Real-time fracture monitoring, evaluation and control

Systems and methods of the present disclosure generally relate to monitoring, evaluating, and controlling fracture geometry during a hydraulic fracturing operation, in real time. A method comprises measuring a signal representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.

BACKGROUND

Subterranean wells such as hydrocarbon wells, for example, may be stimulated by a hydraulic fracturing operation or fracking. During the fracking, a pressurized fluid is pumped into a wellbore at a pressure sufficient to create fractures propagating from the wellbore into a surrounding subterranean formation. Fracture monitoring and evaluation are critical real-time processes utilized during the fracking. These processes include monitoring and evaluating a structural change in the fractures (e.g., opening, closing, and growing), dimensions of fracture clusters, and/or a balance of fracture clusters, for example.

Fiber optic distributed acoustic sensor (DAS) technology is commonly used to monitor or evaluate a fracking result. DAS data may disclose a rock stress/strain which may be exerted by a slurry flow. The DAS data may also supply temporal and spatial information of rock stress/strain. However, only about 4% of wells include optical fiber cable installations, and most are installed in newer wells.

Another issue with the DAS is a detection of only a flow that passes through perforations. These perforations may cause acoustic interference due to cavitation noise. Thus, a development of a fracture cluster behind a perforation may be indirectly estimated from the flow through this perforation. Such estimates may require intensive training and application practice to interpret DAS data, accompanied with data from other sources.

DETAILED DESCRIPTION

Systems and methods of the present disclosure generally relate to systems and methods for real-time monitoring, evaluating, and controlling of fracture geometry during a hydraulic fracturing operation.

In some examples, the systems and the methods may estimate at least one dominant fracture dimension, such as a length of the dominant fracture (Ld). For example, a signal, such as a pressure, stress, or micro-seismic signal may be measured in the field, in real time, and may be inputted into an RLC model to estimate at least one dominant fracture dimension, such as the Ld. The RLC model is based on an RLC circuit that includes a resistor (R), an inductor (L), and a capacitor (C). An estimate for a dominant fracture dimension (Ld) and a target fracture length (LT) may then be utilized to determine a difference between the Ldand the LT. This difference may be minimized by control actions such as adjustment of a hydraulic fracturing fluid flow rate into the wellbore and/or injection pressure of the hydraulic fracturing fluid, for example. Alternatively, a poroelastic inversion may be utilized to estimate the dominant fracture dimension, such as the Ld. It should be noted that the above-mentioned techniques are non-limiting examples, and that other suitable techniques may be utilized to estimate the Ldand the LT, as should be apparent to one having skill in the art, with the benefit of this disclosure.

In particular examples, the systems and method of the present disclosure may also utilize a time varying RLC model which may describe a relation between a slurry rate and a borehole pressure during fracking. The techniques disclosed herein may also enable real-time monitoring and evaluation during a hydraulic fracturing operation in producing wells, which may improve a quality of the hydraulic fracturing services rendered.

For example, surface and/or downhole pressure data from a treatment well, which are available for many of wells, may be inputted into the RLC model for inversion to obtain fracture characteristics. For example, a fracture characteristic may include a fracture volume indicator which reveals a fracture propagation status in real time. The fracture volume indicator may also identify open or close times for fractures. With real-time information of the fracture propagation status, a field engineer may adjust operational fracking parameters (e.g., pumping rates) to control fracture clusters. There are also other operational indicators, such as net fracture pressure, slurry sagging rate, and borehole expansion rate which may collectively assist in guiding a fracking operation.

The fracture volume indicator may be obtained in real time from model parameters. Additional operational indicators such as slurry accumulation (sagging) rate, and borehole expansion rate may also be extracted via techniques disclosed herein. Also, the systems and methods may differentiate a pressure drop in and near a borehole, and a net fracture pressure drop. An algorithm may be utilized to calculate a power flow acting on a fracture to validate the techniques disclosed herein, by cross-checking a DAS average acoustic energy.

In some examples, a water hammer signal may be inverted to obtain the dominant fracture dimension. The dominant fracture dimension may be associated with the least resistant flow path. As noted above, a pressure signal may be inputted into the RLC model. The measured pressure signal may be high a frequency or low frequency measurement of an excitement of a hydraulic fracture system. The pressure signal(s) may be measured continuous or intermittently and may be utilized to solve the following system of Equations 1 and 2:

C⁢∂H∂t+∂Q∂x=0(1)L⁢∂Q∂t+∂H∂x+R⁢Q=0(2)
where Q is a pumping flow rate of hydraulic fracturing fluid into a wellbore; H is the hydraulic head,

∂H∂t
is time rate of change of hydraulic head;

∂Q∂x
is a change in Q as a function of distance x;

∂Q∂t
is a change in Q as a function of time t;

∂H∂x
is _change of hydraulic head as function of distance x. R, L, and C may be obtained from Equations (1) and (2) such that the measured pressure signal or response matches the response calculated by Equations 1 and 2. Once R, L, and C are determined, a geometry of a planar fracture may be obtained from Equations 3 to 7:

Ld=C⁢L⁢Δ⁢Pρ(3)hf=4⁢E⁢X⁢Cπ2⁢Lf2(4)hf=4⁢E⁢X⁢Cπ2⁢Lf(5)w=ρ⁢LfL⁢hf(6)Δ⁢⁢P=4⁢Eπ2⁡(1-v2)⁢Xw(7)
where ΔP is the pressure difference; p is density of the fluid; hfis fracture height; E is Young's Modulus; X is an elliptical integral of the second kind; Lfis a length of a fracture; w is a width of the fracture; and v is Poisson's ratio. Equation 4 may be used for shorter fractures where 2 Lf/hf<1 and Equation 5 for other cases.

Alternatively, as noted above, a second technique for estimating the dominant fracture dimension (e.g., the Ld) inverts a poro-elastic response measured at an observation well. For example, most of a stress induced may be caused by the dominant fracture. The poro-elastic inversion may be formulated as:
Ld=f(Δpporo,Pnet,E,V, . . . )  (8)
where Δpporois poro-elastic pressure change; Pnetis a net fracture pressure; E is Young's Modulus; and v is Poisson's ratio.

As noted above, alternate techniques that provide at least one dominant dimension (e.g., the Ld), such as, for example, techniques based on distributed acoustic sensing (DAS) flow rate measurements, stress, deformation or micro-seismic measurements, may be utilized. After determining the Ld, the LT(e.g., the target fracture length) may be determined to prevent well-interference, for example.

FIGS. 1A and 1Billustrate techniques for well interference control between a first wellbore A and a second wellbore B, in accordance with examples of the present disclosure. As illustrated onFIG. 1A, the target fracture length LTmay be determined as follows:
LT=LAB/2  (9)
where a distance between the two wellbores is indicated by LAB. The LTmay be half of a distance between the two wellbores.

Alternatively, as illustrated onFIG. 1B, the target fracture length LTmay be determined as follows:
LT=LAB−Lf(10)
where Lfis a length of a fracture100propagating from the second wellbore B toward the first wellbore A. It should be noted that the well interference control scenarios, as illustrated inFIGS. 1A and 1B, are non-limiting examples. Other examples may include a distance to a depleted zone of a producing well, rather than a distance to a neighboring wellbore.

In some examples, a target dimension or the fracture length LTmay be estimated with a theoretical estimate such as a Perkins-Kern-Nordgren (PKN) model or a Kristianovich-Geertsma-de Klerk (KGD) model. For example, the LTmay be determined as follows:

LT=0.48[q3⁢G(1-v)⁢μ]1/6⁢t2/3(11)q=Qn⁢c(12)
where v is Poisson's ratio; μ is hydraulic fracturing fluid viscosity; t is injection time; G is shear modulus of a formation; Q is a pumping flow rate of hydraulic fracturing fluid into a wellbore; nc is the number of clusters; and q represents a flowrate through each cluster. Each cluster may include a plurality of fractures.

In other examples, approaches to generate the target fracture length LTmay be based on a machine learning a correlation based on measurements of past data as a function of time:

LT⁡(t)=f(Q,P,nc,cl,UI,SRVQ,…⁢)(13)
where UI is a uniformity index of a fracture; nc is the number of clusters; cl is a length of each cluster; Q is the pumping flow rate of the hydraulic fracturing fluid into the wellbore; t is time; and SRV is a stimulated reservoir volume.

Once this correlation function is developed, the LTfor a flowing condition may be inferred. For example, to achieve UI=0.8, then the 0.8 value may be inputted into the above correlation along with other known inputs, to determine the LT. In particular examples, a system controller may display differences, in real time, between the LTand the Ld. This allows adjustments to injection pressure and/or flow rate of a hydraulic fracturing fluid or slurry, to minimize these differences.

FIGS. 2A-2Fillustrate achieving equal fracture dimensions in real time, during a hydraulic fracturing operation, in accordance with examples of the present disclosure. As shown onFIG. 2A, a primary or dominant fracture200and non-dominant or secondary fractures202and204may propagate from a wellbore206.FIG. 2Billustrates a length Ldof the dominant fracture200ofFIG. 2Aand the target length LTfor the dominant fracture200in real time t.

FIG. 2Cillustrates controlling the dominant fracture200such that the non-dominant fracture202increases to a length slightly less than the dominant fracture200. The non-dominant fracture204may be unchanged. Control of the dominant fracture200may lead to different and/or increased growth rates in the non-dominant fractures202and204, due to mass conservation.FIG. 2Dillustrates a difference between the length Ldof the dominant fracture200and the target length LTfor the dominant fracture200ofFIG. 2C, as the t increases. The difference between the length Ldand the target length LTdecreases as the t increases.

FIG. 2Eillustrates controlling the dominant fracture200such that the dominant fracture200and the non-dominant fractures202and204achieve equivalent geometry. As shown onFIG. 2F, a difference between the length Ldof the dominant fracture200and the target length LTfor the dominant fracture200may be controlled in real time t such that all of the fractures achieve equal geometry. Real time controlling of the fractures may occur with adjustment of pumping rates and/or pressures until desired fracture geometries are achieved. Controlling may include preventing, maintaining, reducing, or increasing propagation of at least one fracture.

FIG. 3illustrates a schematic diagram of a system300for controlling fracture geometry during a hydraulic fracturing operation, in accordance with examples of the present disclosure. A system controller302may be in communication with hydraulic fracturing equipment304(e.g., pumps, sensors, conduits, hydraulic fracturing fluid). A signal or response306resulting from injection of a hydraulic fracturing fluid into a wellbore, may be measured and inputted into a mathematical model308(e.g., RLC model) for estimating a dimension of a dominant fracture (e.g., Ld). The system controller302may calculate the Ldvia the model308. The LTmay be calculated by the system controller302or inputted into the system controller302as an input310. The input310may include a variety of inputs such as the LT.

The system controller302may direct the hydraulic fracturing equipment304to adjust a pumping pressure or rate to minimize a difference between the Ldand the LT. The system controller302may include a programmable logic controller (“PLC”), for example. In other examples, the system controller302may include may include any instrumentality or aggregate of instrumentalities operable to compute, estimate, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. The system controller302may be any processor-driven device, such as, but not limited to, a personal computer, laptop computer, smartphone, tablet, handheld computer, dedicated processing device, and/or an array of computing devices. In addition to having a processor, the system controller302may include a server, a memory, input/output (“I/O”) interface(s), and a network interface. The memory may be any computer-readable medium, coupled to the processor, such as RAM, ROM, and/or a removable storage device for storing data and a database management system (“DBMS”) to facilitate management of data stored in memory and/or stored in separate databases. The system controller302may also include display devices such as a monitor featuring an operating system, media browser, and the ability to operate one or more software applications. Additionally, the system controller302may include non-transitory computer-readable media. Non-transitory computer-readable media may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time.

FIG. 4illustrates a flow chart for controlling fracture geometry during a hydraulic fracturing operation, in accordance with examples of the present disclosure. At step400, a signal representing a condition in a wellbore is measured. The signal may include pressure measurements, flow rate measurements, stress measurements, deformation measurements, and/or micro-seismic measurements. At step402, the signal may be inputted into a mathematical model (e.g., the RLC model) for estimating a dimension of a dominant fracture (e.g., Ld). At step404, a target dimension for the dominant fracture (e.g., LT) may be determined. At step406, a difference between the Ldand the LTmay be minimized by adjusting an injection pressure and/or flow rate of a hydraulic fracturing fluid into the wellbore.

FIG. 5illustrates various pressures that may be utilized to control a fracking operation, in accordance with examples of the present disclosure. As illustrated, Psurfis a surface pressure that may be measured with a sensor or gauge500. Phydis a hydrostatic pressure within a wellbore502. PBHor Pwbis a bottom hole or well bottom pressure of the wellbore502. Pfricis a friction pressure in the wellbore502. Pperfis a perforation pressure at perforations504of casing506that is disposed within the wellbore502. Ptortis tortuosity pressure within the wellbore502. Pfracis a fracture pressure. Pnetis a net fracture pressure. Pclose=σmin(minimum stress) which is a fracture close pressure for a fracture508. A flow of fracking fluid is indicated with directional arrows510. A relationship between different pressures may be expressed by the following system of equations:
PBH=Psurf+Phyd−Pfric(14)
PBH=Pperf+Ptort+Pfrac(15)
Pfrac=Pnet+Pclose(16)
Equations 14 to 16 utilize the PBHas a pressure input, however, if the PBHis not available, the Psurfmay be used as the pressure input.

The net fracture pressure Pnetmay directly act on reservoir rock pore space to expand a fracture volume, however, the Pnetmay be difficult to measure directly. If a slurry rate is constant, Pfric+Pperf+Ptortmay remain unchanged, if assuming the close pressure Pcloseis also unchanged. Thus, the borehole pressure change PBHmay correspond with the net fracture pressure Pnet, if the slurry rate is constant. In some examples, the friction pressure Pfricmay change dramatically, hence the borehole pressure PBHmay be a combined result of the friction pressure Pfricand the net fracture pressure Pnet. In other examples, a borehole pressure change may be dominated by a friction pressure change. The actual net fracture pressure change may be overwhelmed by the friction pressure change. Thus, particular examples utilize a slurry mass flow RLC model to characterize fracture propagation during fracking.

FIG. 6illustrates a hydraulic system600including a horizontal wellbore601during a fracking operation, in accordance with examples of the present disclosure. A fracking fluid such as a slurry is injected into the wellbore601. This slurry mass flow (or flow rate) is indicated with directional arrows602. The slurry may accumulate at a bottom604of the wellbore601and may also accumulate in existing fractures606in the borehole601. A pressure of a reservoir608is indicated with PR. A fracture pressure of each fracture606is indicated with Pfrac.

FIG. 7illustrates an equivalent or corresponding electrical circuit700that represents the hydraulic system600ofFIG. 6, in accordance with examples of the present disclosure. A flow of electrical current is indicated with directional arrows702that correspond to the slurry mass flow ofFIG. 6, for example. R1and R2are resistors. A capacitor C and inductor L are also illustrated. The circuit700may correspond to a fracture606ofFIG. 6. As illustrated, the electrical current (i.e., an equivalent to the slurry ofFIG. 6) may be stored in the capacitor C, or pass through the resistors R1and R2and inductor L. Components of the circuit700are connected in parallel, rather than in series. For example, in contrast toFIG. 7, a water hammer model may be represented as an RLC circuit with components in series.

FIG. 8illustrates an equivalent parallel circuit800that may also represent the hydraulic system600ofFIG. 6, in accordance with examples of the present disclosure. The circuit800represents the slurry moving inside a borehole, fracture, and reservoir, and a resultant reservoir pressure change. The capacitor is represented by C and the inductor is represented by L. R1is the friction pressure and close pressure related resistance. A complex impedance frequency response of R1may be a full bandwidth constant value in a frequency domain. R2is a net fracture pressure related resistance. R2may be an accumulative response of the slurry rate. Thus mathematically, the accumulation or integral effects may act as a low-pass filter (or a high-pass filter if the net fracture pressure is releasing due to an open fracture or propagation), which may be delineated by R2, L, and C in the analog electrical circuit800. A current I represents the slurry flow rate. A voltage V represents a measured pressure.

FIG. 9illustrates an inversion result900of a mass flow RLC model, in accordance with examples of the present disclosure. A complex impedance frequency response is shown within a window. A curve902is fitted to the inversion result900. Fitting parameters may include R1, R2, C, and L.

FIG. 10illustrates a mass flow RLC model transfer function FFT frequency response simulation and its fitting model, in accordance with examples of the present disclosure. FFT is Fast Fourier Transform. R1is a horizontal straight line since it is a frequency independent parameter. R2is frequency dependent resistance whose amplitude decay curve1000in a frequency domain, may be determined by L and C. The mass flow RLC model utilizes different characteristics of friction pressures and net fracture pressures in the FFT frequency domain. R2is frequency dependent and R1is frequency independent. Based on the different frequency responses, the mass flow RLC model may separate the net fracture pressure from a total bottom hole heel pressure and may infer fracture propagation or operational indicators. By inverting this mass flow RLC model through a moving window, the operational indicators inferred from inversion results may include: a net fracture pressure drop (P2) and a borehole, near borehole, and close-pressure drop (P1); a power-flow acting on a fracture (power); a fracture volume (Vfracture) or its indicator; a fracture volume expansion speed (δ); a slurry accumulation ratio (λ); a slurry density indicator (R2); a borehole slurry mass indicator (L). For example, the fracture volume indicator (Vfracture) indicates instantaneous progress of a fracture propagation (i.e., expanding or shrinking); and fracture volume expansion rate (δ) indicates a dynamic fracture status (i.e., open or close). The slurry density indicator (R2) and the slurry mass indicator (L) may indicate a condition of a wellbore (e.g., a slurry mass accumulating and slurry density change), or may be used for geo-hazard prevention during fracking.

FIG. 11illustrates a workflow1100for monitoring and evaluating subterranean fractures during a hydraulic fracturing operation, in accordance with examples of the present disclosure. At block1102, pressure data (e.g. heel pressure) and slurry data (e.g., flow rate) are inputted into a mass flow RLC model. At block1104, a cascading sliding local window is selected. At block1106, the pressure and slurry data are truncated in the local window. At block1108, the slurry data is autocorrelated. At block1110, the slurry data is cross-correlated with the pressure data. At block1112, a filter may be applied to the slurry data and the pressure data to obtain a matching filter (i.e., complex impedance). A non-limiting example of a filter is a Wiener filter. It should be noted that other filters may be utilized to obtain the matching filter, as should be understood by one having skill in the art, with the benefit of this disclosure. Then, a new sliding local window may be selected and blocks1104to1112may be repeated, as indicated by directional arrow1116. At block1118, the mass flow RLC model parameters are inverted to obtain the operational indicators, as noted above.

FIG. 12illustrates a workflow1200for monitoring and evaluating subterranean fractures during a hydraulic fracturing operation, in accordance with examples of the present disclosure. At step1202, slurry rate data and borehole heel pressure data are selected. At step1204, the slurry rate data and the borehole heel pressure data are interpolated and aligned with respect to time. At step1206, a matching filter between the slurry rate data and the borehole heel pressure data are computed for a selected window (e.g., on a per window basis). At step1208, RLC model parameters are inverted based on the matching filter and a response of the RLC model. At step1210, the operational indicators are obtained through the RLC inversion response or result. It should be noted that a proppant data correction may be applied by calibrating an input pressure by deducting effects of the proppant. This data correction may provide for an improved match with DAS acoustic data.

FIGS. 13A and 13Billustrate a power flow of the RLC inversion response compared with averaged DAS acoustic energy intensity, in accordance with examples of the present disclosure.FIG. 13Aillustrates a power flow, a power flow with proppant correction, and acoustic energy intensity as a function of time.FIG. 13Billustrates the acoustic energy intensity at various depths. As indicated by quality control (QC) lines1302, the power flow acting on a fracture calculated from the mass flow RLC model inversion is similar to the average acoustic energy intensity of DAS data. This illustrates that the mass flow RLC model may simulate an actual physical process of slurry flow in the borehole, and the fracture during fracking. The DAS acoustic energy intensity may reveal a rock stress/strain, in which rock stress is exerted by the slurry rate power flow during the fracking process.

FIG. 14illustrates a fracture expansion speed indicator, in accordance with examples of the present disclosure. Positive values indicate that a fracture is open and/or a fracture is propagating; and a negative value indicates a fracture is closing and/or a fracture is shrinking. It should be noted that the value of the fracture expansion indicator is not an exact the fracture expansion speed; rather the value represents an indicative trend. The QC lines1302may indicate fracture open and/or close timing events which are interpreted from the DAS data ofFIG. 13B. Those fracture open/close timing events may align or correspond with the fracture expansion rate indictor plot peak or trough perfectly.

FIG. 15illustrates a fracture volume indicator varying with time, in accordance with examples of the present disclosure. The fracture volume indicator indicates the fracture volume varying with time (hour(s):minute(s):second(s)). The fracture volume indictor may be proportional to a corresponding actual fracture volume value. It should be noted thatFIG. 15illustrates an indicative trend based on the fracture volume indicator. A fracture volume increase indicates a fracture is growing, whereas a decrease indicates the fracture is shrinking. Displayed in real time, the fracture volume indicator may indicate fracture propagation. The QC lines1302may indicate that the fracture volume indicator aligns or corresponds with the DAS data ofFIG. 13B.

FIG. 16illustrates a bottom hole heel pressure as a function of time, in accordance with examples of the present disclosure. The close pressure Pcloseis indicated by reference number1600. A pressure P1is indicated by the reference number1602. P1is a pressure drop inside a borehole and near borehole:
P1=Pfric−Pclose(17)
where Pfricis the friction pressure, and Pcloseis the fracture close pressure, as previously noted. P1is the friction pressure drop that may be transferred to heat energy, and may have no contribution to the fracture propagation. Another component of P1is the fracture close pressure. This pressure may be a balance pressure in rock pore of the reservoir.

A pressure P2is indicated by reference numbers1604. P2is a pressure drop inside a fracture (without the close pressure). P2is the net fracture pressure during fracking:
P2=Pnet fracture=Pfracture−Pclose=0  (18)
where P2is zero when a slurry rate is zero. These parameters of Equations 17 and 18 are examples of operational indicators. It should be noted that the trend of P2is different from a trend of borehole heel pressure indicating that the borehole heel pressure trend has been overwhelmed by the friction pressure change P1. P2may directly reflect the rock stress which is exerted by a slurry flow. This pressure is the determining force which is used to open the fracture. Through the mass flow RLC model inversion, this pressure may be calculated and separated from the borehole heel pressure. The QC lines1302may indicate that the bottom hole heel pressure data aligns or corresponds with the DAS data ofFIG. 13B.

FIGS. 17 and 18illustrate borehole slurry mass accumulation and slurry density change indicators, in accordance with examples of the present disclosure. As illustrated onFIG. 17, the QC lines1302may indicate that the borehole slurry mass accumulation indicator aligns or corresponds with the DAS data ofFIG. 13B. The QC lines1302ofFIG. 18may indicate that the density change indicator aligns or corresponds with the DAS data ofFIG. 13B. These two operational indicators may be utilized to monitor conditions of the borehole.

FIG. 19illustrates a non-limiting example of a fracking system1900that may be utilized to control and monitor fracture geometries in real time, in accordance with particular examples of the present disclosure. The fracking system1900may be utilized on land operations as well as offshore operations. A wellbore1902extends into a subterranean formation1904. The wellbore1902may include a heel1903and a toe1905. Casing1906may extend along at least a portion of the wellbore1902and may be cemented in place with cement1908. A wellhead1910may be disposed at a surface1912above the subterranean formation1904and may be in communication with the wellbore1902. A frac tank1914and a fracking pump1916may be in fluid communication with the wellhead1910and the wellbore1902via conduits1918and1920. The conduit1918may fluidly couple the wellhead1910to the fracking pump1916. The conduit1920may fluidly couple the fracking pump1916with the frac tank1914. The frac tank1914may contain a fracturing fluid or slurry for pumping into the wellbore1902. A pressure gauge or sensor1922may be disposed at the wellhead1910and may be in fluid communication with the wellbore1902. A slurry flow sensor1924such as a magnetic flow sensor, for example, may be in fluid communication with the conduit1918to determine a slurry flow rate. The system controller302may be in communication with the pressure sensor1922, the slurry flow sensor1924, and the fracking pump1916.

The fracking pump1916may pump the slurry downhole as desired, to create fractures1921propagating from the wellbore1902via perforations1925in the casing. In some examples, the system controller302may adjust the slurry flow rate and/or pumping pressure to control and monitor geometries of the fractures1921, in real time. These adjustments may occur according to the workflows and/or techniques described herein.

Accordingly, the systems and methods of the present disclosure allow for real-time controlling of a fracture dimension to satisfy one or more target objectives, such as, for example: achieving the highest uniformity index, maximizing a ratio of SRV to a pumped slurry volume, or preventing well/depleted production zone interference, when a partial estimate of fracture geometries is available.

Additionally, techniques described herein may be utilized to monitor and evaluate borehole health for wells that include pressure and/or slurry gauges, without additional hardware installation or data acquisition. The systems and methods may include any of the various features disclosed herein, including one or more of the following statements.

Statement 1. A method for real-time controlling of a fracture geometry during a hydraulic fracturing operation, the method comprising: measuring signals representing a condition in a wellbore; inputting the signal into a model for estimating a dimension of a dominant fracture; determining the dimension of the dominant fracture; determining a target dimension for the dominant fracture; and minimizing a difference between the dimension of the dominant fracture and the target dimension in real time, by adjusting at least an injection pressure or flow rate of a hydraulic fracturing fluid into the wellbore.

Statement 2. The method of the statement 1, further comprising inputting the signal into a model comprising at least one resistor, inductor, or capacitor.

Statement 3. The method of the statement 1 or statement 2, further comprising inputting the signal into a poro-elastic inversion.

Statement 4. The method of any preceding statement, further comprising controlling a geometry of the dominant fracture.

Statement 5. The method of any preceding statement, further comprising controlling a geometry of at least the dominant fracture or non-dominant fractures.

Statement 6. The method of any preceding statement, further comprising reducing propagation of the dominant fracture.

Statement 7. The method of any preceding statement, further comprising adjusting at least the injection pressure or the flow rate of the hydraulic fracturing fluid such that geometries of the dominant fracture and non-dominant fractures are of approximately equal size.

Statement 8. The method of any preceding statement further comprising preventing the dominant fracture and non-dominant fractures from extending into another wellbore or a depleted production zone based on the target dimension of the dominant fracture.

Statement 9. A method for real-time monitoring and evaluation of fractures during a hydraulic fracturing operation, the method comprising: selecting slurry rate data and borehole heel pressure data; interpolating and aligning the slurry rate data and the borehole heel pressure data with respect to time; determining a matching filter for slurry rate data and borehole pressure data; inverting parameters of a model that is based on at least resistance, inductance, and capacitance, wherein the parameters are based on the matching filter and a response of the model; obtaining operational indicators from an inversion of the parameters; and controlling a slurry flow rate into a wellbore based on the operational indicators for the hydraulic fracturing operation.

Statement 10. The method of the statement 9, further comprising selecting a local window.

Statement 11. The method of the statement 9 or statement 10, further comprising truncating the slurry rate data and the borehole heel pressure data in the local window.

Statement 12. The method of the statement 11, further comprising applying a filter to the slurry rate data and the borehole heel pressure data to obtain the matching filter.

Statement 13. A hydraulic fracturing system comprising: a frac tank; a pump in fluid communication with the frac tank; a sensor configured to measure a property in a wellbore; and a system controller in communication with the pump and the sensor, the system controller configured to: receive signals from the sensor and estimate a dimension of a dominant fracture propagating from the wellbore; invert parameters from a model based on resistance, inductance, and capacitance, wherein the signals are inputs for the model; and control the pump based on the estimate of the dimension of the dominant fracture or operational indicators.

Statement 14. The system of the statement 13, wherein the system controller is further configured to input the signals into a model comprising at least one resistor, inductor, or capacitor.

Statement 15. The system of the statement 13 or statement 14, wherein the system controller is further configured to input the signals into a poro-elastic inversion.

Statement 16. The system of any one of the statements 13 to 15, wherein the system controller is further configured to: select slurry rate data and borehole heel pressure data; and interpolate and align the slurry rate data and the borehole heel pressure data with respect to time.

Statement 17. The system of the statement 16, wherein the system controller is further configured to: select a local window of the slurry rate data and the borehole pressure data for processing.

Statement 18. The system of the statement 17, wherein the system controller is further configured to: determine a matching filter between the slurry rate data and the borehole pressure data.

Statement 19. The system of the statement 18, wherein the system controller is further configured to: invert parameters from a model based on the matching filter and a response of the model; and obtain operational indicators from inverted parameters, the operational indicators indicative of a hydraulic fracturing operation.

Statement 20. The system of the statement 19, wherein the system controller is further configured to control the pump based on the operational indicators.