation clusters of the monitor well, which generates large temperature variations and contaminates the strain signal required for fracture mapping.

Vertical Well Measurement

Fiber can also be installed in a vertical monitor well to observe the vertical extent of hydraulic fracture hits from the stimulation well. FIG. 6 shows an example, where the instrumented vertical well is about 600 ft away from the operation well. The microseismic events indicates the hydraulic fractures have reached the monitor well. Seven pairs of temperature and pressure gauges in the monitor well are shown as blue disks. However, only three pressure gauges were functioning during the operation. The fiber in this case is also cemented in place to be mechanically coupled with the formation.

The low-frequency DAS response together with pressure and temperature gauge data are plotted in FIG. 7. The gauge data are differentiated in time to match the DAS strain rate measurement. The gauge response is plotted at their measured depths and scaled for better visualization. Microseismic events with distance less than 100 feet from the monitor well are plotted based on their time and location.

Two separate fracture hit events can be clearly identified in the data, which are associated with fiber extending, and increased temperature and pressure differential. The DAS response is highly correlated with the temperature gauge response. The hyperbolic arrival-time curves that relate to the fracture propagation can also be observed in the DAS data due to the high spatial resolution and large coverage. The shape of the fracture signal arrival curves can be used to estimate the fracture propagation velocity near the monitor well, while the delay time from the ball seat event to the fracture hit arrival can be used to estimate the average fracture propagation velocity between the wells. The first DAS responses are consistent with the first recorded local microseisms in time and depth. However, the DAS data indicate a deeper fracture growth compared to the microseismic observations. It is also ambiguous to interpret the two fracture hits as two sections of one fracture plane, or two individual fractures, as the dipping angle of the fractures is not well constrained in this case.

Because the fiber in the vertical well is almost parallel to the fracture plane, it cannot detect the extension strain created by the fracture opening. Due to the high correlation with the co-located temperature gauge data, we believe that the DAS response in this example is caused by the thermal expansion of the fiber due to temperature change. Although the injected fluid is much cooler than the reservoir temperature, temperature increases are observed at all gauges. We interpret this warming event as the adiabatic compression heating of formation fluid. As shown in FIG. 8, the absolute temperature is increased by about 1° F., while the pressure is increased by about 3000 PSI. This ratio is consistent with the adiabatic compression heating coefficient of water under the reservoir pressure (Knoerzer et al. 2010), which predicts a 1.5° F. temperature increase.

CONCLUSIONS

DAS signal in the low-frequency band (<0.05 Hz) can be used to measure small and gradual strain variation along the fiber. The strain variation can be caused mechanically and/or thermally. Fibers in the horizontal well can be used to monitor the strain perturbation due to fracture propagation during hydraulic stimulation. Fracture intersections with the monitor well can be precisely located, and magnitude of stress shadow can be quantitatively measured. The low-frequency DAS data in this case can be used to constrain the fracture length, density, and width. If the fiber is installed in the vertical well that is parallel to the fracture plane, it can be used to detect the small temperature perturbation due to the increased pressure in the fractures, which can be used to constrain the fracture vertical height. The low-frequency band of DAS data contain valuable information and should be carefully preserved in data processing and hardware development. Hydraulic fracture geometry characterizations using this method can be used to evaluate completions and well spacing design, and constrain reservoir models.

The following references are incorporated by reference in their entirety for all purposes.

• 1. U.S. Ser. No. 10/095,828, Swan, et al., “Production Logs from Distributed Acoustic Sensors,” (2018)
• 2. US-2014-0358444, Friehauf & Dean, “Method of Hydraulic Fracture Identification Using Temperature,” (2014)
• 3. US-2017-0260839, Beardmore, et al., “DAS for Well Ranging,” (2017)
• 4. US-2017-0260842, Jin, et al., “Low Frequency Distributed Acoustic Sensing,” (2017)
• 5. US-2017-0260846, Jin, et al., “Measuring Downhole Temperature by Combining DAS/DTS Data,” (2017)
• 6. US-2017-0260849, Friehauf & Gibson, “DAS Method of Estimating Fluid Distribution,” (2017)
• 7. US-2017-0260854, Jin, et al., “Hydraulic Fracture Monitoring by Low-Frequency DAS,” (2017)
• 8. US-2017-0342814, Krueger, et al., “Low-Frequency DAS SNR Improvement,” (2017)
• 9. US-2018-0016890, Friehauf, “Hydraulic Fracture Analysis,” (2018)
• 10. Kulander, B. R., Dean, S. L., and Ward B. J. 1990. Fractured Core Analysis: Interpretation, Logging, and Use of Natural and Induced Fractures in Core, AAPG Methods in Exploration Series, No 8. Tulsa, 1990
• 11. Kevin T. Raterman, et al. Sampling a Stimulated Rock Volume: An Eagle Ford Example, Unconventional Resources Technology Conference (URTeC), 2017, URTeC: 2670034.
• 12. Fisher, M. K., J. R. Heinze, C. D. Harris, B. M. Davidson, C. A. Wright, and K. P. Dunn. 2004. “Optimizing Horizontal Completion Techniques in the Barnett Shale Using Microseismic Fracture Mapping”. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. doi:10.2118/90051-ms.
• 13. Haustveit, K., K. Dahlgren, H. Greenwood, T. Peryam, B. Kennedy, and M. Dawson. 2017. “New Age Fracture Mapping Diagnostic Tools-A STACK Case Study”. In SPE Hydraulic Fracturing Technology Conference and Exhibition. Society of Petroleum Engineers. doi:10.2118/184862-ms.
• 14. Atkinson, Jared, and Thomas Davis. 2011. “Multicomponent Time-Lapse Monitoring of Two Hydraulic Fracture Stimulations in an Unconventional Reservoir Pouce Coupe Field, Canada”. In SEG Technical Program Expanded Abstracts 2011. Society of Exploration Geophysicists. doi:10.1190/1.3628062.
• 15. Dawson, Matthew, and Günther Kampfer. 2016. “Breakthrough in Hydraulic Fracture & Proppant Mapping: Achieving Increased Precision with Lower Cost”. In Proceedings of the 4th Unconventional Resources Technology Conference. American Association of Petroleum Geologists. doi:10.15530/urtec-2016-2432330.
• 16. Boone, K., R. Crickmore, Z. Werdeg, C. Laing, and M. Molenaar. 2015. “Monitoring Hydraulic Fracturing Operations Using Fiber-Optic Distributed Acoustic Sensing”. In Unconventional Resources Technology Conference. Society of Petroleum Engineers (SPE). doi:10.2118/178648-ms.
• 17. Webster, Paul, Barbara Cox, and Mathieu Molenaar. 2013. “Developments in Diagnostic Tools for Hydraulic Fracture Geometry Analysis”. In Unconventional Resources Technology Conference Denver, Colo., 12-14 Aug. 2013. Society of Exploration Geophysicists. doi:10.1190/urtec2013-025.
• 18. Webster, P., J. Wall, C. Perkins, and M. Molenaar. 2013. “Micro-Seismic Detection Using Distributed Acoustic Sensing”. In SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists. doi:10.1190/segam2013-0182.1.
• 19. Grechka, V. 2005. “Penny-Shaped Fractures Revisited”. Studia Geophysica Et Geodaetica 49 (3). Springer Nature: 365-81. doi:10.1007/s11200-005-0015-3.
• 20. Knoerzer, Kai, Roman Buckow, Peerasak Sanguansri, and Cornelis Versteeg. 2010. “Adiabatic Compression Heating Coefficients for High-Pressure Processing of Water Propylene-Glycol and Mixtures A Combined Experimental and Numerical Approach”. Journal of Food Engineering 96 (2). Elsevier BV: 229-38. doi:10.1016/j.jfoodeng.2009.07.017.

Claims

1. A method of determining strain response in a hydrocarbon-containing reservoir comprising: an operation well and a monitor well, said monitor well comprising a distributed acoustic sensing (DAS) fiber;fracturing said operation well to form a set of fractures;monitoring a DAS signal while fracturing said operation well in a fracture stimulation to form a set of fractures;down-sampling the DAS signal to 1 s after a low-pass anti-aliasing filter (0-0.5 Hz) to obtain Low-Frequency DAS data;determining the far-field fracture length, height, width and density of said fractures.

2. The method of claim 1, wherein said monitor well is a horizontal well, a vertical well, or an adjacent production well comprising a DAS fiber.

3. The method of claim 1, wherein said DAS fiber is a permanently installed DAS fiber or a temporary drop-in DAS fiber.

4. The method of claim 1, wherein said monitor well collects data from one or more adjacent operation wells.

5. The method of claim 1, wherein said determining step includes modeling the stress shadow, displacement, fracture propagation velocity, or a combination thereof.

6. The method of claim 1, further comprising repeating the method for multiple fractures in said hydrocarbon reservoir.

7. A method of recovering hydrocarbons from a hydrocarbon-containing reservoir comprising: drilling at least one operation well;drilling at least one monitor well;installing a distributed acoustic sensing (DAS) fiber in said monitor well;fracturing an operation well using pre-determined fracturing parameters to form a set of fractures;obtaining DAS data from said monitor while fracturing said operation well;down-sampling the DAS data to 1 s after a low-pass anti-aliasing filter (0-0.5 Hz) to obtain Low-Frequency DAS data;characterizing the far-field fracture length, height, width and density of said fractures;updating said pre-determined fracturing parameters based on said characterizing step; and,fracturing an operation well to form at least one additional set of fractures; and,producing hydrocarbons.

8. The method of claim 7, wherein said monitor well is a horizontal well, a vertical well, or an adjacent production well comprising a DAS fiber.

9. The method of claim 7, wherein said DAS fiber is a permanently installed DAS fiber or a temporary drop-in DAS fiber.

10. The method of claim 7, wherein said monitor well collects data from one or more adjacent operation wells.

11. The method of claim 7, wherein said characterizing step includes modeling the stress shadow, displacement, fracture propagation velocity, or a combination thereof.

12. The method of claim 7, further comprising repeating the method for multiple fractures in said hydrocarbon reservoir.

13. A computer-implemented method for modeling the stimulated reservoir volume (SRV) of a hydrocarbon-containing reservoir, the method comprising: drilling at least one operation well into an area of said reservoir to be stimulated;drilling at least one monitoring well in said reservoir;installing at least one distributed acoustic sensing (DAS) fiber in at least one monitoring well;obtaining DAS data while fracturing at least one operation well in a first fracture stimulation stage according to pre-determine fracturing parameters to form a set of fractures;down-sampling the DAS data to 1 s after a low-pass anti-aliasing filter (0-0.5 Hz) to obtain Low-Frequency DAS data;identifying said set of fractures formed in said fracturing step; and,characterizing the complexity, length, branching, or density of said fractures.

14. The method of claim 13, wherein said monitor well is a horizontal well, a vertical well, or an adjacent production well comprising a DAS fiber.

15. The method of claim 13, wherein said DAS fiber is a permanently installed DAS fiber or a temporary drop-in DAS fiber.

16. The method of claim 13, wherein said monitor well collects data from one or more adjacent operation wells.

17. The method of claim 13, wherein said characterizing step includes modeling the stress shadow, displacement, fracture propagation velocity, or a combination thereof.

18. The method of claim 13, further comprising repeating the method for multiple fractures in said hydrocarbon reservoir.