Wellbore fracturing treatment based on real-time modeling

Abstract

A hydraulic fracturing system and method identifies, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective segment, applies pattern recognition to fracturing data associated with one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event, determines, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, whether the selected fracturing event is likely to occur in the wellbore.

Description

BACKGROUND

[0001] The oil and gas industry uses well stimulation techniques to increase the transfer of hydrocarbon resources from a reservoir formation to a wellbore. Such stimulation typically relies on the introduction of a pressurized fracturing fluid into a wellbore. The pressurized fracturing fluid generates fractures downhole in the reservoir formation. As part of the process, a flow network, sometimes referred to as “frac iron,” is constructed between one or more pumps and a wellhead of a borehole. The flow network provides a path to deliver the pressurized fracturing fluid to the borehole so the fracturing fluid may be used to generate and propagate fractures in the reservoir formation.BRIEF DESCRIPTION OF THE DRAWINGS

[0002] The disclosure may be better understood by referencing the accompanying drawings.

[0003] FIG. 1 is a block diagram illustrating an example well stimulation system, according to aspects of the present disclosure.

[0004] FIG. 2 is a block diagram illustrating another example approach to a well stimulation system, according to aspects of the present disclosure.

[0005] FIG. 3 is a flow diagram illustrating an example method for anticipating and reacting to events that may occur during a segment of hydraulic fracturing treatment of one or more wells, according to aspects of the present disclosure.

[0006] FIGS. 4-7 are graphs illustrating segments of hydraulic fracturing treatment across different wells within a common basin and events that occurred during the respective hydraulic fracturing treatments, according to aspects of the present disclosure.

[0007] FIG. 8 is a block diagram illustrating a multi-level predictive data driven model, according to aspects of the present disclosure.

[0008] FIG. 9 is a block diagram illustrating another multi-level predictive data driven model, according to aspects of the present disclosure.

[0009] FIG. 10 is a block diagram illustrating a computer system that may be used as the computer system in FIGS. 1 and 2.

[0010] Like reference numbers and designations in the various drawings indicate like elements.DETAILED DESCRIPTION

[0011] The description that follows includes example systems, methods, techniques, and program flows that embody embodiments of the disclosure. Unless otherwise specified, use of the terms “connect,”“engage,”“couple,”“attach,” or any other like term describing an interaction between elements is not meant to limit the interaction to a direct interaction between the elements and may also include an indirect interaction between the elements described. Unless otherwise specified, use of the terms “up,”“upper,”“upward,”“uphole,”“upstream,” or other like terms shall be construed as generally away from the bottom, terminal end of a well; likewise, use of the terms “down,”“lower,”“downward,”“downhole,” or other like terms shall be construed as generally toward the bottom, terminal end of the well, regardless of the wellbore orientation. Use of any one or more of the foregoing terms shall not be construed as denoting positions along a perfectly vertical axis. In some instances, a part near the end of the well can be horizontal or even slightly directed upwards. Unless otherwise specified, use of the term “subterranean formation” shall be construed as encompassing both areas below exposed earth and areas below earth covered by water such as ocean or fresh water.

[0012] Hydraulic fracturing is a form of energy transfer. In one example approach, the energy transfer initiates with hydraulic horsepower (via positive displacement pumps) that injects a unit volume of an incompressible fluid into the formation via one or more wellheads. In one such example approach, the incompressible fluid includes a certain volume fraction of proppant. The injection process applies energy through compression to convert a low-pressure volume to a high-pressure state. Surface energy consumption is defined as the integration of the horsepower deployed over time. Integrating the horsepower deployed over time provides total surface energy consumption for the entire hydraulic pumping duration.

[0013] It can be difficult to balance formation development costs versus formation production. It may not be enough to simply look at the total slurry and fluid volume delivered to a formation without taking into account the dynamic conditions in which fracturing jobs occur. One may instead look at well stimulation as an exchange of energy for production (in any form) within the formation. In one example approach, the energy consumed on the surface may be directly correlated to fuel consumed by the hydraulic horsepower and by the horsepower operating cost. This correlation is direct given the fact that fuel and horsepower maintenance may be valued in units of energy (e.g., million British thermal units (MMBtu) or megawatt-hour (MWh)). The effective energy delivered to formation, however, is different from surface energy consumed since the unit volume of slurry that is pumped from the surface down the wellbore, past the perforations and into formation undergoes a series of energy losses and energy gains before reaching the formation. In practice, completion related variables are often changed with no regard to the impact on total energy consumption and related cost since that relationship is not understood. Similarly, the lack of understanding of effective energy delivered to formation prevents operators from making informed decisions regarding drill space unit (DSU) production optimization.

[0014] Furthermore, as a reservoir formation becomes more fractured, the effect of energy transfer into the formation becomes difficult to predict. It may be useful to further understand how energy transferred into the reservoir formation alters the formation; such information may be especially useful in optimizing complex fracture systems that are generated during the hydraulic fracture completion of single wells, multiple wells, multiple wells on a single pad, and multiple wells from multiple pads. Understanding how this energy is being dissipated in the reservoir and the upper effective limits of this energy may enable optimization of asset development all the way from well design to pad design and then on to completion sequencing and well production. In some cases, the optimum completion design may also change depending on when a given well is completed during the sequence of operations.

[0015] Traditionally, physic-based models have been used to design hydraulic fracturing treatments. Such models have not proven effective due to the complexity of formations having two or more wellheads. Current fracture models consider predominately Mode 1 (tension) types of failure. Shale applications, however, include multiple fractures along a horizontal wellbore. There may be significant stress interference between different fractures and the levels of stress increase significantly as fractures interfere with each other. High strain levels within regions of high stress interference can cause rock failure in shear that include Mode 2 (in plane shear failure) and Mode 3 (out of plane shear) types of failure. Dilation of Mode 2 and Mode 3 fractures creates a pressure field within the multi-mode fracture system that may be detected far away from the hydraulically initiated fractures themselves.

[0016] There are similar types of interference between multi-stage horizontal wells. In multi-stage horizontal wells, the amount of stress interference between stages and between different wells can become extreme. It may no longer be a reasonable assumption to rely on Mode 1 models since while Mode 1 failure will occur, shear and compressional failure will also occur resulting in a multi-mode fracture system. The result is a complex fracture system combining Mode 1, Mode 2, and Mode 3 failures. Open fractures may appear well away from the hydraulic fracture and significant pressure fields associated with the hydraulic fracture process may be detected a significant distance away from the hydraulic fracture.

[0017] Stress is transmitted through the rock very quickly, but pressure front development or pressure dissipation may be much slower as it is controlled by fracture dilation and leak-off into initially closed fracture systems within the sheared region. Pressure response lags the strain response. Once these fractures are open, however, the pressure signal moves faster. Either way the pressure field from hydraulic fracturing dissipates throughout the complex fracture system and can be used to understand the system itself. For the first wells in a system, the pressure front is mostly behind the newest fractures. After the first wells are fractured, however, the pressures will be much more interactive and connected. Interior wells, therefore, will behave much differently than those on the outside of the reservoir formation or on the outside of a given pad of wells or multiple pads of wells.

[0018] Underground sensors such as pressure gauges and Low Frequency Distributed Acoustic Sensing (LFDAS) may be used to characterize fractures in a complex fracture system. For instance, it is possible to use external pressure gauges combined with LFDAS to identify specific distributed acoustic sensing (DAS) behaviors or patterns that are indicative of a pressure communication event in the formation. Data driven models of such fractures have been proposed. Some such models may depend on above-ground measurements of surface energy and underground measurements of stress to determine propagation of fractures in a complex fracturing system. Such models, however, are not predictive in nature and have been proven to be quite limited in enabling effective and relevant decisions in real-time. Most of the current models are look back models that provide basic information on completion designs based on historical data. They do not enable real-time responses to specific events in the completion process.

[0019] In the United States unconventional market, some operators are using a trial-and-error method to replace physics-based modelling when designing hydraulic fracturing treatments. The general assumption is that these wells and each fracturing segment will behave the same. In addition, operators rely on people to make decisions in real time when executing a treatment option. Optimally, the person making the decisions has experience in treating wellbores in the given type of formation. In reality, some people have a great deal of relevant experience while others do not, and the decisions made may not be consistent or correct in many situations.

[0020] In addition, operators may see significant variations in response between treatment segments. Very little effort has been expended, however, on understanding the cause of these variations or the results of this behavior. The trial-and-error method is time consuming and provides a relatively slow means of making decisions due to a slow feedback loop; with well performance or production the trial-and-error method may take several months to get the desired feedback.

[0021] Other operators are using data driven models when designing hydraulic fracturing treatments. In most cases, data driven models only tell the operator what he or she already knows, or what he or she should already know. In shale fracturing, data driven models tend to tell operators that what they are currently doing is best and it is hard to move past this when designing new hydraulic fracturing treatments.

[0022] A more predictive data-driven model accelerates the learning curve and reduces the risk of the costly repetition of mistakes. If used properly, this data-driven model may be supported with physics-based solutions to help make treatment changes beneficial both to current pumping operations and to longer-term well production performance.

[0023] In one example approach, rather than attempting to create a model for an entire hydraulic fracturing treatment, each hydraulic fracturing treatment is broken down into specific segments and models for each segment are trained using pattern recognition and machine learning. The behaviors observed for each segment are the used to create higher level models that are more robust in nature. Advanced pattern recognition techniques may be used to make multiple predictions during a single treatment and machine learning techniques may be used to make predictions based on those observations.

[0024] In one such example approach, formation breakdown behavior (the hydraulic efficiency or the time required to reach design injection rate) may be modeled based on automated breakdown techniques used to provide a consistent procedure to establish flow rate based on pressure response. This consistent procedure means that different breakdown behaviors may be identified, and effective treatment outcomes predicted. In one such example approach, actions recommended based on the observed response may be used to improve the probability of achieving the desired result or the desired outcome.

[0025] To date, no one has taken a data driven approach to characterizing specific behaviors and responses at each segment of the treatment process. The ability to characterize such behaviors and responses may, however, not only enable actions to be taken at each segment of an operation, but also may predict what to expect on subsequent treatment segments. For example, observations of pressure response after breakdown may be used to provide suggestions for later in the treatment. Observations of pressure response when proppant hits perforations for proppant sweeps or main proppant schedule, observations of pressure response as proppant concentrations are increased during proppant segments, observations of proppant pulse behavior during operations (e.g., during quick step-down tests or during diverter cycles) and observation of pressure response when the diverter reaches the perforations may be used to characterize performance at each segment of treatment. Similarly, observations of water hammer behavior, observations of offset well interference such as, for example, frac hits observed with fiber optic or offset well pressure monitoring, and observations of DAS interpretation on perforation cluster efficiency in real time may be used to characterize performance at particular segments of treatment. Finally, observations of hydraulic efficiency including time to reach target injection rate and effective energy placed into the formation may be used to characterize performance at given segments of treatment. As treatment continues through each segment, the model refines the predictions and recommendations to reflect further observations and behaviors. At the same time, the model may suggest actions to be taken by the control system at each segment based on specific treatment responses in previous segments. Actions may include performing a quick step-down test to capture pressure pulse data, perforation friction, near wellbore tortuosity and the number of open perforations taking fluid, running a procedure for alleviating wellbore friction and reducing treating pressures, running a near wellbore or far field diversion stage, running a proppant sweep, changing the high viscosity friction reducer (HVFR) concentration, changing the proppant schedule or ending the segment early.

[0026] In one example approach, this data driven approach is used to provide initial recommendations for treatment options. The selected options at each treatment segment may change, however, as the system is refined at each treatment segment. based on the observations at each segment. In one such example approach, the system captures all relevant data within a basin to train a model to recognize early indicators and to make logical decisions based on previous results observed over hundreds or thousands of previous treatments. In one example approach, the data is supplemented with information from subject matter experts to formalize a decision-making process. The result may be used by the treatment engineer to accept or decline the recommendations or, in some cases, may be forwarded directly to the system controller to implement the recommended action. The data driven technique provides a means to achieve consistent and repeatable performance for crews across the entire organization and, as experience is gained, may provide real-time decisions guiding automated treatment processes in the future.

[0027] In some implementations, a downhole operation or attribute in the wellbore may be modified or updated based on the determining whether the fracturing event might occur in the wellbore. For example, an operation (at the surface or downhole) may be performed and / directed to be performed to change a downhole operation or attribute based on whether the fracturing event might occur in the wellbore. For example, attributes of an actual fracturing operation in the wellbore may be set based on whether the fracturing event might occur. Examples of such attributes of the actual fracturing operation may include depth, composition of the proppant used for fracturing, composition of the fracturing fluid used for fracturing, the pump rate for fracturing, etc. For instance, if the fracturing event is determined to not likely to occur, any one of these attributes may be updated to increase the likelihood that the fracturing event is to occur.

[0028] 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.Example Well Stimulation System

[0029] FIG. 1 illustrates an example well stimulation system, according to aspects of the present disclosure. A typical well stimulation system 100 includes one or more pumps 114 and a flow network connecting the pumps 114 through a borehole 102 to a reservoir formation 103. In operation, the flow network conveys pressurized fracturing fluid from the pumps to the reservoir formation. In general, fracturing fluids in well stimulation systems are injected into the wellbore at a high pressure in order to convey sufficient energy to a subterranean formation to cause fracturing in the formation. In some example approaches, well stimulation systems induce fluid pressures in a range of 3,000 to 20,000 pounds / square inch (psi) in the fluid injected into the wellbore with total slurry rate in the range of 10 to 200 barrels per min (bpm).

[0030] In the example shown in FIG. 1, well stimulation system 100 includes a frac iron configuration 110 connected through borehole 102 to reservoir formation 103. Frac iron configuration 110 includes frac iron 112 and one or more pumps 114. In the example approach shown in FIG. 1, frac iron 112 receives pressurized fracturing fluid pumped by the one or more pumps 114 from fluid source 116 and conveys the received pressurized fluid through borehole 102 to reservoir formation 103.

[0031] In some example approaches, borehole 102 includes a casing 101 and an opening 105 at surface 104. In some such example approaches, the pressurized fracturing fluid passes through an isolation valve 106 before entering borehole 102 at opening 105. In some example approaches, borehole 102 further includes perforations 120 at certain locations in reservoir formation 103, the pressurized fracturing fluid passing through the perforations 120 to cause fractures in the reservoir location 103.

[0032] In one example approach, well stimulation system 100 includes a computer system 150. In one such example approach, computer system 150 includes a processor 152 and a memory 154. In one example approach, instructions are stored in memory 154 that, when executed by processor 152, allow the processor 152 to control the fracturing process and to capture data representing pressure fields generated via fracturing.

[0033] In one example approach the computer system 150 receives an effective energy model for energy loss in pressurized fracturing fluid passing into and through borehole 102 to a reservoir formation 103 during hydraulic fracturing. The model may be used at each segment identified for the hydraulic fracturing treatment to determine effective energy delivered to the reservoir formation as a function of surface energy added to the fracturing fluid when raising the fracturing fluid to a high-pressure state; gravitational potential energy gains in the pressurized fracturing fluid as the fluid travels down to the reservoir formation; and energy losses in the pressurized fracturing fluid as the fluid travels down to the reservoir formation. In one example approach, an operator may use computer system 150 to apply the effective energy model to a selected reservoir formation and may select, based on the effective energy model, an operational cost for hydraulic fracturing of the selected reservoir formation. The operator may then use the computer system to control the one or more pumps 114 in frac iron configuration 110 to achieve the selected operational cost.

[0034] As noted above, surface energy consumption may be expressed as units of energy. For instance, surface energy consumption may be expressed as horsepower times hours, or horsepower hours, which may be considered a unit of energy. The surface energy used in treatment of one or more wells may be calculated by multiplying pressure, rate, and operational time for the duration of the well treatment to arrive at surface horsepower hours consumed. Surface horsepower hours consumed may also be calculated by integrating pressure with respect to volume pumped for the duration of the treatment. That is, surface energy may be determined as the energy input to elevate a unit volume of slurry (fluid containing a certain volume fraction of proppant) from a low-pressure state to a high-pressure state.

[0035] As the unit volume of slurry traverses down borehole 102 it is met with assistance in the form of gravitational potential energy (hydrostatic pressure) and with resistance in the form of pipe friction. Additionally, when the unit of slurry flows past perforations 120 and near wellbore tortuous regions, the unit undergoes additional energy losses before reaching the formation. This relationship may be expressed via a derivation of Bernoulli's equation. Bernoulli's equation also adheres to conservation of energy. As a result, the pressure losses or gains may be represented in terms of energy if the individual terms of Bernoulli's equation are integrated with respect to volume.

[0036] This then becomes the backbone of hydraulic fracturing energy analyses, where the effective energy delivered to the formation may be calculated by adding gravitational potential energy to the surface energy component and subtracting all the energy loss contributions (e.g., pipe friction and perforation friction). As noted above, the energy received at the perforations 120 is not all converted to Mode 1 fractures. Some is lost at the perforations 120 and via tortuosity / near wellbore (NWB). The remainder is dissipated as a pressure field generated via Mode 2 and Mode 3 fractures in the vicinity of the Mode 1 fractures.

[0037] By understanding this energy system and applying completion strategies and technologies to reduce system losses such as pipe friction, perforation friction, NWB losses and pressure field propagation, one may lower surface energy consumption while maintaining or improving the effective energy delivered bottomhole. As the ratio of effective energy downhole to surface energy is maximized, so is the ratio of effective energy to operational cost maximized.

[0038] In the example approach of FIG. 1, a predictive data driven process supplements the energy model above by identifying behavioral patterns at each segment identified for the the hydraulic fracturing treatment, by attributing probable causes for the different behavioral patterns and then by creating recommended actions when encountering each behavioral pattern. In some example approaches, the action is recommended based on one or more of previous observed results in the data, on results from physical modeling and on information from subject matter experts. By breaking up the hydraulic fracturing treatments into logical segments, it is possible to create higher level models with reduced uncertainty based on the behavioral patterns observed over multiple segments. By relating the observed behaviors of multiple observations, the model is able to make predictions with higher levels of accuracy. In addition, for real-time operations, the ability to quickly identify relevant behaviors enables fast and effective decisions to be made very quickly, even within the same pumping stage.

[0039] In one example approach, Low Frequency Distributed Acoustic Sensing (LFDAS) may be used in the presence of external pressure gauges to detect DAS behaviors or patterns that are indicative of a pressure communication event. Data captured by the LFDAS sensors and by the external pressure gauges may then be correlated using a physics-based method and / or used to train a machine learning system such as a neural network to operate solely with LFDAS sensors to detect such DAS behaviors in the absence of external pressure gauges. The machine learning process may include supervised and unsupervised learning approaches. In some example approaches, the pressure data may be processed and tagged with events of interest. In some such approaches, the events of interest include deviations from a static base line, deviations from a slowly declining pressure rate as pressure bleeds off into the formation, rate of pressure changes where different rates of change may indicate different events (e.g. an approaching fracture with a slow rate of pressure increase or a fracture intersecting a pressure gauge where the rate of pressure change can be associated with the location of the fracture, sudden drops in pressure during fracturing operations where the pressure drop may be associated with fault activation, or gradual changes to pressure rates as fractures may intersect existing fracture networks). These tags may then be used for supervised machine learning approaches. The tagged events may include events flagged and / or removed / ignored during unsupervised learning where the machine learning model development process identifies similar events and groups them in order to identify features that may then be classified by a subject matter expert and used for supervised learning approaches. These pressure events and features may then be used to train a machine learning model where, for instance, the strain data is used as the measured / applied as input data in cases where external pressure gauges may not be available or coarsely spaced. Data from fracture operations may also be included in the data set and one or more of treatment well pressure, rate, fluid chemical composition, completion parameters, target formation, microseismic data captured with Distributed Acoustic Sensing (DAS) systems / geophone / accelerometers and associated processed data, treatment well flow allocation and uniformity index using DAS, instantaneous and cumulative flow rate per cluster, formation parameters like depth / permeability / porosity / reservoir rock / temperature / pressure and geographical data like GPS position for formation specific models. This approach of building machine learning models where strain may be used as a proxy or a substitute for pressure enables the use of distributed sensing where the distributed fiber may be deployed on demand during fracturing operations.

[0040] In the example shown in FIG. 1, monitoring wells 160.1 and 160.2 (collectively, monitoring wells 160) include a borehole 162. Borehole 162 includes LFDAS sensors 164 and external pressure gauges 166. During fracturing operations, sensors 164 monitor for DAS behaviors or patterns that are indicative of a pressure communication event while pressure gauges 166 monitor for the pressure communication event itself. In one example approach, the data is stored and correlated using a physics-based method and / or used to train a machine learning system such as a neural network to detect pressure communication events in adjacent wells. The use of LFDAS sensors is described in further detail in U.S. patent application Ser. No. 18 / 441,976, filed Feb. 14, 2024, the description of which is incorporated herein by reference.

[0041] FIG. 2 illustrates another example approach to a well stimulation system, according to aspects of the present disclosure. As in the well stimulation system 100 of FIG. 1, well stimulation system 200 includes one or more pumps 114 and a flow network connecting the pumps 114 through a borehole 102 to a reservoir formation 103. In operation, the flow network conveys pressurized fracturing fluid from the pumps to the reservoir formation. In general, fracturing fluids in well stimulation systems are injected into the wellbore at a high pressure in order to convey sufficient energy to a subterranean formation to cause fracturing in the formation. In some example approaches, well stimulation systems induce fluid pressures in a range of 3,000 to 20,000 pounds / square inch (psi) in the fluid injected into the wellbore with total slurry rate in the range of 10 to 200 barrels per min (bpm).

[0042] In the example shown in FIG. 2, well stimulation system 200 includes a frac iron configuration 110 connected through borehole 102 to reservoir formation 103. Frac iron configuration 110 includes frac iron 112 and one or more pumps 114. In the example approach shown in FIG. 2, frac iron 112 receives pressurized fracturing fluid pumped by the one or more pumps 114 from fluid source 116 and conveys the received pressurized fluid through borehole 102 to reservoir formation 103.

[0043] In some example approaches, borehole 102 includes a casing 101 and an opening 105 at surface 104. In some such example approaches, the pressurized fracturing fluid passes through an isolation valve 106 before entering borehole 102 at opening 105. In some example approaches, borehole 102 further includes perforations 120 at certain locations in reservoir formation 103, the pressurized fracturing fluid passing through the perforations 120 to cause fractures in the reservoir location 103.

[0044] In one example approach, well stimulation system 200 includes a computer system 250. In one such example approach, computer system 250 includes a processor 252 and a memory 254. In one example approach, instructions are stored in memory 254 that, when executed by processor 252, allow the processor 252 to control the fracturing process and to capture data representing pressure fields generated via fracturing. In one such example approach, memory 254 further includes instructions stored in memory 254 that, when executed by processor 252, allow the processor 252 to execute a machine learning system that receives training data 256 stored in memory 254 and generates a model for interpreting LFDAS sensor data to detect and measure pressure communication events as discussed above. Processor 252 then uses the model to process LFDAS signals received during stimulation of the reservoir formation 103.

[0045] In one example approach the computer system 250 receives an effective energy model for energy loss in pressurized fracturing fluid passing into and through borehole 102 to a reservoir formation 103 during hydraulic fracturing. The model may be used to determine effective energy delivered to the reservoir formation as a function of surface energy added to the fracturing fluid to raise the fracturing fluid to a high-pressure state; gravitational potential energy gains in the pressurized fracturing fluid as the fluid travels down to the reservoir formation; and energy losses in the pressurized fracturing fluid as the fluid travels down to the reservoir formation. In one example approach, an operator may use computer system 150 to apply the effective energy model to a selected reservoir formation and may select, based on the effective energy model, an operational cost for hydraulic fracturing of the selected reservoir formation. The operator may then use the computer system to control the one or more pumps 114 in frac iron configuration 110 to achieve the selected operational cost.

[0046] In the example shown in FIG. 2, monitoring wells 160.1 and 160.2 (collectively, monitoring wells 160) include a borehole 162. Borehole 162 includes LFDAS sensors 164. During fracturing operations, computer system 250 uses sensors 164 to monitor DAS behaviors or patterns that are indicative of a pressure communication event. In one example approach, the LFDAS signal data is stored and analyzed by processor 252 using the model for interpreting LFDAS sensor data.

[0047] In one example approach, monitoring wells 160 equipped with LFDAS sensors 164 are established throughout the reservoir formation 103 to capture information representing pressure interference between pads and to provide vital information to help optimize treatments for a new pad. Such an approach is less expensive than the combination of LFDAS sensors 164 and pressure gauges 166 discussed in FIG. 1, while presenting accurate detection of the scope of the pressure field around the fracturing event.

[0048] FIG. 3 is a flow diagram illustrating an example method for anticipating and reacting to events that may occur during a segment of a hydraulic fracturing treatment of one or more wells, according to aspects of the present disclosure. The early detection and mitigation of adverse events during hydraulic fracturing treatment would go a long way in reducing the risks and costs of such treatments. At the same time, the early detection and encouragement of positive events at segments of hydraulic fracturing treatment further reduce the risk and cost of such treatments. The challenge in developing a useful data driven model for real time applications is to identify early, key indicators that correlate to specific outcomes (patterns). When these indicators (patterns) are identified, they may be used in real time to predict outcomes and therefore enable decisions to take preventative action. For hydraulic fracturing, real time decisions may involve 1) making specific changes to the current pumping stage to prevent an undesirable outcome; 2) making specific changes to the stages following the current pumping stage, and 3) making changes in the design of hydraulic fracturing treatments for other wells to prevent undesirable outcomes over a broader sample of stages and wells. Since these indicators may be predictive in nature, analysis of recent and historical data may be used during fracture design and planning to take preventative steps to avoid future problems based upon regional observations (patterns) from existing data.

[0049] Pattern recognition may be used to identify the patterns that lead to specific events or outcomes. Pattern recognition is the ability of machines to identify patterns in data, and then to use those patterns to make decisions or predictions using computer algorithms. The benefits of pattern recognition include identification (behavioral, structural, audio, visual patterns may be used for identification), discovery (enable thinking out of the box and seeing things not normally seen), prediction (forecasting data and making predictions), decision-making (making decisions based on reliable, data-based insights) and big-data analytics (patterns may be found in large data sets and may be used to predict future outcomes).

[0050] In one example approach, Explainable Artificial Intelligence (XAI) is used to support pattern recognition when designing hydraulic fracturing treatments. XAI is a set of processes and methods that allow human users to comprehend and trust the results and output created by machine learning algorithms. One goal of XAI is to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the information. Explainable AI eliminates the “Black Box” often associated with Data Analytics. It therefore allows engineers to understand why something is happening and then how to utilize this information to create the most value.

[0051] As data sets grow, it becomes more difficult to extract nuanced information. The combination of AI and pattern recognition in tools such as XAI provides a mechanism that may be used to identify patterns in data that represent early indicators, precursors or warning signs of a probable result, outcome or event. Such early indicators, precursors and warning signs may then be used to increase the probability of positive results and to decrease the probability of negative results. When applied to hydraulic fracturing treatments, a combination of AI and pattern recognition provide a mechanism for modeling each segment of the hydraulic fracturing treatment, gaining insight in how modifications to the treatment at each segment effect that segment, how those in-segment modifications effect subsequent segments in the hydraulic fracturing treatment for that well and how the in-segment modifications of the well effect hydraulic fracturing treatment of other wells in the formation. Identification of early indicators, precursors or warning signs also makes it possible to identify actions that will either increase the probability of a certain event occurring during a segment identified for the hydraulic fracturing treatment or that will decrease the probability of a certain event occurring, depending on the desired outcome.

[0052] In the example shown in FIG. 3, the method includes defining contributing factors that lead to a fracturing event (300). In some example approaches, the events and the contributing factors are associated with one or more fracturing stages. Data analytics and pattern recognition are then applied to fracturing data to identify precursors for each contributing factor (302). This early identification of precursors may be used to anticipate and react to events at each segment identified for the hydraulic fracturing treatment using probabilistic tools. The method then determines, based on the identified precursors, whether the event might occur (304) and proposes actions to change (positively or negatively) the likelihood of the event occurring (306). In one example approach, the method further includes modifying parameters of the hydraulic fracturing treatment to compensate for the detected fracturing event.

[0053] As noted above, in one example approach, the hydraulic fracturing treatment of a well may be broken into two or more segments. In one such example approach, the segments may be the traditional segments for making decisions during hydraulic fracturing jobs. A typical hydraulic fracturing treatment might then be broken down into several sections, with each section providing certain insights based on patterns associated with the segments. While some patterns may be understood or recognized, there may be patterns that are not readily observed. It is here that pattern recognition tools may help identify new patterns that may be used to help predict the outcomes or results within a segment and across one or more hydraulic fracturing treatments.

[0054] The method described above may be used to identify patterns from fracturing data and to determine how the patterns interact to drive events and actions to mitigate events. To date, certain aspects of hydraulic fracturing treatments have been identified as potential sources of patterns in fracturing data. The patterns represent early indicators, precursors or warning signs of a probable result, outcome or event in stages of hydraulic fracturing treatment. The following segments of the hydraulic fracturing treatment have been identified as potential sources of patterns: 1) Fracture Breakdown 420, time to rate or hydraulic efficiency analytics, 2) Pressure Pulse analytics, 3) Quick Step-Down Test analytics. 4) Diverter response, 5) Pressure response to first proppant at perforations and later concentration changes, 6) In well fiber perforation flow distribution, 7) Offset well fiber optic and offset well pressure data for frac hit detection, 8) ISIP and pressure decline analytics, 9) Pressure swings suggesting water quality problems / FR issues, 10) Effective hydraulic fracture energy delivered to the reservoir, and 11) Potential to integrate additional data from other sources, such as early production data, production data, smart drill bit data, and drilling data. Patterns, once identified, may then be analyzed to determine how they are connected or related.

[0055] Segments identified across different fracturing treatments will be discussed next. FIGS. 4-7 illustrate segments of hydraulic fracturing treatment across different wells within a common basin and events that occurred during the respective hydraulic fracturing treatments, according to aspects of the present disclosure. In the examples shown in FIGS. 4-7, treating pressure 402 is pressure at the borehole. Slurry proppant concentration 404 and Borehole slurry proppant concentration 410 are proppant concentrations in pounds / gallon. Slurry rate 408 is in barrels per minute (bpm). Calculated borehole pressure 406 and backside pressure 412 are in psi. In the examples shown in FIGS. 4-7, Borehole slurry proppant concentration 410 lags slurry proppant concentration 404.

[0056] As can be seen in FIGS. 4-7, fracture breakdown (segment 420) occurs in an early segment of hydraulic fracturing treatment. Fracture automation provides an opportunity to perform analytics on breakdown behavior because the pumping process is consistent. In one example approach, patterns in breakdown behavior are used to create a predictive model that may be used immediately in the breakdown segment 420 and in subsequent segments. Parameters identified include perforation cluster efficiency and near wellbore tortuosity and complexity. As seen in FIG. 8, patterns detected in the breakdown segment 420 are used to create a first order model that adds immediate value and that enables immediate recommendations / actions. In one example approach, potential corrective actions include using fine particulates to hold fractures open in areas that normal proppant cannot reach, using 100 mesh sweeps to reduce tortuosity, adjusting proppant ramp. altering perforation strategy to help achieve improved distribution and performing a pressure pulse or quick step-down test.

[0057] Pressure Pulse 428, Quick Step-Down (430, 532 and 736), Diverter Response (532) and Rate Modulation provide other opportunities for evaluating wellbore conditions. Unlike the physics driven models used to calculate parameters, this model relates observed pressure behavior to a probable result: treating pressure / injection rate, step down pressure behavior, pressure pulse behavior or diverter response. Patterns recognized here are used to further strengthen the model for fracture breakdown and to reduce uncertainty. Anomalies are also useful in characterizing hydraulic treatment behavior. In the example shown in FIGS. 4, 6 and 7, inclement weather impacted treatment at segment 424.

[0058] The data driven model described above becomes more robust with a higher degree of certainty with additional information. In some example approaches, for instance, physics models are used to help understand the data driven relationships identified in pressure pulse test and analysis and quick step-down test and analysis. Probable outcomes for these stages include expected pressure response as proppant reaches perforations, early screen out and well productivity performance.

[0059] In one example approach, pressure response when proppant reaches perforations (segments 422 and 426) is used to strengthen validity of breakdown pattern behavior or to change the prediction. Patterns drawn from the pressure response when first proppant reaches the perforations are compared across thousands of fracturing stages to relate to probable outcomes. These patterns may then be related to existing patterns for breakdown 420, pressure pulse 428, step down tests 430 etc. to create a more robust model to predict a probable response or series of probable responses. Possible actions include microproppant, proppant sweeps of 100 mesh or smaller, changes in sand ramp and changes in proppant mesh size / distribution.

[0060] In one example approach, treatment well fiber optic results are used to strengthen relationships developed for near wellbore parameters and perforation cluster efficiency. In one such example approach, perforation efficiency is calculated from one or more of breakdown analytics and the relational model, from pressure pulse and from the quick step-down test. In one example approach, treatment well fiber optic results are also used to generate patterns when proppant reaches the perforations, by identifying clusters that screen out quickly during proppant stages. Key decisions include perforation strategy, degree of limited entry and the use of sand schedules and sand ramps to minimize risk of early partial screen outs at any given point during a fracturing treatment.

[0061] In one example approach, patterns within data detected at offset wells drive the hydraulic fracturing treatment. In one such example approach, the data includes strain and microseismic detection via fiber optics and pressure. In one example approach, fracture hits are detected at the offset well and compared to model predictions of perf cluster efficiency and asymmetric fracture growth. Perf cluster efficiency may be impacted by run-away fractures. Asymmetric fracture growth may be impacted due to high-pressure or low-pressure regions around the treatment wellbore and may reflect reservoir depletion. Far field strain measurements along the wellbore are examined for patterns and information on pressure communication and cross well interference.

[0062] Patterns here may benefit from including regional maps with faults and cumulative production from existing wellbores. Parent well / child well interaction is an ongoing concern. If patterns are recognized, then actions are possible as follows: 1) pump a near field diverter stage, 2) pump a far field diverter stage and 3) Pre-load the depleted wellbore to restore some pressure in the depleted region.

[0063] In one example approach, Instantaneous Shut-In Pressure (ISIP) 430 trends and patterns are used to evaluate stress interaction and near wellbore effects. ISIP trends provide insight into well performance. In addition, such patterns provide means for evaluating stress interference behaviors. Pressure decline behavior 532 following ISIP may also provide correlations to well performance.

[0064] Pressure variations during treatments have been problematic and unexplained for fracture diagnostics. Recent work on water monitoring, however, shows that many of these behaviors may be driven by sudden changes in the water quality. In one example approach, pattern recognition is used to identify operational concerns around water quality changes and to identify when an operator should consider changing friction reducing (FR) concentrations or applied chemistry to respond to these issues. Possible decisions / actions include 1) monitoring water properties continuously, adjusting FR and FR concentration based upon water properties, and 3) flushing equipment with treated water or non-corrosive solution between stages when problem waters are identified.

[0065] Effective fracture energy delivered to the reservoir per acre provides a realist tie to well productivity. This information enables this predictive data driven model to provide realistic information as to how effectively the well is being completed. In one example approach, effective fracture energy is presented in the form of a “Completion Index” along with information as to how this number can be improved. This also provides information regarding well spacing that can be used to talk to our clients. Decisions that are made in real time include 1) Adjusting FR concentration and injection rate to maximize energy to formation, 2) Optimizing stage length, number of clusters and number of perforations per stage, 3) Optimizing treatment size based upon well spacing and energy placed per acre of the drainage volume, and 4) Identifying limiting factors that may cap the maximum effective energy.

[0066] FIG. 8 illustrates a multi-level predictive data driven model, according to aspects of the present disclosure. The multi-level predictive data driven model 801 shown in FIG. 8 enables real time decisions that have an immediate impact on results of current treatment to increase efficiency and reduce risk, that enable improved completion designs in the use of Proppant, Pad, Fluid, and Diagnostic tests, that enable the use of smaller grain proppant where needed for hydraulic efficiency and that tie frac responses to well performance.

[0067] FIG. 9 illustrates another multi-level predictive data driven model, according to aspects of the present disclosure. The multi-level predictive data driven model 901 shown in FIG. 9 enables real time decisions that have an immediate impact on results of current treatment to increase efficiency and reduce risk, that enable improved completion designs in the use of Proppant, Pad, Fluid, and Diagnostic tests, that enable the use of smaller grain proppant where needed for hydraulic efficiency and that tie frac responses to well performance. The model 901 increases recovery of hydrocarbons from the reservoir and enables pad level decisions in real time to attempt to achieve an optimum sequence of operations (drilling and completion), optimum size treatments for order in sequence, optimum completion designs for entire pad and well planning, well placement and well spacing. In one example approach, the model 901 provides tight coupling to well performance to enable reliable predictions, can be supplemented via physics-based models for improved understanding and hypothesis testing and demonstrates continuous improvement at a local or basin level scale.

[0068] FIG. 10 illustrates a computer system that may be used as the computer system 150 in FIG. 1 and the computer system 250 of FIG. 2. Computer system 500 may be employed to practice the concepts, methods, and techniques disclosed herein, and variations thereof. In one example approach, computer system 500 includes a plurality of components in electrical communication with each other, in some examples using a bus 503. The computing system 500 may include any suitable computer, controller, or data processing apparatus capable of being programmed to carry out the method and apparatus as further described herein.

[0069] In one example approach, computing system 500 may be a general-purpose computer, and may include a processor 501 (possibly including multiple processors, multiple cores, multiple nodes, and / or implementing multi-threading, etc.). In one such example approach, computer system 500 includes a memory 507. The memory 507 may be system memory (e.g., one or more of cache, SRAM, or DRAM) or any one or more of the possible realizations of machine-readable media. Computer system 500 also includes bus 503 (e.g., PCI, ISA, PCI-Express, etc.) and a network interface 505 (e.g., ethernet or Fiber Channel).

[0070] The computer may also include an image processor 511 and a controller 515. The controller 515 may control the different operations that can occur in response to data received at sensor inputs 519 and / or calculations based on data received from sensor inputs 519 (such as data from sensors used to sense, for instance, LFDAS sensor data received from LFDAS sensor 164 of FIG. 2) using any of the techniques described herein, and any equivalents thereof, to provide outputs to control pumps 114 and valves in frac iron 110. In some example approaches, controller 515 may communicate instructions to the appropriate equipment, devices, etc. to alter control number and / or the horsepower setting use by the pumps (such as pumps 114 in FIG. 1) that may be utilized in a fracturing procedure. Any one of the previously described functions may be partially (or entirely) implemented in hardware and / or on the processor 501. For example, the functions may be implemented with an application specific integrated circuit, in logic implemented in the processor 501, in a co-processor on a peripheral device or card, etc. Further, realizations may include fewer or additional components not illustrated in FIG. 5 (e.g., video cards, audio cards, additional network interfaces, peripheral devices, etc.). As illustrated in FIG. 5, the processor 501 and the network interface 505 are coupled to the bus 503. Although illustrated as also being coupled to the bus 503, the memory 507 may be coupled to the processor 501 only, to both processor 501 and bus 503 or to processor 501, image processor 511 and bus 503. Controller 515 may be coupled to sensor inputs 519 and to pumps 114 using any type of wired or wireless connection(s), and may receive data, such as measurement data, obtained by sensors inputs 519, LFDAS sensors 164 or provided by the pumps 114 at pumps / valves 521. Sensor inputs 519 may include any of the sensors associated with a wellbore environment, including but not limited to the pressure sensors configured to output signals indicative of pressure level within a frac iron configuration. Controller 515 may include circuitry, such as analog-to-digital (A / D) converters and buffers that allow controller 515 to receive electrical signals directly from one or more of sensor inputs 519.

[0071] Processor 501 may be configured to execute instructions that determine predictive data driven models such as shown in FIGS. 8 and 9. In addition, processor 501 may be configured to provide control over the pressurized fluid fracturing procedures described in this disclosure, and over any equivalents thereof. For example, processor 501 may control operations of one or more pumps 114 being utilized to pressurize a frac iron configuration as part of a fracturing procedure. Control of pumps may include determining a set of predefined pump configurations, wherein a particular one of the predefined pump configurations are assigned to be used during each of a plurality of pressure testing cycles, and providing output signal, for example to controller(s) located at the pumps 114, to configure and control the operations of the pumps during the duration of the procedure according to the predefined pump configuration that is to be applied to that particular pressure testing cycle.

[0072] With respect to computing system 500, basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. In some examples, memory 507 includes non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks (DVDs), cartridges, RAM, ROM, a cable containing a bit stream, and hybrids thereof.

[0073] It will be understood that one or more blocks of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, may be implemented by program code. The program code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable machine or apparatus. As will be appreciated, aspects of the disclosure may be embodied as a system, method or program code / instructions stored in one or more machine-readable media. Accordingly, aspects may take the form of hardware, software (including firmware, resident software, micro-code, etc.), or a combination of software and hardware aspects that may all generally be referred to herein as a “circuit,”“module” or “system.” The functionality presented as individual modules / units in the example illustrations can be organized differently in accordance with any one of platform (operating system and / or hardware), application ecosystem, interfaces, programmer preferences, programming language, administrator preferences, etc.

[0074] Computer program code for carrying out operations for aspects of the disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as the Java® programming language, C++ or the like; a dynamic programming language such as Python; a scripting language such as Perl programming language or PowerShell script language; and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on a stand-alone machine, may execute in a distributed manner across multiple machines, and may execute on one machine while providing results and or accepting input on another machine. While depicted as a computing system 400 or as a general-purpose computer, some embodiments can be any type of device or apparatus to perform operations described herein.

[0075] The techniques described above utilize the vast amount of data contained within fracturing databases to identify relevant patterns to build predictive capabilities. Fracture diagnostics data as well as physical modeling may be used to enhance the capability (verification of what causes or generates any given pattern). Pattern recognition of the early, formation break down portion of the frac job provides a unique starting point for analysis that allows patterns recognized in existing data to: predict the future responses in a current treatment, recommend actions to address probable problems, and predict responses for the next stages. As the treatment progresses and additional data is collected, new observed behaviors are used to further improve the predictability of the model. Predictive capability may allow anticipation of future behavior and identify potential early actions and preventative steps. The predictive data driven method further allows pull through of additional services for more complete diagnostic solutions.

[0076] Various modifications to the implementations described in this disclosure may be readily apparent to persons having ordinary skill in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

[0077] Additionally, various features that are described in this specification in the context of separate implementations also can be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also can be implemented in multiple implementations separately or in any suitable subcombination. As such, although features may be described above as acting in particular combinations, and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0078] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one or more example processes in the form of a flowchart or flow diagram. However, other operations that are not depicted can be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the illustrated operations. In some circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. Additionally, other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results.EXAMPLE IMPLEMENTATIONS

[0079] Implementation #1: A method, comprising identifying, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective segment; applying pattern recognition to fracturing data associated with one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event; determining, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, whether the selected fracturing event is likely to occur in the wellbore; and modifying parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

[0080] Implementation #2. The method of Implementation #1, wherein the fracturing event and the at least one contributing factor are associated with at least one of the two or more fracturing segments.

[0081] Implementation #3. The method of Implementation #1, wherein applying pattern recognition to fracturing data to identify at least one precursor for each of the contributing factors includes applying data analytics to the fracturing data.

[0082] Implementation #4. The method of Implementation #1, further comprising directing at least one action to change the likelihood of the selected fracturing event occurring.

[0083] Implementation #5. The method of Implementation #4, wherein directing the at least one action includes directing the at least one action that increases the likelihood of the selected fracturing event occurring.

[0084] Implementation #6. The method of Implementation #4, wherein directing the at least one action includes directing the at least one action that decreases the likelihood of the selected fracturing event occurring.

[0085] Implementation #7. The method of Implementation #4, wherein the fracturing event and the at least one contributing factor are associated with the one or more segments.

[0086] Implementation #8. The method of Implementation #4, wherein identifying the at least one precursor for each of the at least one contributing factor includes applying at least one of data analytics or pattern recognition to fracturing data to identify the at least one precursor for each of the contributing factors.

[0087] Implementation #9. A well stimulation system, comprising at least one pump configured to pressurize fracturing fluid received from a fluid source and to direct the pressurized fracturing fluid down a borehole to a reservoir formation; one or more monitoring wells in the reservoir formation and in proximity to the borehole, each monitoring well including one or more sensors; and a computer system having a processor and a memory, the memory including instructions that, when executed by the processor, cause the processor to identify, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective stage or segments within a stage, apply pattern recognition to fracturing data associated with the one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event, determine, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, if the selected fracturing event is likely to occur in the wellbore, and modify parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

[0088] Implementation #10. The well stimulation system of Implementation #9, wherein the instructions that apply pattern recognition to fracturing data associated with the one or more monitoring wells to identify at least one precursor for each of the contributing factors further include instructions that, when executed by the computer, cause the computer to apply applying data analytics to the fracturing data to identify the at least one precursor for each of the contributing factors.

[0089] Implementation #11. The well stimulation system of Implementation #9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action to change the likelihood of the fracturing event occurring.

[0090] Implementation #12. The well stimulation system of Implementation #9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that increases the likelihood of the event occurring.

[0091] Implementation #13. The well stimulation system of Implementation #9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that decreases the likelihood of the event occurring.

[0092] Implementation #14. The well stimulation system of Implementation #9, wherein identifying the at least one precursor for each of the at least one contributing factor includes applying at least one of data analytics or pattern recognition to fracturing data to identify the at least one precursor for each of the contributing factors.

[0093] Implementation #15. A non-transitory computer readable medium configured to store instructions that are executable by a processor, the instructions comprising instructions to identify, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective stage or segments within a stage; instructions to apply pattern recognition to fracturing data associated with the one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event; instructions to determine, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, if the selected fracturing event is likely to occur in the wellbore; and instructions to modify parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

[0094] Implementation #16. The non-transitory computer readable medium of Implementation #15, wherein the events and the contributing factors are associated with one or more of the segments.

[0095] Implementation #17. The non-transitory computer readable medium of Implementation #15, wherein the instructions that identify precursors for each contributing factor further include instructions that, when executed by the computer, cause the computer to apply data analytics to the fracturing data to identify the at least one precursor for each of the contributing factors.

[0096] Implementation #18. The non-transitory computer readable medium of Implementation #15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action to change likelihood of the selected fracturing event occurring.

[0097] Implementation #19. The non-transitory computer readable medium of Implementation #15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that increases likelihood of the selected fracturing event occurring.

[0098] Implementation #20. The non-transitory computer readable medium of Implementation #15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that decreases likelihood of the selected fracturing event occurring.

Claims

1. A method, comprising:identifying, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective segment;applying pattern recognition to fracturing data associated with one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event;determining, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, whether the selected fracturing event is likely to occur in the wellbore; andmodifying parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

2. The method of claim 1, wherein the fracturing event and the at least one contributing factor are associated with at least one of the two or more fracturing segments.

3. The method of claim 1, wherein applying pattern recognition to fracturing data to identify at least one precursor for each of the contributing factors includes applying data analytics to the fracturing data.

4. The method of claim 1, further comprising directing at least one action to change likelihood of the selected fracturing event occurring.

5. The method of claim 4, wherein directing the at least one action includes directing the at least one action that increases likelihood of the selected fracturing event occurring.

6. The method of claim 4, wherein directing the at least one action includes directing the at least one action that decreases likelihood of the selected fracturing event occurring.

7. The method of claim 4, wherein the fracturing event and the at least one contributing factor are associated with the one or more segments.

8. The method of claim 4, wherein identifying the at least one precursor for each of the at least one contributing factor includes applying at least one of data analytics or pattern recognition to fracturing data to identify the at least one precursor for each of the contributing factors.

9. A well stimulation system, comprising:at least one pump configured to pressurize fracturing fluid received from a fluid source and to direct the pressurized fracturing fluid down a borehole to a reservoir formation; andone or more monitoring wells in the reservoir formation and in proximity to the borehole, each monitoring well including one or more sensors; anda computer system having a processor and a memory, the memory including instructions that, when executed by the processor, cause the processor to:identify, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective stage or segments within a stage;apply pattern recognition to fracturing data associated with the one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event;determine, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, if the selected fracturing event is likely to occur in the wellbore; andmodify parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

10. The well stimulation system of claim 9, wherein the instructions that apply pattern recognition to fracturing data associated with the one or more monitoring wells to identify at least one precursor for each of the contributing factors further include instructions that, when executed by the computer, cause the computer to apply applying data analytics to the fracturing data to identify the at least one precursor for each of the contributing factors.

11. The well stimulation system of claim 9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action to change likelihood of the fracturing event occurring.

12. The well stimulation system of claim 9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that increases likelihood of the event occurring.

13. The well stimulation system of claim 9, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that decreases likelihood of the event occurring.

14. The well stimulation system of claim 9, wherein identifying the at least one precursor for each of the at least one contributing factor includes applying at least one of data analytics or pattern recognition to fracturing data to identify the at least one precursor for each of the contributing factors.

15. A non-transitory computer readable medium configured to store instructions that are executable by a processor, the instructions comprising:instructions to identify, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective stage or segments within a stage;instructions to apply pattern recognition to fracturing data associated with one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event;instructions to determine, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, if the selected fracturing event is likely to occur in the wellbore; andinstructions to modify parameters of the hydraulic fracturing treatment to compensate for the selected fracturing event.

16. The non-transitory computer readable medium of claim 15, wherein the selected fracturing event and the respective contributing factors are associated with one or more of the segments.

17. The non-transitory computer readable medium of claim 15, wherein the instructions that identify precursors for each contributing factor further include instructions that, when executed by the computer, cause the computer to apply data analytics to fracturing data to identify the at least one precursor for each of the respective contributing factors.

18. The non-transitory computer readable medium of claim 15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action to change likelihood of the selected fracturing event occurring.

19. The non-transitory computer readable medium of claim 15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that increases likelihood of the selected fracturing event occurring.

20. The non-transitory computer readable medium of claim 15, wherein the instructions further comprise instructions that, when executed by the computer, direct at least one action that decreases likelihood of the selected fracturing event occurring.

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