Real-time evaluation of perforation cluster efficiency in hydraulic fracturing wells using relative resistance

Abstract

A system for evaluating a flow distribution of a treatment fluid through perforation clusters in a wellbore casing of a hydraulic fracturing wellbore. The system can access pumping measurements generated by pumping a treatment fluid during the treatment operation and initialize, at a first time, a minimum treatment value and a least resistance value. The system can also determine, using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time. The system can also calculate a relative perforation hole count ratio using the number of perforation holes open in the wellbore at the second time. The system can also determine, via tracking the relative perforation hole count over time, a cumulative reduction in the relative perforation hole count over time to predict a flow distribution of the treatment fluid.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to wellbore drilling operations and, more particularly (although not necessarily exclusively), to evaluating fluid flow through perforation clusters of a hydraulic fracturing well.BACKGROUND

[0002] In the oil and gas industry, a well that is not producing as expected may need stimulation to increase the production of subsurface hydrocarbon deposits, such as oil and natural gas. Hydraulic fracturing is a type of stimulation treatment that has long been used for well stimulation in unconventional reservoirs. A multistage stimulation treatment operation may involve drilling a horizontal wellbore and injecting treatment fluid into a surrounding formation in multiple stages via a series of perforations or formation entry points along a path of a wellbore through the formation. During each of the stimulation treatment stages, different types of fracturing fluids, proppant materials (e.g., sand), additives or other materials may be pumped into the formation via the entry points or perforations at high pressures to initiate and propagate fractures within the formation to a desired extent. With advancements in horizontal well drilling and multi-stage hydraulic fracturing of unconventional reservoirs, there is a greater need for ways to accurately monitor the downhole flow and distribution of injected fluids across different perforation clusters and efficiently deliver treatment fluid into the subsurface formation.BRIEF DESCRIPTION OF THE DRAWINGS

[0003] FIG. 1 is a diagram illustrating an example of a hydraulic fracturing well according to one example of the present disclosure.

[0004] FIG. 2 is a block diagram of the control device of FIG. 1, which may be used to determine a flow distribution of pressurized treatment fluid during a treatment operation of the hydraulic fracturing well using relative resistance according to one example of the present disclosure.

[0005] FIG. 3 is a flow chart illustrating an example of a process for determining the flow distribution of a treatment fluid in a hydraulic fracturing well according to one example of the present disclosure.

[0006] FIG. 4 is a plot illustrating an example percentage of relative number of perforation holes open during a hydraulic fracturing operation according to one example of the present disclosure.

[0007] FIG. 5 is a plot illustrating an example correlation between a relative perforation hole count and uniformity index data collected from historical fiber optic measurements according to one example of the present disclosure.DETAILED DESCRIPTION

[0008] Certain aspects and examples of the present disclosure relate to real-time evaluation of the flow of pressurized treatment fluid through perforation clusters in a wellbore casing of a hydraulic fracturing well using a relative resistance approach. Hydraulic fracturing (i.e., “fracking”) is a process used in the oil and gas industry to extract hydrocarbons from deep within underground formations that may have various subsurface properties. After drilling a wellbore, which typically includes a horizontally-oriented portion, a steel casing may be inserted into the wellbore and cemented in place during well completion. The casing (and the cement surrounding the casing) may then be perforated in multiple locations by a perforating gun or by another technique for creating openings (perforations) through which treatment fluid can flow outward from the wellbore in the treatment well stage to create fractures in the formation. Each perforation location normally includes multiple perforations. These groupings of perforations may be referred to as perforation clusters.

[0009] Prior to placing the well into production, fracturing of the formation can be accomplished by injecting a treatment fluid, typically a staged mixture of water, chemicals, and proppant (e.g., sand), into the wellbore under high pressure such that the treatment fluid will flow forcibly outward through the casing perforation clusters to create small fractures in the surrounding formation. The subsurface properties of a given formation can vary depending on the location of the formation. Thus, the performance of a particular arrangement and quantity of perforation clusters in one formation may not translate perfectly to another formation. This results in desirability to monitor and evaluate each fracturing operation to ensure that a given formation is being adequately fractured prior to placing a completed well into production.

[0010] Understanding the flow distribution across perforation clusters and the efficiency of the flow of treatment fluid through various perforation clusters present in the casing of a hydraulic fracturing wellbore (herein after referred to as “flow distribution” or “cluster completion efficiency”) can allow a hydraulic fracturing well operator to appraise how effectively a given well stage and completion design is performing. Knowledge and real-time evaluation of flow distribution can also be used by the well operator to assess the likelihood that pressurized treatment fluid is sufficiently fracturing the formation surrounding the wellbore. Knowledge and real-time evaluation of flow distribution can also be used by the well operator to, for example, alter the pattern, quantity, or frequency of perforation clusters as required to produce a desired level of treatment fluid flow through the wellbore casing and into the formation.

[0011] A system for evaluating flow distribution according to an example of the present disclosure can operate by utilizing a relative resistance approach to estimate the cluster completion efficiency during a treatment operation by evaluating a deviation from the least resistance value for a given time point and recommending control action if cluster completion efficiency is too low. The least resistance value assumes that 100% of the holes of the perforation clusters are open downhole and therefore relative resistance of the flow of the treatment fluid is zero. As described herein, “open” perforation holes can refer to a perforation hole with an unblocked fluid path. In other words, a “closed” hole may refer to a perforation hole, or cluster of perforation holes, with a blocked or screened out fluid path that may occur, for example, when the fluid path is blocked by materials such as proppant, sand, material from the formation, etc. A blocked or otherwise closed perforation hole can lead to an increased resistance of the fluid flow and therefore, an inability to pump fluid (e.g., treatment fluid) in the well within given operating limits.

[0012] The least resistance value may be initialized at the start of a fracturing operation when the assumption is that 100% of the perforation holes are open downhole. Then, the least resistance value is used as a reference to evaluate the number of perforation holes open at any time point after the start of the fracturing operation. More specifically, the number of perforation holes open at a time, t, after the start of the fracturing operation over the number of perforation holes open downhole at the minimal resistance situation / time point provides a ratio corresponding to a relative number of perforation holes open at time, t. Using the relative resistance approach by tracking a change in the relative number perforation holes open as a function of time, the system can compute a cumulative reduction in the relative number of perforation holes over time and since the start of the treatment operation. Based on the cumulative reduction in relative resistance, the system can evaluate flow distribution of the treatment fluid during the treatment operation.

[0013] Utilization of the relative resistance approach to determine cluster completion efficiency based on surface level measurements collected by a system and implemented by methods according to examples of the present disclosure can be used for a number of purposes and may cause initiation of a number of control actions in the hydraulic fracking context. For example, the cumulative reduction in relative resistance may be correlated to historical flow distribution from fiber optics data to aid in implementing control actions to adjust one or more treatment fluid pumping parameters (e.g., use of diverters to increase / decrease treatment fluid rate, stop pumping, use of diverters to redirect a location of the treatment fluid insertion points, and so on). In other examples, the system may independently evaluate and predict the flow distribution in absence of fiber optics data, and based on the flow distribution evaluation, the system can automatically adjust one or more treatment fluid pumping parameters. Elimination in the need for real-time fiber optic data and evaluation of flow distribution solely from surface level measurements (e.g., measurements taken / received at the surface of the wellbore) reduces monetary costs as well as labor time as there is no requirement for wellbores to include additional fiber components to monitor cluster completion efficiency. In yet other examples, the system, using the real-time flow distribution modeling, may generate a report to a user operating one or more components (e.g., pumps, diverters, etc.) enabling the user to make real-time adjustments to improve the flow distribution.

[0014] Illustrative examples are given to introduce the reader to the general subject matter discussed herein and are not intended to limit the scope of the disclosed concepts. The following sections describe various additional features and examples with reference to the drawings in which like numerals indicate like elements, and directional descriptions are used to describe the illustrative aspects, but, like the illustrative aspects, should not be used to limit the present disclosure.

[0015] FIG. 1 is a diagram illustrating an example of a hydraulic fracturing well 100 according to one example of the present disclosure. As shown, the hydraulic fracturing well 100 can include a wellbore 102 that is formed in a subterranean formation 104. In some examples, the wellbore 102 may alternatively be formed in a sub-oceanic formation. The hydraulic fracturing well 100 can include a vertical wellbore portion 106 and a horizontal wellbore portion 108. The hydraulic fracturing well 100 may include a wellbore casing 110 for transporting produced fluid from the formation 104 to the surface 112 once the hydraulic fracturing well 100 is completed. The wellbore casing 110 may be cemented into the wellbore 102 by introducing cement 114 into the annular space between the wellbore 102 and wellbore casing 110.

[0016] As part of the completion process, multiple perforation clusters 122 are produced at intervals through the wellbore casing 110 of the horizontal wellbore portion 108. It is also possible in other examples for perforation clusters to be produced at intervals through the wellbore casing 110 of the vertical wellbore portion 106. The perforation clusters 122, which are typically produced in one stage of the wellbore casing 110 at a time as the wellbore casing 110 is installed to the wellbore 102, may be created by a perforation gun or by any other technique known in the art. A given well may include a multitude of treatment stages separated using plugs or other suitable elements to isolate stages, and that each treatment stage may have a number of perforation clusters 122. Examples of completion design practices include, for example, plug and perf completions or completions with sliding sleeves, packers, delineation by particular features of the formation 104, and examples according to the present disclosure are applicable to any completion design where it is of interest to measure a flow distribution through the perforation clusters 122 across multiple fluid entry points from a wellbore 102 into a subsurface formation 104.

[0017] The perforation clusters 122, which pass through both the wellbore casing 110 and the cement 114 between the wellbore casing 110 and the wellbore 102, allow fractures 124 to be created in the formation 104 by pumping pressurized treatment fluid 126 into the wellbore 102, whereafter the pressurized treatment fluid 126 will flow forcibly outward through the perforation clusters 122 and into the formation 104. Although not shown in FIG. 1, derricks can, in some examples, be in place on the rig floor 120 to raise or lower equipment related to the fracturing operation.

[0018] Pressurized treatment fluid126 may be pumped into the wellbore 102 through any combination of one or more valves of an injection tool(s). For example, as depicted in FIG. 1, the hydraulic fracturing well 100 includes one or more pump(s) 118, which are controlled by control device 130, for pumping fracturing fluid 116. In more detail, pump(s) 118 can be a suitable machine to circulate and pressurize fracturing fluid 116 from a tank to the interior of the wellbore 102, via one or more ports 140, as pressurized treatment fluid 126 to create the fractures 124. In an example, pump(s) 118 may be of any type (centrifugal, gear, etc.) and powered by any suitable means (e.g., electricity, combustible fuel, etc.). In addition to pump(s) 118, other injection tools may be utilized including, but not limited to, valves, sliding sleeves, actuators, ports, or other features that communicate pressurized treatment fluid 126 from a working string disposed within the wellbore 102 into the formation 104 via perforation clusters 122. Pump(s) 118 may be mounted and transported on an automotive vehicle 144 for transportation to and from the hydraulic fracturing well 100. As described in more detail below, pump(s) 118 may be connected to control device 130, where control device 130 may control pump(s) 118 (e.g., initiate powering on / off, throttling, etc.).

[0019] The perforation clusters 122 may include, for example, open-hole sections along an uncased portion of the wellbore path, a cluster of perforations along a cased portion of the wellbore path, ports of a sliding sleeve completion device along the wellbore path, slots of a perforated liner along the wellbore path, or any combination of the foregoing. Fracturing of the formation 104 facilitates the passage of hydrocarbon fluids from the formation 104 into the wellbore 102 after completion of the hydraulic fracturing well 100.

[0020] Pressurized treatment fluid 126 may include a mixture of liquid(s) or solids which may be pumped through perforation clusters 122 to create fractures 124 and collect hydrocarbon deposits, such as oil and natural gas from formation 104. In an example, pressurized treatment fluid 126 includes a mixture of water, proppant, and chemical additives. Water in pressurized treatment fluid 126 is used to transmit the pressure to formation 104 to create fractures 124. A proppant (e.g., sand, ceramic beads, etc.) keeps fractures 124 open after pressurized treatment fluid 126 is withdrawn from the wellbore 102. Chemical additives may be used to improve the performance of pressurized treatment fluid 126 by reducing friction or preventing loss of viscosity. In an example, the pressurized treatment fluid 126 is pumped down wellbore 102 and through perforation clusters 122 of wellbore casing 110 into formation 104 and causes the subterranean rocks of formation 104 to split and create fractures 124. Consequently, the pressurized treatment fluid 126 is forced into fractures 124, and the proppant adheres to the inner cavity of the fractures 124 to keep the fracture 104 open when the pressurized treatment fluid 126 is withdrawn.

[0021] Evaluating a flow distribution of pressurized treatment fluid 126 through the perforation clusters 122 of the hydraulic fracturing well 100 of FIG. 1 can be monitored and controlled via a control device 130 located at a position (e.g., at the surface or at a remote location) of wellbore 102. Control device 130 is a hardware computing device communicatively coupled to one or more injection tool(s), such as pump(s) 118, and includes suitable software for controlling injection of fracturing fluid 116 into wellbore 102 as well as suitable software for monitoring the flow distribution of the injected pressurized treatment fluid 126 during a treatment operation of a hydraulic fracturing well. In examples, control device 130 may be communicatively coupled to a surface pump (or other suitable injection tool) for injecting pressurized treatment fluid 126 into wellbore 102. In addition, wellbore 102 may include one or more sensors, such as one or more pressure gauges, configured to provide sensor data within the wellbore 102 or at the surface of the wellbore 102 in real-time. In addition to such pressure data, additional data may be captured by control device 130 including, but not limited to, flow rate of the pressurized treatment fluid 126, surface proppant concentration, bottomhole pressure, friction reducer concentration, slurry density, and the like. Control device 130 may collect and analyze the data in real-time, and based on the real-time measurements evaluate a flow distribution of the pressurized treatment fluid 126 through the perforation clusters 122.

[0022] Based on the evaluation of the flow distribution, control device 130 can execute one or more control actions such as adjusting one or more parameters of the pump(s) 118 to increase or decrease the rotational speed, torque, power, or flow rate of the pump(s) 118 (or other suitable injection tool(s)). In one example, control device 130 may control the flow of electrical power (e.g., voltage, current) to pump(s) 118 or control the flow of fuel (e.g., via a choke, valve, etc.) to pump(s) 118. In turn, the rotational speed, torque, power, etc. of the pump(s) 118 may be adjusted. Control device 130 may also execute one or more control actions to adjust a positioning of a flow diverter within wellbore 102, insert a diverter agent (e.g., a substance mixed with pressurized treatment fluid 126 to block certain pathways within the wellbore 102 or control the flow of the pressurized treatment fluid 126 to different locations in the wellbore 102), and so on to adjust the flow distribution to a desired level for the treatment operation. Examples of diverter agents include biodegradable particulates, fibrous materials, ball sealers, soluble polymers, self-degrading gels or foams, and so on. The particular diverter agent used may be selected based on specific conditions of the wellbore 102 such as temperature, pressure, and the desired duration of the blockage.

[0023] FIG. 2 is a block diagram of the control device 130 of FIG. 1, which may be used to determine a flow distribution of pressurized treatment fluid 126 during a treatment operation of the hydraulic fracturing well 100 using relative resistance according to one example of the present disclosure. While FIG. 2 depicts the control device 130 as including certain components, other examples may involve more, fewer, or different components than are shown in FIG. 2. Additionally, while control device 130 is shown in FIG. 1 as being directly connected to automotive vehicle 144 and pump(s) 118, in some examples, control device 130 may be any suitable processing device operatively coupled to any system involved in the fracturing operation via a wired or wireless (e.g., via wireless connection over a network) connection in a distributed computing environment. In some examples, control device 130 may be remotely located from hydraulic fracturing well 100.

[0024] As shown, the control device 130 includes a processor 218 communicatively coupled to a memory 220 by a bus 222. The processor 218 can include one processor or multiple processors. Non-limiting examples of the processor 218 include a Field-Programmable Gate Array (FPGA), an application specific integrated circuit (ASIC), a microprocessor, or any combination of these. Instructions 224 may be stored in the memory 220. The instructions are executable by the processor for causing the processor to perform various operations. In some examples, the instructions 224 can include processor specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, such as C, C++, C#, Java, or Python.

[0025] The memory 220 can include one memory device or multiple memory devices. The memory 220 can be non-volatile and may include any type of memory device that retains stored information when powered off. Non-limiting examples of the memory 220 include electrically erasable and programmable read-only memory (EEPROM), flash memory, or any other type of non-volatile memory. At least some of the memory device includes a non-transitory computer-readable medium from which the processor 218 can read instructions 224. A non-transitory computer-readable medium can include electronic, optical, magnetic, or other storage devices capable of providing the processor 218 with the instructions 224 or other program code, such as program code for executing timing module 212, flow distribution model 214, and so on. Non-limiting examples of a non-transitory computer-readable medium include magnetic disk(s), memory chip(s), ROM, random-access memory (RAM), an ASIC, a configured processor, optical storage, or any other medium from which a computer processor can read the instructions 224.

[0026] In one configuration, through the instructions 224, the control device 130 may cause the timing module 212 to generate timestamp data. Through the instructions 224, the control device 130 may also execute the flow distribution model 214 to evaluate one or more pumping measurements associated with a treatment operation of the hydraulic fracturing well 100. The flow distribution model 214 may implement a relative resistance approach to estimate the flow distribution of the pressurized treatment fluid 126 through perforation clusters 122 during the treatment operation. The relative resistance approach is discussed in more detail below with respect to FIGS. 3-5; but, in general, the control device 130, via execution of the timing module 212 and flow distribution model 214, can track the relative perforation hole count as a function of time, compute a cumulative reduction in the relative number of perforation holes over time and since the start of the treatment operation, and responsive to the cumulative reduction, predict flow distribution of the pressurized treatment fluid 126 during the treatment operation. In cases where the flow distribution is poor, the control device 130 can recommend or implement control actions to adjust the flow distribution to a desired level. The control device 130 can then provide an output 216 containing various information, including but not limited to the calculated relative number of perforation holes, the cumulative reduction in the number of perforation holes over time, the predicted flow distribution of the pressurized treatment fluid 126, and the recommended control actions. The output 216, parts of the output 216, or other information can be presented via various mediums, including on a display device 226 communicatively coupled to the processor 218 by the bus 222. In some examples, the output 216 may be automatically provided to one or more components of the hydraulic fracturing well 100 to automatically adjust one or more treatment fluid pumping parameters (e.g., adjust operation of pump(s) 118) without the need for human interaction.

[0027] FIG. 3 is a flow chart illustrating an example of a process 300 for determining the flow distribution of a treatment fluid in a hydraulic fracturing well according to one example of the present disclosure. The process 300 will be described with respect to the hydraulic fracturing well 100 shown in FIG. 1 and the control device 130 shown in FIGS. 1 and 2; but any suitable system or platform according to this disclosure may be employed, including, for example, a separate or additional control device remotely located from hydraulic fracturing well 100. Additionally, process 300 is provided in the order shown, but in some examples other orders are possible. Additionally, process 300 includes all the steps illustrated, but in some examples less or additional steps may be provided. As described above with respect to FIG. 2, the control device 130 includes a processor 218, a memory 220, and a display device 226. The memory 220 includes instructions 224 that when executed by the processor 218 to perform the operations described by process 300. Such operations as described by process 300 may be performed by processor 218 executing instructions 224, executing the timing module 212, executing the flow distribution model 214, and executing instructions to generate output 216 for display device 226 or otherwise.

[0028] At block 302, a treatment operation of hydraulic fracturing well 100 begins. As mentioned with respect to FIG. 1, as part of the completion process of hydraulic fracturing well 100 multiple perforation clusters 122 are produced at intervals through the wellbore casing 110 of the horizontal wellbore portion 108 using a perforation gun or by any other technique known in the art. Each perforation cluster 122 may have one or more perforation holes. The perforation clusters 122, which pass through both the wellbore casing 110 and the cement 114 between the wellbore casing 110 and the wellbore 102, allow fractures 124 to be created in the formation 104 by pumping pressurized treatment fluid 126 into the wellbore 102, whereafter the pressurized treatment fluid 126 will flow forcibly outward through the perforation clusters 122 and into the formation 104.

[0029] At block 304, processor 218 receives, by virtue of executing timing module 212, timestamp data, t. Timestamp data may refer to real-time measurements recorded by timing module 212 upon starting of the treatment operation of hydraulic fracturing well 100. The timestamp data may be received by processor 218 from timing module 212 instantaneously upon start of the pumping process in increments of 0.01 ns to 1 ns, 1 ns to 1 second, 1 second to 1 minute, or longer timing periods and scales. Further, pumping operation measurements may be acquired and matched to the associated timestamp data. The pumping operation measurements may be stored in memory 220 and may be useable by the processor 218 executing flow distribution model 214 at any time. As such, processor 218 implementing flow distribution model 214 to evaluate the cluster completion efficiency of the treatment operation may utilize the pumping operation measurements from any point in time during the pumping operation. Non-limiting examples of pumping operation measurements include a surface treating pressure, a frictional pressure drop, a hydrostatic pressure, a discharge coefficient, a perforation hole diameter, a number of initial perforation holes shot, pressure measurements of the pressurized treatment fluid 126 at the surface, pressure measurements of the pressurized treatment fluid 126 in the wellbore 102, a surface proppant concentration of the pressurized treatment fluid 126, a flow rate of the pressurized treatment fluid 126, a slurry density of the pressurized treatment fluid 126, and so on.

[0030] At block 306, processor 218 determines whether a flow rate of the pressurized treatment fluid 126 and respective proppant conditions of the pressurized treatment fluid 126 are satisfied. The flow rate and respective proppant conditions may be predefined by the control device 130 or user(s) during the treatment operation of the hydraulic fracturing well 100.

[0031] At block 308, if the flow rate of the pressurized treatment fluid 126 and respective proppant conditions of the pressurized treatment fluid 126 are not satisfied, process 300 reverts back to block 304 to obtain and record the current timestamp data as well as to adjust one or more pumping parameters (e.g., increase the flow rate of the pressurized treatment fluid 126 or adjust the proppant conditions of the pressurized treatment fluid 126) to satisfy the predefined conditions of the treatment operation.

[0032] At block 310, if the flow rate of the pressurized treatment fluid 126 and respective proppant conditions of the pressurized treatment fluid 126 satisfy the predefined conditions of the treatment operation, the processor 218 performs a timing analysis using the timestamp data extracted from timing module 212 to determine if the current timestamp, t, is a first timestamp, to.

[0033] At block 312, if the current timestamp, t, is a first timestamp, t0, the processor 218, using one or more pumping measurements occurring at the first timestamp, t0, defines a minimum treatment value, T(t0). The minimum treatment value, T(t0), may be defined from the one or more pumping measurements and is based on a bottom hole pressure at the first timestamp, denoted as BHP(t0), and a flow rate of the pressurized treatment fluid 126 at the first timestamp, Q(t0). The minimum treatment value, T(t0), is referred to herein as the point of least resistance as it is determined at the start of the treatment operation when the assumption is that all perforation holes are open downhole with no blockages, interruptions in flow, etc. The minimum treatment value, T(t0), may be defined using the following equations:B⁢H⁢P⁡(t0)=Ps(t0)-Pf(t0)+Ph(t0)Equation⁢ (1)T⁡(t0)=B⁢HP⁡(t0)Q⁡(t0)Equation⁢ (2)where Ps, Pf, and Ph are surface treating pressure, fractional pressure drop, and hydrostatic pressure, respectively, obtained by the processor 218 from the one or more pumping measurements.At block 314, after the minimum treatment value, T(t0), is set at block 314, or if the current timestamp, t, is not the first timestamp, t0, i.e., the current timestamp is some time after the first timestamp, tn, the processor 218 performs a comparison between the minimum treatment value, T(t0), and a current treatment value, T(tn), where the current treatment value is calculated using Equation (1) and Equation (2), above, and is based on one or more pumping measurements recorded at timestamp tn.

[0035] At block 316, if the current treatment value, T(tn), is less than the previous minimum treatment value, T(t0), processor 218 computes a least resistance value, P(tn), at timestamp, tn, using the following equation:P⁡(tn)=B⁢H⁢P⁡(tn)-(0.2369×ρ⁡(tn)×Q⁡(tn)2Cd⁡(tn)2×D4×NE⁢s⁢t⁢i⁢m⁢a⁢t⁢e⁢d2)Equation⁢ (3)where P(tn), is a pressure inside fractures 124 within the wellbore 102 at the timestamp, tn, ρ is a slurry density associated with the the pressurized treatment fluid 126, Q is a flow rate of the pressurized treatment fluid 126, Cd is a perforation discharge coefficient, D is a perforation hole diameter of the perforation clusters 122, which may be predefined, and NEstimated is a predefined estimate for number of perforation holes shot when the wellbore 102 was initially constructed. Additionally, the least resistance value, P(tn), need not be computed for each new timestamp data. More specifically, P(tn) is computed only when current treatment value, T(tn), is less than or equal to the minimum treatment value, T(t0). Initially, in cases where the current treatment value, T(tn), is greater than the minimum treatment value, T(t0), the least resistance value is set as the one earlier computed using Equation (3) as P(t0) corresponding to minimum treatment value T(t0). As such, the least resistance value, generally denoted as P(t), is only re-calculated at block 316 responsive to a change in the current treatment value, T(tn), as compared to the pre-established minimum treatment value, T(t0). In other words, if current treatment value T(tn) becomes lower or equal to earlier estimated minimum treatment value T(t0) then block 314 also sets T(tn) as the new or updated T(t0). Accordingly, BHP(tn) will also be set to the new or updated BHP(t0). Otherwise, process 300 uses P(t0) and proceeds to block 320, described below.At block 318, processor 218 computes, given the least resistance value, P(tn), computed from block 316, and the current bottom hole pressure at the first timestamp, BHP(tn), computed with respect to block 312, a number of perforation holes open in the wellbore 102, NCalculated, at the least resistance value, P(tn). As previously mentioned with respect to block 316, NEstimated would be similar to a predefined estimate for number of perforation holes shot when the wellbore 102 was initially constructed (e.g., as part of the completion process of hydraulic fracturing well 100 multiple perforation clusters 122 are produced at intervals through the wellbore casing 110 of the horizontal wellbore portion 108 using a perforation gun or by any other technique known in the art). Thus, given the one or more pumping measurements, a quantatative approximation of a number of perforation holes open at the least resistance level in the wellbore 102 may be computed at block 318 using the following formula:NCalculated=0.2369×ρ⁡(tn)×Q⁡(tn)2BHP⁡(t0)-P⁡(tn)Cd⁡(tn)2×D4Equation⁢ (4)where P(tn) is a pressure inside fractures 124 within the wellbore 102 at the timestamp tn, ρ is a slurry density associated with the the pressurized treatment fluid 126, Q is a flow rate of the pressurized treatment fluid 126, Cd is a discharge coefficient, D is a perforation hole diameter of the perforation clusters 122, which may be predefined, and BHP(t0) is the initial bottom hole pressure computed at block 312.At block 320, processor 218 can use a generalized form of Equation (4) to compute a number of perforation holes open in wellbore 102 at any time, t, using the least resistance level, P(t), i.e., where P(t) is set as P(t0) corresponding to the minimum treatment value, T(t0), or where P(t) is set as P(tn) corresponding to the current treatment value, T(tn), recalculated at block 316 responsive to the analysis performed at block 314). In other words, P(t) having the lowest magnitude should be selected where the lowest magnitude of P(t) represents the least resistance level (i.e., the “ideal cluster completion efficiency level”) of the treatment operation. The number of perforation holes open in wellbore 102 at any time, N(t), may be computed using the following formula:N⁡(t)=0.2369×ρ⁡(t)×Q⁡(t)2BHP⁡(t)-P⁡(t0⁢ or⁢ n)Cd⁡(t)2×D4Equation⁢ (5)where P(t<sub2>0 or n< / sub2>) is a pressure inside fractures 124 within the wellbore 102 at the least resistance value, i.e., to or tn), ρ is a slurry density associated with the the pressurized treatment fluid 126, Q is a flow rate of the pressurized treatment fluid 126, Cd is a discharge coefficient, D is a perforation hole diameter of the perforation clusters 122, which may be predefined, and BHP(t) is the bottom hole pressure.At block 322, processor 218 may determine a relative perforation hole count (also referred to herein as perforation count ratio) at timestamp, t, by computing a ratio between the number of perforation holes open in the wellbore at the timestamp, t, computed at block 320 over a number of perforation holes open in the wellbore at the least resistance, Ncalculated, computed at block 318 by using the following formula:NR⁢elative=N⁡(t)NC⁢a⁢l⁢c⁢u⁢l⁢a⁢t⁢e⁢dEquation⁢ (6)In some examples, the minimum value for NRelative computing over time during a treatment operation may represent a minimum cluster completion efficiency in the treatment operation. As previously described, Ncalculated represents a quantitative approximation of a number of perforation holes open at the least resistance level in the wellbore 102. In an ideal circumstance, 100% of perforation holes would be open at the least resistance. For each computation of the number of perforation holes open in wellbore 102 at any time, N(t), as resulting ratio NRelative can be computed. When NRelative is a minimum, the assumption is that a small number of holes are open down hole, and as such, the minimum of NRelative can represent a minimum in terms of the cluster completion efficiency because the pressurized treatment fluid 126 will be flowing through the least amount of perforation holes at that time.At block 324, processor 218 may calculate a cumulative reduction in the relative perforation hole count, NRelative, as a function of time and since the start of the treatment operation of hydraulic fracturing well 100. For instance, and as described in more detail with respect to FIG. 4, the relative perforation hole count may continuously change over time as pressurized treatment fluid 126 is injected into the perforation clusters 122. During the injection process, perforation holes or entire perforation clusters 122 may become blocked or otherwise may prevent the flow of the pressurized treatment fluid 126 into the fractures 124. As such, the relative perforation hole count will decrease due to various changes detected in the one or more pumping measurements (e.g., changes to the flow rate of the pressurized treatment fluid 126, changes to the bottom hole pressure, and so on). The cumulative reduction may refer to instances over time where the relative perforation hole count has decreased. Based on the cumulative reduction in the relative perforation hole count, NRelative, since the start of the treatment operation, the processor 218 can evaluate, using the flow distribution model 214, a flow distribution of the pressurized treatment fluid 126.At block 326, processor 218, by virtue of execution of the flow distribution model 214, may correlate the cumulative reduction in the relative perforation hole count, NRelative, since the start of the treatment operation to historical treatment fluid flow distribution metrics from fiber optics data collected from a number of wellbores. For example, and as described in more detail with respect to FIG. 5, the cumulative reduction in the relative perforation hole count may be correlated, using a lookup table or plot against fiber optic data. Fiber optics data has previously been utilized as a mechanism to evaluate flow distribution through perforation clusters in a wellbore, such as perforation clusters 122 in wellbore 102. For instance, a fiber optic cable may be installed inside or along the wellbore casing 110 to collect data, such as acoustic energy or vibration data, caused by injection of pressurized treatment fluid or fracture activity (sounds, movement, etc.). The fiber optic data can then be used to indicate characteristics of the flow distribution indicating. This historical data may be used in real-time by the processor 218 to evaluate a flow distribution of the treatment operation of hydraulic fracturing well 100.

[0042] At block 328, processor 218 may predict flow distribution from the cumulative reduction in the relative perforation hole count, NRelative, since the start of the treatment operation in the absence of fiber optics data. For example, the flow distribution model 214 can predict, using a machine learning model, a flow distribution based on the cumulative reduction in the relative perforation hole count, NRelative, since the start of the treatment operation in the absence of real-time and historical fiber optic data. More specifically, the machine learning model implemented by flow distribution model 214, which may be a transformer, a neural network (e.g., a feedforward neural network (FNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory (LSTM) model, etc.), a decision tree, a random forest, a support vector machine (SVM), or an ensemble model, among other possibilities, may undergo a training process using labeled training data to train the machine learning model to identify patterns within the cumulative reduction in the relative perforation hole count based on the pumping measurements over time. For instance, the labeled training data may include example pumping measurements paired with resulting reductions in relative perforation hole counts as well as their corresponding control actions taken to improve the flow distribution. Using the training data, the machine learning model can learn the types of patterns of pumping measurements that are indicative of poor flow distribution (e.g., the machine learning model can identify flow distribution patterns based on the cumulative reduction in the relative perforation hole count between time periods using pumping measurements for respective time periods), and then, during inference, the machine learning model may recommend control actions to improve the flow distribution.

[0043] At block 330, the processor 218 may assess the flow distribution correlated to historical flow distribution metrics from fiber optics data at block 326 or the processor 218 may assess the flow distribution predicted at block 328 to determine whether the flow distribution of the pressurized treatment fluid 126 is poor. As described with respect to block 328, such an evaluation at block 330 may be performed using a machine learning model or other suitable processing technique. For instance, the evaluation at block 330 to determine whether the flow distribution of the pressurized treatment fluid 126 is poor can also be performed using a threshold analysis, where the cumulative reduction in the relative perforation hole count, NRelative, since the start of the treatment operation can be evaluated against one or more thresholds. In this example, if the cumulative reduction is greater than a threshold, for example if the cumulative reduction exceeds a maximum tolerable deviation from the ideal cluster completion efficiency level, the threshold is satisfied, and the resulting determination is that the flow distribution is poor.

[0044] At block 332, if the flow distribution is poor, the process 300 proceeds to block 332 where the processor 218 can execute one or more control actions. More specifically, based on the flow distribution evaluation, the processor 218 can automatically adjust one or more pumping parameters. In some examples, the adjustments to the one or more pumping parameters may be recommended by a machine learning model, such as the machine learning model described with respect to block 328, where the machine learning model is trained to recommend control actions having a high likelihood to improve flow distribution (e.g., minimize the cumulative reduction in relative perforation hole count). Evaluation of the flow distribution based solely on surface level measurements (e.g., measurements taken / received at the surface of the wellbore) eliminates the need for real-time optical fiber data. Elimination of real-time optical fiber data reduces monetary costs as well as labor time as there is no requirement for wellbores to include additional optical fiber components to monitor flow distribution and resulting cluster completion efficiency. In some examples, the real-time modeling described by process 300, may generate a report to a user operating one or more components (e.g., pumps, diverters, etc.) enable the user to make real-time adjustments to improve the flow distribution. If flow distribution is not poor, process 300 reverts to block 304 and restarts.

[0045] Process 300 proceeds to continually evaluate the one or more pumping measurements and providing adjustments to one or more pumping parameters responsive to the evaluation until process 300 reaches block 334 where the treatment operation has completed or otherwise been stopped. For purposes of illustration, examples have been provided herein describing a flow distribution evaluation using one or more pumping measurements generated by pumping a pressurized treatment fluid through perforation clusters of a hydraulic fracturing well. It should be understood, however, that the fluid producing the fractures need not be a treatment fluid. For example, other pressurized fluids, which can include but are not limited to gases, may be pumped into the hydraulic fracturing well and may be used for a similar purpose.

[0046] FIG. 4 is a plot illustrating an example percentage of relative number of perforation holes open during a hydraulic fracturing operation according to one example of the present disclosure. FIG. 4 illustrates the various surface level pumping measurements that may be tracked over time during a treatment operation of a hydraulic fracturing well, such as hydraulic fracturing well 100. The pumping measurements shown in FIG. 4 include slurry rate, treating pressure, friction reducer (FR) concentration, treatment pressure overflow rate, slurry proppant concentration, bottom hole proppant concentration, and global kickout pressure. Other pumping measurements, however, are possible. As described herein, the various pumping measurements, such as those shown in FIG. 4, may be used in a relative resistance approach, to determine real-time perforation count ratio (e.g., a relative number of perforation holes open at time, t) as illustrated by curve 402. Using the techniques described with respect to FIG. 3, the relative number of perforation holes open is tracked over time during a treatment operation.

[0047] Staying with FIG. 4, block 404 illustrates the point of least resistance, as described with respect to FIG. 3. More specifically at the timestamp(s) associated with block 404 the relative resistance approach assumes that all perforation holes are open downhole with no blockages, interruptions in flow, etc. as illustrated by curve 402 having a maximum. At this timestamp, the minimum treatment value, T(t0), is set as the point of least resistance and the least resistance value, P(t0), can be calculated using Equation (3) as described above with respect to FIG. 3. After the timestamp(s) illustrated by block 404, current treatment value(s) of the treatment operation are continuously monitored and compared against the minimum treatment value, T(t0), at the point of least resistance. If a current treatment value recorded is less than the minimum treatment value, T(t0), a new current treatment value, T(tn) (assigned a new minimum treatment value, T(t0)), may be computed as well as a new least resistance value, P(tn). Using the least resistance value, P(t), as previously described with respect to FIG. 3, the number of perforation holes open in wellbore 102 at any time, N(t), may be computed using Equation (5). Based on N(t) and based on the calculated initial number of perforation holes open using Equation (4), the relative number of perforation holes open NRelative, may be determined using Equation (6) and as shown by curve 402.

[0048] FIG. 4 also illustrates two timestamp blocks illustrating drops 406 in the relative number of perforation holes open. The drops 406 may correspond to timestamps during the treatment operation where perforation holes or perforation clusters become blocked or are otherwise are not properly flowing the treatment fluid. As shown in FIG. 4, curve 402 recovers back to a constant level (e.g., back to a point of least resistance) at timestamps occurring after drops 406. This is because when a perforation hole or perforation clusters becomes blocked, the treatment fluid will naturally divert to other perforation holes or perforation clusters that remain open and therefore, curve 402 will eventually balance back to a point of least resistance. But a return back to a point of least resistance does not signify that blocked perforation holes or blocked perforation clusters are operating normally (e.g., are open); rather, a recovery to a point of least resistance may signify that such blocked perforation holes or blocked perforation clusters still remain blocked and the non-blocked perforation holes may be eroding further (due to increased velocity) and offering less resistance. As such, the cumulative reduction in the relative number of perforation holes open (e.g., each drop 406 in the relative number of perforation holes shown by curve 402) is computed as a function of time to more accurately predict the overall flow distribution and cluster completion efficiency of the treatment operation.

[0049] FIG. 5 is a plot illustrating an example correlation between a relative perforation hole count and uniformity index (UI) data collected from historical fiber optic measurements according to one example of the present disclosure. The x-axis of FIG. 5 corresponds to a UI determined from the same well data in this case, such as, for example, fiber optic measurements. In some examples, the well data may be obtained from historical fiber optic measurements from different wells. The UI may be a measure of formation entry point efficiency at each perforation hole. In some examples, UI may be defined by the following equation:UI=1-(S⁢DMean)Equation⁢ (7)Where SD is the standard deviation of a measurement of the slurry volume or proppant mass placed into each formation entry point, and Mean, is the average slurry volume or proppant mass placed into each formation entry point. In some examples, these measurements can be obtained via fiber optics cables. Generally, the higher the UI value, the more evenly distributed the flow of fluid into the formation entry points may be. As such, using a correlation plot such as the plot shown in FIG. 5, the relative perforation hole count (y-axis) can be correlated to historical UI flows to perform an evaluation as to flow distribution where a relative perforation hole count corresponding to a lower UI value indicates poor flow distribution.According to aspects of the present disclosure, a system, a non-transitory computer readable medium, and a method, are provided according to one or more of the following examples. As used below, any reference to a series of examples is to be understood as a reference to each of those examples disjunctively (e.g., “Examples 1-4” is to be understood as “Examples 1, 2, 3, or 4”).

[0051] Example 1 is a system comprising a processing device and a memory. The includes instructions executable by the processing device for causing the processing device to: access one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore; initialize, at a first time, and based on the one or more pumping measurements, a minimum treatment value and a least resistance value; determine, using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time; calculate a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time; calculate a cumulative reduction in the relative perforation hole count between the first time and the second time; and predict, using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

[0052] Example 2 is the system of example 1, wherein instructions are further executable by the processing device for causing the processing device to: control, responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

[0053] Example 3 is the system of example(s) 1-2, wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

[0054] Example 4 is the system of example(s) 1-3, wherein the instructions for controlling the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises controlling an introduction of a diverter agent to redirect the treatment fluid to different locations within the wellbore.

[0055] Example 5 is the system of example(s) 1-4, wherein the instructions for predicting the flow distribution of the treatment fluid comprises correlating the cumulative reduction in the relative perforation hole count to a set of historical treatment fluid flow distribution metrics.

[0056] Example 6 is the system of example(s) 1-5, wherein the set of historical treatment fluid flow distribution metrics comprises fiber optics data collected from a plurality of wellbores separate from the wellbore.

[0057] Example 7 is the system of example(s) 1-6, wherein the instructions for predicting the flow distribution of the treatment fluid is performed in an absence of real-time fiber optic data.

[0058] Example 8 is the system of example(s) 1-7, wherein the instructions for predicting the flow distribution of the treatment fluid comprises identifying, using a machine learning model, one or more flow distribution patterns based on the cumulative reduction in the relative perforation hole count between the first time and the second time and the one or more pumping measurements.

[0059] Example 9 is the system of example(s) 1-8, wherein the instructions for predicting the flow distribution of the treatment fluid comprises generating, based on the one or more flow distribution patterns, one or more recommendations for adjusting one or more parameters associated with pumping of the treatment fluid.

[0060] Example 10 is the system of example(s) 1-9, wherein the one or more pumping measurements are generated from one or more components located at a surface of the wellbore, and wherein the minimum treatment value is initialized based on a bottom hole pressure at the first time and a flow rate of the treatment fluid at the first time.

[0061] Example 11 is the system of example(s) 1-10, wherein wherein the one or more pumping measurements comprise a surface treating pressure, a frictional pressure drop, a hydrostatic pressure, a discharge coefficient, a perforation hole diameter, a number of initial perforation holes shot, pressure measurements of the treatment fluid at a surface of the wellbore, pressure measurements of the treatment fluid in the wellbore, a surface proppant concentration of the treatment fluid, a flow rate of the treatment fluid, a slurry density of the treatment fluid, or any combination thereof.

[0062] Example 12 is the system of example(s) 1-11, wherein the least resistance value is initialized at the minimum treatment value based on pressure measurements of the treatment fluid in the wellbore, the discharge coefficient, the slurry density of the treatment fluid, the flow rate of the treatment fluid, the perforation hole diameter, and the number of initial perforation holes shot.

[0063] Example 13 is a computer-implemented method comprising: accessing, by a processor, one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore; initializing, by the processor at a first time and based on the one or more pumping measurements, a minimum treatment value and a least resistance value; determining, by the processor and using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time; calculating, by the processor, a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time; calculating, by the processor, a cumulative reduction in the relative perforation hole count between the first time and the second time; and predicting, by the processor and using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

[0064] Example 14 is the method of example 13, further comprising controlling, by the processor and responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

[0065] Example 15 is the method of example(s) 13-14, wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

[0066] Example 16 is the method of example(s) 13-15, wherein adjusting one or more parameters associated with pumping of the treatment fluid comprises controlling an introduction of a diverter agent to redirect the treatment fluid to different locations within the wellbore.

[0067] Example 17 is the method of example(s) 13-16, further comprising correlating, by the processor, the cumulative reduction in the relative perforation hole count to a set of historical treatment fluid flow distribution metrics, wherein the set of historical treatment fluid flow distribution metrics comprise fiber optics data collected from a plurality of wellbores separate from the wellbore.

[0068] Example 18 is a non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising: access one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore; initialize, at a first time, and based on the one or more pumping measurements, a minimum treatment value and a least resistance value; determine, using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time; calculate a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time; calculate a cumulative reduction in the relative perforation hole count between the first time and the second time; and predict, using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

[0069] Example 19 is the non-transitory computer-readable medium of example 18, wherein instructions are further executable by the processing device for causing the processing device to: control, responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

[0070] Example 20 is the non-transitory computer-readable medium of example(s) 17-18, wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

[0071] The foregoing description of certain examples, including illustrated examples, has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Numerous modifications, adaptations, and uses thereof will be apparent to those skilled in the art without departing from the scope of the disclosure.

Claims

1. A system comprising:a processing device; anda memory device that includes instructions executable by the processing device for causing the processing device to:access one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore;initialize, at a first time, and based on the one or more pumping measurements, a minimum treatment value and a least resistance value;determine, using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time;calculate a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time;calculate a cumulative reduction in the relative perforation hole count between the first time and the second time; andpredict, using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

2. The system of claim 1, wherein instructions are further executable by the processing device for causing the processing device to:control, responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

3. The system of claim 2 wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

4. The system of claim 2, wherein the instructions for controlling the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises controlling an introduction of a diverter agent to redirect the treatment fluid to different locations within the wellbore.

5. The system of claim 1, wherein the instructions for predicting the flow distribution of the treatment fluid comprises correlating the cumulative reduction in the relative perforation hole count to a set of historical treatment fluid flow distribution metrics.

6. The system of claim 5, wherein the set of historical treatment fluid flow distribution metrics comprises fiber optics data collected from a plurality of wellbores separate from the wellbore.

7. The system of claim 1, wherein the instructions for predicting the flow distribution of the treatment fluid is performed in an absence of real-time fiber optic data.

8. The system of claim 1, wherein the instructions for predicting the flow distribution of the treatment fluid comprises identifying, using a machine learning model, one or more flow distribution patterns based on the cumulative reduction in the relative perforation hole count between the first time and the second time and the one or more pumping measurements.

9. The system of claim 8, wherein the instructions for predicting the flow distribution of the treatment fluid comprises generating, based on the one or more flow distribution patterns, one or more recommendations for adjusting one or more parameters associated with pumping of the treatment fluid.

10. The system of claim 1, wherein the one or more pumping measurements are generated from one or more components located at a surface of the wellbore, and wherein the minimum treatment value is initialized based on a bottom hole pressure at the first time and a flow rate of the treatment fluid at the first time.

11. The system of claim 1, wherein the one or more pumping measurements comprise a surface treating pressure, a frictional pressure drop, a hydrostatic pressure, a discharge coefficient, a perforation hole diameter, a number of initial perforation holes shot, pressure measurements of the treatment fluid at a surface of the wellbore, pressure measurements of the treatment fluid in the wellbore, a surface proppant concentration of the treatment fluid, a flow rate of the treatment fluid, a slurry density of the treatment fluid, or any combination thereof.

12. The system of claim 11, wherein the least resistance value is initialized at the minimum treatment value based on pressure measurements of the treatment fluid in the wellbore, the discharge coefficient, the slurry density of the treatment fluid, the flow rate of the treatment fluid, the perforation hole diameter, and the number of initial perforation holes shot.

13. A computer-implemented method comprising:accessing, by a processor, one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore;initializing, by the processor at a first time and based on the one or more pumping measurements, a minimum treatment value and a least resistance value;determining, by the processor and using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time;calculating, by the processor, a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time;calculating, by the processor, a cumulative reduction in the relative perforation hole count between the first time and the second time; andpredicting, by the processor and using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

14. The method of claim 13, further comprising controlling, by the processor and responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

15. The method of claim 14, wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

16. The method of claim 14, wherein adjusting one or more parameters associated with pumping of the treatment fluid comprises controlling an introduction of a diverter agent to redirect the treatment fluid to different locations within the wellbore.

17. The method of claim 13, further comprising correlating, by the processor, the cumulative reduction in the relative perforation hole count to a set of historical treatment fluid flow distribution metrics, wherein the set of historical treatment fluid flow distribution metrics comprise fiber optics data collected from a plurality of wellbores separate from the wellbore.

18. A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:access one or more pumping measurements generated by pumping a treatment fluid during a hydraulic fracturing operation of a wellbore;initialize, at a first time, and based on the one or more pumping measurements, a minimum treatment value and a least resistance value;determine, using the least resistance value and the minimum treatment value, a number of perforation holes open in the wellbore at a second time occurring after the first time;calculate a relative perforation hole count by computing a ratio between the number of perforation holes open in the wellbore at the second time over a number of perforation holes open in the wellbore at the first time;calculate a cumulative reduction in the relative perforation hole count between the first time and the second time; andpredict, using the cumulative reduction in the relative perforation hole count, a flow distribution of the treatment fluid.

19. The non-transitory computer-readable medium of claim 18, wherein instructions are further executable by the processing device for causing the processing device to:control, responsive to the flow distribution satisfying a threshold, an adjustment of one or more parameters associated with pumping of the treatment fluid.

20. The non-transitory computer-readable medium of claim 19, wherein the adjustment of the one or more parameters associated with pumping of the treatment fluid comprises a flow rate adjustment of the treatment fluid.

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