HYDRAULIC FRACTURING OPERATION PLANNING USING DATA-DRIVEN MULTI-VARIATE STATISTICAL MACHINE LEARNING MODELING

The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site. In other aspects, the methods can be used to revise an existing HF job plan in real-time, such as after a treatment cycle, a pumping stage, or a time interval.

TECHNICAL FIELD

This application is directed, in general, to designing a hydraulic fracturing stimulation treatment job plan, and, more specifically, to utilizing multiple data sources and machine learning to design and modify a hydraulic fracturing stimulation treatment job plan.

BACKGROUND

Understanding the impact of stimulation and completion variables, i.e., features, on well production is important to improve the efficiency of hydraulic fracturing (HF) jobs. Some examples of stimulation features include fluid type, fluid volume, additive type and concentration, proppant type, size, concentration and mass, and pumping rate. Some examples of completion features include perforated length, number of stages, and number of perforation clusters. Additional features affecting well production include operator production practices and reservoir or spatial characteristics. The large number of features for a single well and across numerous wells in a region, location, basin, or county presents the need for large scale complex mathematical and statistical modeling. The relationships and interactions between these and other additional features are neither simple nor linear. Simple bilinear or multivariate linear models may not adequately explain the effect of the multiple input features on the well productivity outcomes. Well productivity and cost, together with efficiency and cost of the fracture stimulation treatment, determine the economic viability of the well and return on investment for the well operator.

Current approaches in modeling well production involve incorporating controllable (such as stimulation) features and uncontrollable (such as well location) features in the same statistical model. Often, well location, which can serve as a proxy for reservoir properties and operator practices, dominates the model accuracy and percentage of variance explained in the model error. Therefore, teasing out the effect of controllable features on production outcomes can be difficult. A more accurate modeling technique would be beneficial to predicting production outcomes using the many input feature types.

DETAILED DESCRIPTION

In the oil and gas industry, methods have been developed to estimate well characteristics using various diverse modeling approaches. A well can be one of various types, such as an oil or natural gas production well. Well characteristics can include, for example, productivity, completion efficiency, drilling direction, and many other characteristics. These modeling approaches encompass and utilize concepts from many different technical domains, such as numerical method, physics-based modeling, earth and reservoir modeling, chemical engineering, and data driven statistical and mathematical modeling.

Each of these modeling approaches relies on unique assumptions, concepts, hypotheses, and parameters from that technical domain to estimate well characteristics. Attempts have been made to incorporate some of these approaches together to obtain a combined estimate that may increase accuracy in estimating the well characteristic of interest. Most of the combination techniques have been heuristics or rule based and may involve manual or subjective processing of information from different domains.

The modeling, simulation, or prediction insights gained from the various techniques can be useful to well engineers. The model predictions can be utilized prior to the start of development of a well site. The information can be used to build or modify a job design plan, i.e., a stimulation treatment operation plan. Once a well site has been started, other models can be used to monitor the progress of the well development and provide insight into modifications or changes to the job design plan that can provide a benefit. The benefit can be decreasing the time frame to hit a production goal, decreasing the cost to develop and work the well during the development and production phases, increasing the projected barrel of oil equivalent (BOE), increasing the net present value, return on investment, rate of return, or other economic indicator for the well following completion and production for a period of time, and impacting other key performance indicators or measures (KPI).

For example, conventional pumping decisions during hydraulic fracturing operations are generally not governed by data and statistics. Many decisions are subjective to the subject matter expertise (SME) of the fracturing crew. The decision-making process is geared towards executing a pre-determined pumping schedule (such as pumping 100,000 pounds (lbs.) of proppant in 30,000 gallons (gal) of fluid at 75 barrels per minute (bpm) for each treatment) or reacting to catastrophic deviations from expected outcomes (such as lowering the flow rate during a pressure increase caused by a screenout). This fixed and reactive approach does not optimize job results. It seeks to complete the job as designed, with little mechanism for accepting feedback during the job and modifying the design to enhance the outcome.

The techniques and models of the disclosures herein can be applied to various types of well sites. This application will use the hydraulic fracturing (HF) of horizontal wells as the well type to demonstrate the disclosures herein. The same methods could be applied to vertical, deviated, or otherwise oriented wells. Conventional approaches to HF design require a large number of parameters as input that is usually unknown or not reliable. There can be over 10,000 parameters, i.e., features, that can be input into the techniques and models. Some of the features can be well parameters, reservoir parameters, HF stimulation parameters, environment factors (such as geological formations, ground stability, offshore vs onshore, and other factors), equipment factors (such as the type of well equipment and monitoring equipment in use at a well site, and other equipment factors), operational feasibility (such as the ability to bring certain equipment on site, availability of SME, potential impact on neighboring locations, and other operational feasibility factors), and legal factors (such as laws in effect at the well site location, and other legal factors).

The significance and impact of each of these features on the KPIs can vary such that there can be 10, 100, or another number of features that are important for the analysis needed. The well engineers must often estimate several parameter values, which may add significant bias and uncertainty in the modeled predictions.

One dataset collected during HF pumping is time-series data containing information regarding fluids, proppants, chemicals, pressure, and other on-site field measurements and observations. This dataset can be leveraged for various applications and analysis. The HF time-series data captures information at regular and irregular time intervals. A grouping of time-series data can be an HF event. An HF event can be a treatment cycle, i.e., a job stage, pumping stage, diversion cycles, minifracs, step-up, step-down, sand slugs, screenouts, water hammer, and other events. Each HF event captured can have different characteristics, such as shape, scale, magnitude, and other characteristics. Currently, there is no defined process to identify and detect these events. Current industry practice includes manual detection or marking of the events by a user that introduces subjective variability, or a user guided method in a software system. These methods are not automated nor provide consistent and robust results.

This disclosure describes several enhancements to the process of determining a HF job design prior to beginning work at a well site and during active conducting of an HF stimulation on a well site. One aspect provides for data driven statistical modeling and machine learning algorithms that can be utilized to perform the modeling and generate prediction outcomes. Other aspects provide for a systematic, scalable, and robust framework of combining different modeling and estimation techniques to provide an enhanced estimate of the well characteristics. For example, well productivity can be estimated from earth models based on geological and reservoir information. It can also be estimated based on physics-based modeling techniques that rely on well geometry, fracture topology, and other factors.

Various data sources can contain unique and common information and parameters about the well site. When more than one data source is available, it would be desirable to join the different data sources to generate a combined data set for use in prediction and estimation of the well productivity. Modeling of predicted well production, or other KPI, can involve multiple different modeling assumptions and techniques, ranging from straightforward to very complex. One aspect of this disclosure can use an ensemble modeling technique. Ensemble modeling can be used to build a systematic way to incorporate diverse modeling techniques, methods, and data sources. This can be used to provide a more accurate estimate of well characteristics since the estimates can encompass a larger variety of information and parameters than analyzing each data source independently.

In another aspect, an automated detection of HF events based on HF time-series data can be generated. In addition to the presence or absence detection of an HF event, the start and end times of the HF event (HF event timeframe) can be identified. The HF event information can be utilized as modeling features for further estimations and predictions for the well site job operation plan, i.e., job plan.

From a well productivity perspective, identifying HF events can be a step in building a data driven model to determine the effectiveness of the stimulation job plan. From the time markers, aggregated values, such as the average pump rate, total proppant mass, fluid volume, and more complex derived properties, such as perforation and near wellbore friction, and other factors, can be determined or computed for use in the modeling process. From a surface efficiency perspective, the HF events can serve as operational metrics, such as the number of screenouts per 1000 treatments and planned sand slugs vs the actual number. In addition, an automated post job report generation after a HF treatment can be an optional component.

In this aspect, a machine learning or data drive statistical modeling approach can be utilized, with the time-series pumping data, such as treating pressure, slurry rate, proppant concentration, user defined event flags, and other factors, to characterize the HF event (such as treatment, cycle, mini-frac, and other event types) and derived parameters (such as fracture gradient, slopes, averages, weighted averages, and other derived parameters). This information can be utilized to build a well site job plan.

Utilizing real-time or near real-time information, monitoring and improvements of the well site job plan can be implemented. Real-time, for the purposes of this disclosure, means information and data collected or received from a well site equipment or monitoring equipment at a well site within a time interval, for example, 1 second, 2 hours, or another time interval. Real-time processes and events as used herein is inclusive of near real-time processes and events. Non-real-time collected data and information is received from stored data or historical data and may not be sourced from the target well site.

In another aspect prior to implementation of a job plan, a multi-stage data analytics method to maximize well productivity can be used to develop the job plan. The analytics method can automate processing of large-scale data from a variety of public and proprietary sources to prepare and extract standard and novel parameters. It can decouple spatial variability, i.e., uncontrollable features, from stimulation parameters, i.e., controllable features. It can also utilize uncharacteristic design parameters that capture variation in job designs along the lateral section of horizontal wells for building predictive machine learning models. The analytics method can also identify and recommend optimal and customized design features, i.e., variables, for HF job plans.

These various aspects can be utilized separately or in combination to maximize value for a well site production. Data driven decisions prior to, or in real-time can be determined to satisfy customer defined KPIs by predicting likely outcomes and recommending well site job plan design changes.

In one aspect of this disclosure, training a model can be conducted to lead to higher quality outputs of the various respective models. First, a determination of manual or previous machine learning based identification of HF events from historical or other completed HF treatments can be collected into a dataset. The dataset can be one or more datasets, or an ensemble dataset. The dataset can be split into two or three portions. A training portion, a validation portion, and an optional testing portion. The training portion can be used to train a machine learning model. The validation portion can be used to validate the training model.

After training and validation have been completed, various models can be run against the dataset and the model can be selected that yields the outcomes desired by the customer KPIs. The testing portion can be used for this analysis. This model can then be used for the well site job plan. This type of HF event detection methodology is used in the industry for fault and anomaly detection with respect to equipment failure, such as drilling pipe stuck events and electrical submersible pump (ESP) failure. It is not being used for a machine learning based approach to solve multi-variate HF time-series event detection and its timeframe determination situations.

Turning now to the figures,FIGS. 1 to 2demonstrate example well systems to which the various aspects disclosed herein can be applied.FIGS. 3 to 5demonstrate examples of applying the disclosure to the pre-job design phase, i.e., non-real-time application. HF job plans can be developed and modified based on the modeling demonstrated in these figures.FIGS. 6 to 7demonstrate examples of applying the disclosure to real-time job executions. HF treatments in progress can be monitored and revised models can be generated that can confirm the HF job plan or recommend changes to the HF job plan.FIGS. 8 to 11demonstrate alternate techniques to model various treatments and job design parameters, specifically around response features, such as pressure, flow distribution, and fracturing dimensions.FIGS. 12 to 13demonstrate techniques to detect and monitor response features and events within a well system.FIGS. 14 to 18demonstrate variations of ensemble modeling.

FIG. 1is an illustration of a diagram of an example well site location100. Well site location100can be the target well site for the HF job plans that are designed or modified using the methods disclosed herein. Well site location100includes well equipment110located at surface105and well controller108located proximate to well equipment110. Well equipment110can include, for example, drilling rig, surface production wellhead, and other equipment to construct and produce the well. Well controller108can include, for example, drilling equipment, completions equipment, pumping equipment, monitoring equipment, and production equipment. Prior to the stimulation treatment, well controller108may also include computing equipment to execute the job plan and to perform the modeling as disclosed. Alternatively, the modeling and data computing equipment can be located a distance from well site location100, such as in a data center, server, or other remote location.

Extending below well equipment110is a wellbore120. Wellbore120bends and becomes more aligned approximately horizontally, as shown by horizontal wellbore122. A fracture125is shown for demonstration of a location of a planned hydraulic fracture to be created by a planned HF job design.

FIG. 2is an illustration of a diagram of an example multiple well system200. Multiple well system200includes a well site201, a completed well site202, and a computing system203. Well site201includes well equipment210located at surface location205and well controller208. Extending below well equipment210is a wellbore220. Wellbore220is a horizontal wellbore designed for fracturing operations.

Completed well site202includes a completed well230and well controller and monitor234. Extending below completed well230is a wellbore238. Wellbore238is a horizontal wellbore designed for fracturing operations. Interior to wellbore238can be a sensing device240, for example, an acoustic sensor or fiber optic cable. Sensing device240can be used to gather time-series feature data and response feature data that can be used as inputs to the various modeling algorithms described herein. Other devices, on surface and in the wellbore, can be used as well to gather the time-series and response feature data.

Computing system203can include one or more computing devices, cloud storage and processing systems, distant data centers, on-site data centers, servers, and other types of computing systems. A computing device can be a server, laptop, smartphone, dedicated well equipment, distributed processing system, and other types of computing devices. Computing system203can include a HF design system250(seeFIG. 5) and one or more data sources252. HF design system250can execute the methods, algorithms, and techniques described herein. Well controller and monitor234can relay surface and downhole conditions and measured values before, during, and after the fracture stimulation treatment to the HF design system250.

Data sources252can be located with HF design system250, be located proximate to HF design system250, or be located a distance from HF design system250. Data sources252can be stored in a database, a hard drive, CD, DVD, USB, memory, server, data center, cloud storage, and other storage mediums and locations. Data sources252can be public data sources, private or confidential data sources, proprietary data sources, historical data sources, and other data source types.

HF design system250can be communicatively coupled to the data sources252, and communicatively coupled to one or more well sites, such as well site201and completed well site202, where such communicative coupling is shown by the dotted lines. The information collected from the data sources252and from sensing device240can be used by the HF design system250to build a new or revised HF job plan. Depending on the model being built, the data sources252or the sensing device240data can be optional. HF design system250can execute the methods and algorithms to build models prior to a job design being executed, or in real-time such as after a treatment cycle.

In multiple well system200, data gathered from one well, such as the completed well202can also be used as input data to the HF design system250when building or revising a HF job plan for well site201. Other combinations are possible as well. For example, there can be more than two well sites at a location, where one or more of those well sites are contributing data to HF design system250.

FIG. 3Ais an illustration of a diagram of an example data preparation flow301. Data preparation flow301handles large scale ingestion, handling, cleaning, and processing of data from a variety of public and proprietary data sources. The process can include joining and validating data across different sources, performing statistical and descriptive analysis on the data, and identifying outlier data elements.

There can be one or more data sources, where three data sources are modeled for demonstration purposes as310a,310b, and310m. The data sources310can be public data sources, for example, Drillinginfo, IHS, RigData, Rystad, RS Energy, FracFocus, national or state government oil and gas agencies, and other public sources. Data sources310can also be proprietary data sources, for example, HF job data from previous HF well sites, customer data, sales data, well cuttings, core, log, survey, and other data from the HF or other wells, seismic and other geology and reservoir data, and other proprietary or confidential data sources. The relevant data from each of the selected data sources310can be ingested, cleaned, and processed, as shown in flow312. A series of quality checks and outlier removal processes can be applied to improve data quality at various processing steps. Imputation can be used to fill in missing values. Outlier data can be excluded. After the data has been processed and passed quality checks, the data from disparate sources can be combined together based on the common information available between the data sources310, for example, well API (American Petroleum Institute unique well identifier), well name, well number, and other data elements.

FIG. 3Bis an illustration of a diagram of an example feature engineering flow302. Feature engineering flow302includes extracting one or both standard and engineered features for use within the model building stages. The engineered features can be mathematical or statistical transformation of data, statistical or mathematical computation of derived features, SME knowledge driven, logic or rule-based features, or a combination of features across different data sources.

The feature engineering flow302takes as input the processed data from data preparation flow301. The data can be analyzed and categorized. Standard features320include traditional and commonly available information about the well, for example, the well name, well number, API, spatial location, lateral length, measured depth, true vertical depth, and other information.

Logic based features321can be computed. Logic based features321can be computed using statistical or mathematical transformations325or computations326of standard features320or other types of features. The computation and usage of these designed features are distinctive to this disclosure. For example, a computed feature can be a toe-to-heel or stage-to-stage variation of proppant amount, fluid volume, maximum proppant concentration, proppant mesh size, pad fraction, fluid type, and other computed features. SME knowledge-based features322can be identified for the targeted well site. For example, for a new location, SME geological knowledge can be utilized since neighboring wells' data may not be available for the modeling process.

Features can be combined across different data sources as shown in flow327. This can assist the analysis when certain features are at the well site level and other features are at the stage level, i.e., treatment cycle. The treatment and pumping stage level features can be statistically aggregated at the well level while maintaining enough information to build a well level model. The collective output of feature engineering flow302can be a dataset or repository of well level features329.

FIG. 3Cis an illustration of a diagram of an example production data processing flow303. Data processing flow303includes processing and cleaning of the production data to result in a cleaned production metric which can be used as a target variable for modeling. A type curve can be fitted, such as using the Arps equation or similar, to reduce noise in production data, overcome production reporting issues, and estimate production values per well, for example cumulative 180-day production outcomes and estimated ultimate recovery targets.

Depending on the region, the production data source330may have limited availability in the public domain or it may not be trustworthy. The production data can be ingested, cleaned, and processed, as shown in flow332. One or more quality checks can be applied. Imputation can be used to fill in missing values. Outlier data can be excluded. In the decision flow334, a determination can be made whether type fitting would be beneficial for the ingested data. If ‘Yes’, then a type curve, shown in flow336, or another type of smoothing method can be applied to reduce noise and correct for erroneous production data, or to identify wells for which the production data is unusable and should be removed from the dataset. An appropriate target production value, i.e., KPI, is computed from the smoothed or fitted data. The KPI can be, for example, a 30-day, 90-day, 180-day, 365-day, a maximum initial production, an estimated ultimate recovery (EUR), or other rolling cumulative production outcome. In flow338, the KPI can then be combined with other well level features to generate a dataset to be used in the modeling steps.

FIG. 3Dis an illustration of a diagram of an example statistical or machine learning predictive modeling flows304. This can include a single predictive model or multi-stage model consisting of geospatial and predictive models. Modeling flows304include a first step to determine a feature selection. After joining the production data, as described inFIG. 3C, with well-level features, as described inFIG. 3B, a feature selection can be performed to reduce dimensionality of feature space, find relevant input features, or build simpler models using small number of features. Features are selected to be applied within one or both geospatial and predictive models. In some aspects, a single model can be built for the selected features. The geospatial modeling includes building a predictive geospatial model for well productivity based on spatial location parameters using mathematical techniques, such as universal kriging, decisions trees, or other machine learning methods. This can decouple the effect of the well location, which is an uncontrollable feature, from the stimulation parameters, which are controllable features. The predictive modeling includes using stimulation parameters along with additional relevant features to build a predictive model for well productivity. Information can be shared between the geospatial and predictive models.

The output of the feature engineering flow302is represented by the flow329. The output of the production data processing flow303is represented by the flow338. The combined dataset of production and well level features is represented by flow342. The output of the model data is flow344. From the output of the model data flow344, feature selection can be performed as shown in flow346.

The selection process in flow346can be achieved through many standard techniques and algorithms, for example, forward step-wise selection, backward step-wise selection, correlation analysis, lasso regression, ridge regression, elastic net regression, other techniques, and a combination of these techniques. Features can also be selected based on domain or SME knowledge. Flow346can select the same, different, or some same and some different features for each of the subsequent modeling steps, such as the output for the geospatial model flow348and the output for the predictive model flow349. Flow346can also enable dimensionality reduction of the feature space along with decoupling the impact of relevant features from non-relevant features in predicting well production output.

In the geospatial modeling flow350, the previously prepared data can be divided into three separate classes: (1) training data, (2) validation data, and (3) test data. Training data can be leveraged to build one or more models. Validation data can be used to evaluate the model performance and select a modeling algorithm. A geospatial model can be built using features that characterize well spatial location, either two or three dimensionally, and well completion parameters. Techniques such as universal kriging, random forest, and other machine learning methods can be used to build the models. The test data can be used to ensure model robustness and predictive power. The model resulting in the highest accuracy can be selected as the geospatial model to be used for the output of well production predictions flow364.

In the predictive modeling flow360, the data can again be divided into three classes (train, validate, test). The division of the data can be the same or different from the geospatial model division of data. The KPI can be the residual production (predicted well production output from the geospatial model) or it can be the actual well production output. A predictive model can be built using stimulation parameters, therefore decoupling the effect of location from the HF design parameters. One or more statistical and mathematical modeling techniques can be tested to build the model, for example, linear regression, non-linear regression, support vector machine, random forest, gradient boost, neural networks, deep learning, and other techniques. The algorithm resulting in the highest accuracy or with the best fitted data can be selected as the final predictive model. The evaluation of the algorithm and model performance can be achieved through cross validation, such as 5-fold or 10-fold cross validation. The selected model can output the predictive model well production predictions in the output of well predictions flow364.

FIG. 3Eis an illustration of a diagram of an example HF job design flow307. HF job design307includes building an optimized model based on a list of input parameters, such as the parameters used for the geospatial and predictive models, to maximize or optimize well production in regard to a designated KPI metric. The HF job design307process can generate one or more potential job designs, where each optimizes a pre-defined objective function, or KPI, under given constraints for a given set of parameters. The potential job designs can then be validated under operational constraints and feasibility. The final recommended job design can be selected after the analysis is completed.

A pre-determined well can be selected or a well site can be identified for which the HF job design will be built, and is represented by flow370. A list of features is selected for optimization in flow372. These features can be selected from the features used to build the geospatial model and the predictive model. A set of constraints can be selected, as represented in flow374. The constraints can be pre-defined or customized by a user of the process. The constraints can include operational feasibility, cost factors, and other constraint types.

An optimization algorithm, for example, a genetic algorithm, pattern search, differential evolution, and other types, can be used for the list of features within the provided constraints to generate multiple iterations and scenarios to optimize the objective function, as shown in flow376. The most optimized scenario or one or more potential scenarios can be considered as potential designs for the HF job, shown as flow378. Users of the process can provide their own estimates and ranges for one or more features in the list to optimize. This can create custom scenarios, shown as flow380. Flow382encapsulates the previously presented statistical or machine learning predictive modeling flows304. In flow382, the resulting HF designs can be input to flow304to generate predicted well production or other KPI based on the scenario parameters. The output of382is the resulting output from the output of well predictions flow364.

The output from the analyzation process, flow384, can then be evaluated against the constraints to test that the constraints are satisfied, as shown in decision flow386. If the constraints are not satisfied, the process can loop back to flows378and380to adjust the scenarios or to select a different scenario. If the constraints are satisfied, the design models can be converted into an HF job plan, including pumping schedule level job parameters, as shown in flow388. The HF job plan can be output and utilized by field engineers to implement the HF job design, as shown in flow390.

FIG. 4is an illustration of a flow diagram of an example HF job design method400. Method400starts at a step401and proceeds to a step405. In the step405, the process can prepare the first data set as shown inFIG. 3A. One or more datasets can be ingested, cleaned, and processed, rendering the datasets available for further analysis within the method400. Proceeding to a step410, engineering of features can be conducted, as shown inFIG. 3B. Features can be identified from the datasets, and additional features can be computed or transformed from the datasets. In addition, SME knowledge can be applied to identify additional features.

In a step415, a second set of data, i.e., a production data set, can be processed, as shown inFIG. 3C. Production data from various sources can be ingested, cleaned, and processed. The data can then be analyzed to determine if a fitting or smoothing algorithm should be applied to the data. Outliers can be removed if they do not fit the curve. In a step420, data sets from steps410and415can be combined to create a larger model data set, as shown inFIG. 3D. The design parameters can be selected from the combined datasets for each of the models that will be built. The selected features can be the same, different, or overlap between the various models that are built.

Proceeding to a step425, zero or more geospatial models can be created using the selected features from step420that were selected for geospatial modeling (seeFIG. 3D, elements348and350). Separately, in a step430, zero or more predictive models can be created using the selected features from step420that were selected for predictive modeling (seeFIG. 3D, elements349and360). Various techniques and algorithms can be utilized to build the models. At least one of the steps425and430should be selected, with the other step being an optional selection.

After steps425and430have completed, the method400proceeds to a step435. In the step435, the various geospatial and predictive models can be evaluated against various criteria and constraints (seeFIG. 3E). Models can be optimized for cost, short term production outputs, long term production outputs, and other KPIs. The model that best fits the constraints and KPI goals provided can be selected and converted into a HF job plan. The HF job plan can then be utilized by well engineers and operators, and by well site equipment. The method ends at a step450.

FIG. 5is an illustration of a block diagram of an example HF design system500. HF design system500includes a HF design processor505. HF design processor505includes a receiver510, a storage512, a data analyzer520, a feature selector522, a modeler524, a HF processor526, and a communicator514. Data from various sources, public sources and confidential/proprietary sources, can be received by receiver510. Receiver510can also receive various user inputs, such as SME knowledge, domain knowledge, user defined constraints, and other information. Receiver510can be a conventional communication system, such as a network connection, wireless connection, wired connection, or other communication types. The data can be stored in storage512. Storage512can be a conventional storage medium, for example, a database, a hard drive, CD, DVD, USB memory, a cloud storage, a server, a data center storage area, and other storage medium types.

HF processor526can execute one or more applications and processes to perform the methods as described herein. HF processor526can manage the data stored in storage512, and issue instructions to data analyzer520, feature selector522, and modeler524to complete the method steps. Data analyzer520can clean and process the data received by receiver510and store the processed data back into storage512. Feature selector522can select features to be used in the modeling processes from the data stored in storage512. Modeler524can generate geospatial and predictive models using the features selected in feature selector522and data stored in storage512.

Communicator514can communicate the HF job plans selected by the HF processor. This can typically be the most optimized HF job plan based on the constraints provided. In other aspects, more than one HF job plan can be provided. Communicator514can communicate with a user, such as using a monitor, display, mobile device, or printer, and the communicator514can communicate with other systems, such as a storage medium, data center, server, cloud-based system, well site equipment, and other computing systems. The communication can be via a conventional communication means, such as a network protocol, wireless connection, wired connection, directly to a storage medium, and other communication types.

HF design system500is demonstrating an example system to implement the methods described herein. HF design system500is a logical description of the functionality. The functionality can be combined or separate. For example, storage512can be located at a distance in a data center. Data analyzer520, feature selector522, and modeler524can be combined with HF processor526where the different processes can be executed by one or more applications on a central processing unit (CPU), graphics processing unit (GPU), single instruction multiple data (SIMD) processor, and other processor types.

FIG. 6Ais an illustration of a diagram of an example time-series data processing flow601. The process can leverage time-series data along with well characteristics to build a statistical model to predict KPI in real-time on location while the previous version of the HF job plan is being implemented. This aspect of the disclosure utilizes raw time-series HF pumping data rather than pre-extracted or pre-engineered features to build the revised model. In addition, the model uses other information, such as location, reservoir, and completion parameters. Historical data from a large set of wells is fed into the model for training. Predictions can be made on-location in real-time for a proposed well location or a specific well of interest. The statistical methodology can use various conventional models, such as deep learning, random forest, gradient boost, and other machine learning or statistical methods.

The first step of the flow is to collect production data and then clean and process the data. This is similar to the process described inFIG. 3C, production data processing flow303. The next step is shown in the time-series data processing flow601. The time-series HF data source flow610can be raw time-series HF pumping data. This data can span minutes, hours, days, or other time intervals of pumping data available for jobs in the historical data set. The data can be cleaned and processed for quality control as shown in flow612. The time-series data is collected from the beginning of the data set up to a time t. Time t can be a point of time in the historical data or the present time.

There can be a variable amount of data available across different sized jobs. A decision point, such as decision flow614, can be made to determine whether a change in the sampling of the data would be beneficial to the process. If ‘Yes’, then the data can be down sampled or up sampled to a pre-determined sample density for the wells to be used, as shown in flow618. If no sampling changes are needed or if the sampling changes have been completed, the time-series data can be shifted to account for differences between surface and bottom hole values, as shown in flow620.

A decision point, such as decision flow622, can be made to determine whether a scaling change of the data would be beneficial to the process. If ‘Yes’, then the process proceeds to scaling flow624where the data can be scaled, i.e., standardized or normalized, to a certain range based on the modeling method used. If no scaling changes are needed or if the scaling changes have been completed, the data can be output as shown in flow626. The time-series data is presented as data up to a time t. The data can be used to predict KPI at a later point in time, such as t+1 or t+n, wherein is a selected time interval in the future. The time in the future can be labeled T, which leads to T>t.

FIG. 6Bis an illustration of a diagram of an example non-temporal data processing flow602. The modeling process can use specific information about a well that is not temporal, such as being fixed to the well, as shown in flow630. For example, the location of the well. This flow may include data sourced and processed as inFIG. 3A, flow301, or as engineered inFIG. 3B, flow302. These additional non-temporal features can be processed separately, as shown in flow632. The data can be formatted to be constant with the time-series data, as shown in flow634. One aspect can utilize repeating the constant feature value across the length of the time-series data to maintain a constant time-series dataset. Another aspect can develop a multi-stage model where the features are separately provided to the next steps as input, as shown in output flow636.

FIG. 6Cis an illustration of a diagram of an example time-series predictive model flow603. The data from the previous flows can be combined to make a single dataset. Production data can be collected as shown in flow630a, the time-series data can be collected up to a time t, as shown in flow630b, and the additional non-temporal data can be collected as shown in flow630c. The combined dataset is shown in flow632. The dataset can be divided into three separate classes or portions: (1) training data, (2) validation data, and (3) test data, as shown in flow634. Training data can be used to build one or more models. Validation data can be used to evaluate the trained model and to assess different algorithms. The test data can be used as a blind dataset to check the model robustness and predictive power. The data portions can be created using a random partition selection or using a clustering algorithm based on specific features, such as location.

The data portions can then be used to develop models to predict well production or KPI output using statistical or machine learning techniques, such as deep learning, machine learning, Bayesian modeling, geospatial modeling, and other techniques, as shown in flow634. Deep learning models can further utilize neural networks, such as convolutional neural networks, recurrent neural networks, other neural networks, or a combination of neural networks. In other aspects, the machine learning models can include random forest, gradient boost, decision trees, and other techniques. They can involve hierarchical and non-hierarchical techniques. The output can be a well production prediction at a specific time t+n, as shown in output flow636.

FIG. 6Dis an illustration of a diagram of an example time-series HF job design flow604. A specific well, for which real-time job plan revisions are requested, can be selected, as shown in flow650. A list of time-series features can be selected for optimization, as shown in flow652. The features can be pre-defined or user selected. The pre-defined or custom constraints can be selected, as shown in flow654.

The features from flow652and the constraints from flow654can be utilized in the optimization, i.e., recommendation, model generation, as shown in flow656. The optimization algorithm, such as a genetic algorithm, pattern search, differential evolution, and other types, can be applied to the collection of selected features within the provided constraints to generate multiple iterations and scenarios to optimize the pre-defined KPI, as shown in flow658. The process can then select the most optimized, or best fit, scenario given the constraints from a pool of top scenarios, to generate a predicted well production or other KPI at time t+n. This process can be represented by well production model flow660, which can encapsulate the time-series predictive model flow603. The output of the well production model flow660is the resultant of the output flow636of time-series predictive model flow603.

The output can be a recommended job design revision that satisfies the constraints and KPIs, as shown in flow662. For example, the model can recommend a change in proppant concentration from one treatment to another treatment based on the data collected on the location during the previous treatments. In some aspects, user input can be provided during the development phase of the model and the model can provide automated recommendations without further user input or feedback, once the process has been applied to the HF job plan.

FIG. 7is an illustration of a flow diagram of an example time-series HF job design method700. Method700demonstrates a way to adjust a HF job plan in real-time using treatment or stage data gathered from a well site. The changes can occur after a treatment or stage cycle is completed, or at another time point. Method700can build a model to assist in predicting what change to the HF job plan can move the HF job plan execution toward a better satisfaction of the identified KPIs.

Method700starts at a step701and proceeds to a step705. In the step705, the first data set is processed. The first data set is received, cleaned, and quality checks applied. For example, imputation can be used to fill in missing values and outlier data can be excluded from the first data set. The first data set is sourced from production values and data, reporting issues from one or more production wells, and one or more KPIs. Proceeding to a step710, the time-series data set is received. The time-series data can be collected from HF pumping data from the well site and from historical data sources for the well site and nearby or similar well sites. The time-series data can be cleaned, processed, and have quality checks applied.

In a step715, an optimized predictive model can be built. The predictive model can be built using a predictive data set including one or more elements of the first data set and one or more elements of the time-series data set. In a step720, the HF job plan can be revised using the predictive model. Time-series data from the well site can be run through the predictive model to predict how the well site will react and produce over subsequent time intervals, such as treatment cycles. The method700ends at a step750.

FIG. 8Ais an illustration of a diagram of an example pressure response feature flow800.FIG. 8Bis an illustration of a diagram of an example flow distribution response feature flow801.FIG. 8Cis an illustration of a diagram of an example fracturing dimensions response feature flow802.FIGS. 8A to 8Cdemonstrate various aspects of using response features as part of the time-series data feature selection within the models developed inFIGS. 6A to 6D. The response features can be used as part of a model input to identify response features that can serve as a proxy for well production or other KPI predicted output. A second model input can be generated to establish a relationship between response features and on-location controllable features, such as flow rate, proppant concentration, and other controllable features. The response features thus can be controlled by means of the controllable features thereby leading to better modeling decisions to maximize KPI, such as production output.

Flow distribution feature flow801follows a similar path as described for pressure response feature flow800. In place of the pressure response features, the flow distribution features can be used throughout the description of the pressure response feature flow800. In one aspect, the flow distribution features can be extracted from data gathered from fiber optics data, for example, a distributed acoustic sensing (DAS) system.

Fracturing dimensions flow802follows a similar path as described for pressure response feature flow800and flow distribution feature flow801. In place of the pressure response features, the fracturing dimensions features can be used throughout the description of the pressure response feature flow800. In one aspect, the fracturing dimensions features can be extracted from data gathered from offset well-monitored microseismic data, in-hole-monitored microseismic data, offset well pressure monitoring data (i.e., for fracture hits, such as from a pressure indication of communication between wells), offset well tiltmeter data, surface tiltmeter data, or physics-based modeling of pressure or net pressure response in real-time. In other aspects, other response features can be utilized in the algorithm.

In pressure response feature flow800, flow810(and similar flows830and850for their respective response features) describes the extraction of pressure response features from time-series pumping data. The pressure response features can be identified from historic time-series pressure data or from real-time time-series pressure data. The features can be aggregated at different levels, for example over a treatment job, diversion cycle, pumping stage, or at a time interval, such as one second. The features can be extracted during the pumping operations or during a shutdown operation. An example feature for the pumping stage aspect can be first and second derivative of pressure with respect to time, non-linear spline fits of pressure responses, first, second, and third moment during pad phase, proppant pumping, diversion cycle properties, shape of entire treatments, and other types of features. During the shutdown operation, an example feature can be instantaneous shut-in pressure (ISIP), pressure decline slope post pumping, G-function (a dimensionless time function relating shut-in time to total pumping time) parameters, and water hammer amplitude, frequency, and decay. The features can be extracted using a statistical or machine learning method or can be based on SME engineering and domain knowledge. A combination of extraction techniques can also be used.

In the building of a model flow812(and similar flows832and852for their respective response features), the extracted pressure response features can be aggregated. The model can be built from the aggregated data using statistical or machine learning techniques that establish the relation of the pressure response features with the well production output. This can identify the statistically significant job response features. The pressure response features can then act as a proxy for the production output changes. Conventional machine learning models can be used to build the models, such as linear regression, regularization, ridge regression, lasso, support vector machine, random forest, gradient boosting, decision trees, and other types of algorithms.

In the determine optimal pressure response features flow814(and similar flows834and854for their respective response features), after the relevant response features are identified, the optimal combinations of the pressure response features can be determined. The goal is to determine the combination that will maximize the KPI.

In the build models using controllable features flow816(and similar flows836and856for their respective response features), the on-location features that can be modified during a job can be connected to the pressure responses. Conventional multivariate time-series forecasting models or machine learning or deep learning algorithms (such as recurrent neural network, long-short term memory (LSTM), and other models) can be used to build the models.

In the monitor pressure response features flow818(and similar flows838and858for their respective response features), the significant pressure response features identified in flows812and814can be extracted and monitored in real-time. A determination can be made whether the job response is favorable to the KPI, based on the analysis in model flow812. If yes, then the job plan can continue as currently designed. If no, then one or more controllable features can be adjusted based on the models developed in controllable features flow816to attempt to cause the response features to move in a direction to improve the KPI results.

Multiple extracted response features can be combined in a flow. For example, pressure response and flow distribution response can be used in a single workflow, as input features to the model-building and subsequent steps. In another aspect of the method, flow distribution response and fracturing dimension response could be used together in a single workflow. In another aspect, pressure response and fracture dimension could be used together in a single workflow. In another aspect, pressure response, flow distribution response, and fracture dimension response could be used together in a single workflow.

FIG. 9is an illustration of a diagram of an example general response feature flow900. General response feature flow900is another view demonstrating an example flow for this aspect of the disclosure and is divided into sections, approximating the flows800,801, and802described inFIGS. 8A to 8C. The general response feature flow900begins with flow910where the historical time-series data is identified that is relevant for the well site job plan. The appropriate response features are extracted in flow915. The response features dataset is shown by flow916. Flows910,915, and916are outlined by dash-dot box961. Dash-dot box961represents the flows810,830, and850inFIGS. 8A to 8Crespectively.

A model to predict the KPI is built in model building flow920. It uses customer defined KPI parameters, as shown by flow917. Model building flow920also uses uncontrollable features, as shown in flow918. As part of the model building flow920, a determination of which response features and their respective value ranges can better match the KPIs is made, as shown in flow925. Flows917,918,920, and925are boxed by dotted box962. Dotted box962represents the model flows812,832, and852inFIGS. 8A to 8Crespectively.

An evaluation can be made to determine whether the response features selected are significant in predicting the KPI parameters, as shown in decision flow930. Should the resultant of decision flow930be ‘No’, then the response features can be discarded, as shown in flow932. If the decision flow930is ‘Yes’, the response features are kept and used in further analysis and processing, as shown in flow934. Flows925,930,932, and934are boxed by a dashed line963. Dashed line963represents the flows814,834, and854inFIGS. 8A to 8Crespectively.

New models can be built to predict the significant response feature changes due to the controllable features, as shown in flow940. The controllable features can be provided to flow940, as shown in flow938. A determination of how the controllable features impact the response features can be made based on the predictive model, as shown in flow945. Flows934,938,940, and945are boxed by a light gray line964. Light gray line964represents the controllable feature flows816,836, and856inFIGS. 8A to 8Crespectively.

FIG. 10is an illustration of a diagram of an example job design adjustment flow1000. Job design adjustment flow1000can be a real-time adjustment process for the job design parameters. Job design adjustment flow1000represents the flows818,838, and858inFIGS. 8A to 8Crespectively. The model or models developed to predict the KPI from response features, from the flows described inFIG. 9, are retrieved for this process, as shown in flow1018.

The flow process for the remaining flows form a partial loop mechanism where continuous monitoring, feedback, and adjustment is made to the HF job plan. Flow1010monitors the received data, such as from sensors in and near the well site system. The respective response features can be extracted from the received data, as shown in flow1015. The new data set of extracted response features is shown by dataset flow1016. The dataset flow1016is used as inputs to the model received from flow1018.

A determination is made whether the response features are likely to have results favorable with respect to the KPIs, as shown in decision flow1020. If the resultant from decision flow1020is ‘Yes’, then the HF job plan continues execution as designed, as shown in flow1050. If the resultant from decision flow1020is ‘No’, then the models predicting response features from controllable features is activated, as shown by flow1024. One or more of the controllable features are adjusted, per the model predictions, as shown in flow1026. The flow process from flow1026and from flow1050returns to flow1010for additional monitoring at the next time interval.

FIG. 11is an illustration of a flow diagram of an example response feature method1100. Method1100demonstrates a way to utilize extracted response features in conjunction with a prediction model. Response features can be received in real-time from a well site. Those response features can be run through as input into the prediction model. One or more features can be adjusted as input into the prediction model. The prediction model can output a recommendation on HF job plan changes in real-time to one or more features that can improve the well state with respect to the KPIs.

Method1100starts at a step1101and proceeds to a step1105. In the step1105, response features, such as pressure responses, flow distributions, and fracture dimensions, can be received. The response features can be gathered during pumping operations or during shutdown operations. In a step1110, a prediction model can be built. The prediction model can use the response features as input. The prediction model can produce one or more scenarios so that as the response features are periodically updated, the predictions on the impact to the KPIs can be identified. Proceeding to a step1115, a combination of features can be identified that could impact the KPIs. The features can be evaluated by how impactful they are to the KPIs. The features that are the most impactful can be identified and selected. The number of features can vary from one to the number of features gathered.

Proceeding to a step1120, well site data from one or more treatment cycles can be received. In a step1125the HF job plan can be revised. The revision process can utilize the well site data and the prediction model. The revision process can keep the HF job plan as currently designed should the prediction model indicate, using the extracted response features, that the KPIs can be satisfied. If the KPIs may not be satisfied or could be improved upon, the revision process can select the features to adjust, based on the prediction model's output and extracted response features. The method1100ends at a step1150.

FIG. 12Ais an illustration of a diagram of an example graph1200demonstrating a selection of treatment and cycle start and end time points. A machine learning approach can be used to detect the presence or absence of an event, and the start and end of that event. In some aspects, the event properties can also be identified. An event can be a treatment, diversion cycle, sand slug, minifrac, rate step up sequence, rate step down sequence, initial shut-in pressure (ISIP), formation breakdown, screenout from time-series pumping data, and other event types. The pumping data can be treating pressure, slurry rate, proppant concentration, and other pumping data, along with available user defined data elements. The treatment and cycle information can be utilized by one or more of the time-series models described above, and provide additional analysis to the model generation flows.

Graph1200demonstrates a graph with three sections. Graph1200has an x-axis1215of time, which increases to the right. The three graph sections include the slurry rate1220, the proppant concentration1222, and the treatment pressure1224. Graph1200has multiple vertical dashed lines, one of which is labeled as line1218. Lines1218indicate the separation of each treatment cycle in the graph1200.

Each treatment cycle includes a line graph indicating how each of the tracked data elements in each section correlate with the treatment cycle implementation. The slurry rate1220indicates a start of a detected event at point1230. A maximum event response is shown by point1232. The end of the event is indicated by point1234. The proppant concentration1222demonstrates the same event data collection, shown by line graph portion1240. The treatment pressure1224demonstrates the same event data collection, shown by line graph portion1245.

A SME or an automated process can analyze the collected data, as shown in the graph1200to determine how the events correlate to the various treatment cycles. The information computed from this analysis can be used as input to the modeling phase of this aspect.

FIG. 12Bis an illustration of a block flow diagram of an example treatment and cycle flow1250. The treatment and cycle information1260can include three or more flows. For example, the treatment and cycle flow1250can have a data preparation flow1270. Treatment and cycle information1260can be provided to data preparation flow1270. The data preparation flow1270can identify events from a SME selection, as shown in flow1272or from other data sources, as shown in flow1274. Next, events of interest can be identified, as shown in flow1276, such as beginning and ending of treatments, which can be utilized to create a labeled dataset for training, validation, and test data for an event detection machine learning approach.

Following the data preparation flow1270can be a build model flow1280to automatically detect and identify such events. Using a subset of the data prepared in the data preparation flow1270, one or more machine learning models can be trained, as shown in flow1282. The models can then be validated against additionally collected data, as shown in flow1284. Models can include deep learning, logistic regression, support vector machine, random forest, Bayesian, neural network, gradient boost, and other model algorithms. A model can be selected, as shown in flow1286, based on the model that provides the results that best match the validation dataset, optimization capability and performance, ability to apply real world physical and operational constraints, ease of deployment, and other factors.

Following the build model flow1280can be a deployment of the selected model1290. Events can be detected in unlabeled data using the model selected from the build model flow1280, as shown in flow1292. The model can incorporate available job data, as shown in flow1294. Next, the deployment of the selected model1290can aggregate the resulting event types, times, and attributes in the data for further use, as shown in flow1296.

FIG. 13is an illustration of a diagram of an example graph1300demonstrating a machine learning selection of treatment cycles. Graph1300includes two portions, a graph1305demonstrating event data collected from a plug and perforate completion (plug-n-perf) sequence, and a graph1306demonstrating event data collected from a sliding sleeve completion sequence. Graph1300has an x-axis1310and1330of time, which increases to the right. Graphs1305and1306demonstrate graphs with three sections. The three sections include the slurry rate1315, the proppant concentration1316, and the treatment pressure1317. Graph1300has multiple vertical lines, one of which is labeled as line1319on graph1305, and line1335on graph1306. Lines1319and lines1335indicate the separation of each treatment cycle in the graph1300.

Example treatment cycles are labeled as treatment cycle1320, treatment cycle1322, and treatment cycle1324on graph1305. Each treatment cycle includes a line graph indicating how each of the tracked data elements in each section correlate with the treatment cycle implementation. Similar to graph1200, graph1300indicates how each of the events correlate to the treatment cycle implementation.

A SME or an automated process can analyze the collected data, as shown in the graphs1200and1300, to determine how the events correlate to the various treatment cycles.

FIG. 14is an illustration of a diagram of an example ensemble model flow1400with independent data sources.FIG. 15is an illustration of a diagram of an example ensemble model flow1500with independent and combined data sources.FIG. 16is an illustration of a diagram of an example ensemble model flow1600with independent data sources and data sources as direct input into the ensemble model.FIG. 17is an illustration of a diagram of an example ensemble model flow1700with a two-step ensemble model.FIG. 18is an illustration of a diagram of an example ensemble model flow1800with independent and combined data sources, and a two-step ensemble model.

Ensemble model flows1400,1500,1600,1700, and1800can represent one or more ensemble modeling aspects of this disclosure. Ensemble modeling can be utilized to support the flows previously described herein, utilized in any flow step wherein a predictive or analysis model is described. The ensemble modeling technique can be used to estimate or predict a well characteristic, for example, well production output or other KPI, completion efficiency, geospatial location, completion parameters, stimulation parameters, reservoir data, well logs, well directional surveys, drilling parameters, production data, fracture topology, fluid flow and dynamics, DAS, distributed temperature sensing (DTS) data, well header information, and other characteristics.

The ensemble model can consolidate a suite of modeling techniques and data sources, such as physics-based methods, numerical estimation methods, mathematical, statistical methods, earth models, geospatial, geological, reservoir-based models, data driven machine learning models, and other modeling techniques. Data sources can include information and parameters describing well characteristics. The ensemble modeling can combine some or all of the well characteristic information and data sources in a systematic and scalable method. The ensemble modeling can be a mathematical or data driven statistical model, a weighted average, linear combination, a non-linear model, Bayesian hierarchical model, and other model types. The estimates from each of the models generated can vary and they may not converge towards the true value for the feature being examined. In addition, the data sources can share common underlying data. It can be beneficial to utilize the ensemble modeling techniques to combine the estimates from two or more models to obtain a single estimate that is reflective of the various models and data sources.

FIG. 14ensemble model flow1400can build individual models for each of the data sources, parameters, and assumptions to obtain an estimate of well characteristics. The individual models can be incorporated together using a second stage modeling to obtain a combined estimate. The second stage modeling can develop a model that uses individual estimates as input parameters, and then build a new model to generate a single combined estimate. The second stage model can also be a weighted average, linear regression, or non-linear regression combination of the individual estimates. The second stage model can also be a Bayesian hierarchical graphical model where the individual estimates are nodes in the graph and the edges define relationships across different estimates or data sources.

There can be one or more data sources1405, such as n number of data sources. These are labeled as data sources1405-1through1405-n. The data sources can be public data sources, confidential data sources, proprietary data sources, and other data sources. The data sources can include well characteristics, i.e., features. Well characteristics can include data and information about the wellbore, reservoir, and treatment. Each data source1405can have a respective modeling flow1410. There can be n number of modeling flows1410, matching the number of data sources1405. The output from each respective modeling flow1410is an output1415of well characteristic estimates. There can be n number of outputs1415, matching the number of data sources1405. Data sources1405, modeling flows1410, and outputs1415can be grouped together to form an individual model layer step1402.

The collected outputs1415can be used as inputs into an ensemble modeling flow1422. The output1424of the ensemble modeling flow1422can be a set of final well characteristic estimates. The ensemble modeling flow1422and the output1424can be grouped together to form the ensemble model step1420.

FIG. 15ensemble model flow1500can be a variation of ensemble model flow1400where the first step can include building a model or a combination of models on a subset of data sources or parameters. The resulting output well characteristic estimates can be combined using an ensemble approach. The remaining parameters and data sources can be included with the combined estimate from the first step. This aspect can be applied to sources, methods, and parameters where certain specific relationships should be taken care of prior to combining with the other independent sources.

There can be two or more data sources1505, such as m number of data sources. These are labeled as data sources1505-1,1505-2, through1505-m. As in ensemble model flow1400, the data sources can be of various types and sourced from various data repositories. Each of the data sources1505are used as inputs into a modeling flow1510. The output from the modeling flow1510is an output1515of well characteristic estimates.

In addition, there can be one or more data sources1506, such as n number of data sources. These are labeled as data sources1506-1through1506-n. The data sources, as previously described, can be of various types of data sources. Each data source1506can have a respective modeling flow1511. There can be n number of modeling flows1511, matching the number of data sources1506. The output from each respective modeling flow1511is an output1516of well characteristic estimates. There can be n number of outputs1516, matching the number of data sources1506. Data sources1505and1506, modeling flows1510and1511, and outputs1515and1516can be grouped together to form an individual model layer step1502.

The collected outputs1515and1516can be used as inputs into an ensemble modeling flow1522. The output1524of the ensemble modeling flow1522can be a set of final well characteristic estimates. The ensemble modeling flow1522and the output1524can be grouped together to form the ensemble model step1520.

FIG. 16ensemble model flow1600can be an ensemble model where individual models can be built on a subset of data sources and parameters, and the estimates from the individual models can serve as input to the second layer model in addition to the other data sources and parameters as direct input. The model can then estimate from the first step, and raw parameters from the other data sources can be used as inputs into the second layer model to estimate the well characteristics.

There can be one or more data sources1605, such as n number of data sources. These are labeled as data sources1605-1through1605-n. The data sources, as previously described, can be of various types of data sources. Each data source1605can have a respective modeling flow1610. There can be n number of modeling flows1610, matching the number of data sources1605. The output from each respective modeling flow1610can be an output1615of well characteristic estimates. There can be n number of outputs1615, matching the number of data sources1605.

In addition, there can be one or more data sources1606-m. There can be m number of additional data sources. Data sources1605and1606, modeling flows1610, and outputs1615can be grouped together to form an individual model layer step1602.

The collected outputs1615and the data sources1606can be used as inputs into an ensemble modeling flow1622. The output1624of the ensemble modeling flow1622can be a set of final well characteristic estimates. The ensemble modeling flow1622and the output1624can be grouped together to form the ensemble model step1620.

FIG. 17ensemble model flow1700extends the previously described ensemble model flows1400,1500, and1600, by demonstrating that the two-layer model can be extended to multiple layer models encompassing various options, parameter combinations, and data or model relationships. Ensemble models can be built at different layers and then combined at the next layer using another ensemble model.

There can be one or more data sources1705, such as n number of data sources. These are labeled as data sources1705-1through1705-n. The data sources, as previously described, can be of various types of data sources. Each data source1705can have a respective modeling flow1710. There can be n number of modeling flows1710, matching the number of data sources1705. The output from each respective modeling flow1710can be an output1715of well characteristic estimates. There can ben number of outputs1715, matching the number of data sources1705.

In addition, there can be one or more data sources1706-m. There can be m number of additional data sources. Also, there can be one or more data sources1707-p. There can be p number of additional data sources. Data sources1705,1706, and1707, modeling flows1710, and outputs1715can be grouped together to form a first individual model layer step1702. The collected outputs1715and the data sources1706can be used as inputs into a first ensemble modeling flow1722. The output1724of the first ensemble modeling flow1722can be a set of initial well characteristic estimates. The first ensemble modeling flow1722and the output1724can be grouped together to form the first ensemble model step1720.

The output1724and the data sources1707can be used as inputs into a second ensemble modeling flow1732. The output1734of the second ensemble modeling flow1732can be a set of final well characteristic estimates. The second ensemble modeling flow1732and the output1734can be grouped together to form the second ensemble model step1730.

FIG. 18ensemble model flow1800is a variation of ensemble model flow1700by demonstrating a variation of the multi-stage ensemble modeling. There can be two or more data sources1805, such as m number of data sources. These are labeled as data sources1805-1,1805-2, through1805-m. As in ensemble model flow1400, the data sources can be of various types and sourced from various data repositories. Each of the data sources1805are used as inputs into a modeling flow1810. The output from the modeling flow1810is an output1815of well characteristic estimates.

In addition, there can be one or more data sources1806, such as n number of data sources. These are labeled as data sources1806-1through1806-n. The data sources, as previously described, can be of various types of data sources. Each data source1806can have a respective modeling flow1811. There can be n number of modeling flows1811, matching the number of data sources1806. The output from each respective modeling flow1811is an output1816of well characteristic estimates. There can be n number of outputs1816, matching the number of data sources1806. Data sources1805and1806, modeling flows1810and1811, and outputs1815and1816can be grouped together to form an individual model layer step1802.

The collected outputs1816can be used as inputs into a first ensemble modeling flow1822. The output1824of the first ensemble modeling flow1822can be a set of initial well characteristic estimates. The first ensemble modeling flow1822and the output1824can be grouped together to form the first ensemble model step1820.

The collected outputs1815(from the modeling flows1810) and the outputs1824(from the ensemble model flow1822) can be used as inputs into a second ensemble modeling flow1832. The output1834of the second ensemble modeling flow1832can be a set of final well characteristic estimates. The second ensemble modeling flow1832and the output1834can be grouped together to form the second ensemble model step1830.

Ensemble model flows1400,1500,1600,1700, and1800demonstrate some of the variations possible with this aspect of the disclosure. Additional variations are possible, such as increasing the number of layers, where each layer builds an ensemble model while joining additional data sources and analysis.

A portion of the above-described apparatus, systems or methods may be embodied in or performed by various digital data processors or computers, wherein the computers are programmed or store executable programs of sequences of software instructions to perform one or more of the steps of the methods. The software instructions of such programs may represent algorithms and be encoded in machine-executable form on non-transitory digital data storage media, e.g., magnetic or optical disks, random-access memory (RAM), magnetic hard disks, flash memories, and/or read-only memory (ROM), to enable various types of digital data processors or computers to perform one, multiple or all of the steps of one or more of the above-described methods, or functions, systems or apparatuses described herein.

Those skilled in the art to which this application relates will appreciate that other and further additions, deletions, substitutions and modifications may be made to the described embodiments. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the claims. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, a limited number of the exemplary methods and materials are described herein.

Aspects disclosed herein include:A. A method to design a final hydraulic fracturing (HF) job plan for directing operations of well site equipment for a well, including: (1) preparing a first data set, wherein the first data set is statistically processed, and wherein the first data set is processed to join disparate data elements sourced from different data sources, (2) engineering features utilizing the first data set, and wherein the engineering features comprises identifying standard and engineered features, (3) processing a final second data set, wherein the final second data set is computed from a preliminary second data set comprising estimated production values, reporting issues of a production well, and a key performance indicator (KPI), (4) selecting design parameters from the final second data set and the features, wherein the design parameters represent a reduction of a number of the features, (5) building a model, wherein the model is one or more of a final geospatial model, and a final predictive model, wherein the final geospatial model utilizes a geospatial data set comprising one or more of the first data set, the features, the final second data set, and the design parameters, and wherein the final geospatial model substantially isolates one or more of the design parameters, including a location of the well and stimulation of the well, and the final predictive model utilizes a predictive data set comprising one or more of the first data set, the features, the final second data set, and the design parameters, and (6) designing the final HF job plan, utilizing the final geospatial model and the final predictive model.B. A computer program product having a series of operating instructions stored on a non-transitory computer-readable medium that directs a data processing apparatus when executed thereby to perform operations to design a hydraulic fracturing (HF) job plan to direct operations of well site equipment of a well, having operations including: (1) preparing a first data set, wherein the first data set is statistically processed, and wherein the first data set is processed to join disparate data elements sourced from different data sources, (2) engineering features utilizing the first data set, and wherein the engineering features comprises identifying standard and engineered features, (3) processing a final second data set, wherein the final second data set is computed from a preliminary second data set comprising estimated production values, reporting issues of a production well, and a key performance indicator (KPI), (4) selecting design parameters from the final second data set and the features, wherein the design parameters represent a reduction of a number of the features, (5) building a model, wherein the model is one or more of a final geospatial model, and a final predictive model, wherein the final geospatial model, utilizes a geospatial data set comprising one or more of the first data set, the features, the final second data set, and the design parameters, and wherein the final geospatial model substantially isolates one or more of the design parameters, including a location of the well and stimulation of the well, and the final predictive model, utilizes a predictive data set comprising one or more of the first data set, the features, the final second data set, and the design parameters, and (6) designing a final hydraulic fracturing HF job plan, utilizing the final geospatial model and the final predictive model.C. A system to design a final hydraulic fracturing (HF) job plan for directing operations of well site equipment for a well, including: (1) a data analyzer, operable to generate cleaned data sets by analyzing, cleaning, correcting, and removing outlying data elements from each of received data sets, (2) a feature selector, operable to identify features utilizing a received job plan objective wherein the features affect the job plan objective, and wherein the features are identified in the cleaned data sets, (3) a modeler, operable to build geospatial models and predictive models utilizing the cleaned data sets and the features, and (4) a HF processor, operable to analyze each of the geospatial models and the predictive models, operable to select a final geospatial model and select a final predictive model, and operable to output a designed HF job plan as the final HF job plan.

Each of aspects A, B, and C can have one or more of the following additional elements in combination:

Element 1: wherein the data sources are one or more of publicly available well, reservoir, or treatment data, spatial information, sales data, customer provided data, and previous fracture job data. Element 2: the preparing the first data set further includes imputing data elements, wherein the data elements are missing in the data sources. Element 3: the preparing a first data set further includes processing the first data set to remove outlier data elements. Element 4: wherein the data sources include extracted treatment features extracted from historical or real-time treatment job data. Element 5: the extracted treatment features further includes receiving a first time-series pumping data set. Element 6: the extracted treatment features further includes receiving a second time-series pumping data set. Element 7: identifying a first event set, wherein the first event set comprises event time intervals, utilizing the first time-series pumping data set. Element 8: the extracted treatment features further includes training a machine learning model utilizing the first event set. Element 9: the extracted treatment features further includes estimating a second event set, utilizing the second time-series pumping data set and the machine learning model. Element 10: wherein the first time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration, and wherein the second time-series pumping data set comprises one or more of treating pressure, slurry rate, and proppant concentration. Element 11: wherein the engineering features represent one or more standard features, logic features, physics features, or smart features. Element 12: engineering features further include aggregating one or more of treatment level features and pumping stage level features to define features representing well production over a determined time period. Element 13: wherein the processing the final second data set further includes cleaning the preliminary second data set utilizing statistical techniques. Element 14: wherein the processing the final second data set further includes verifying the preliminary second data set utilizing statistical quality check techniques. Element 15: wherein the processing the final second data set further includes applying a smoothing technique to the preliminary second data set to generate a smoothed second data set, to realize one or more of reducing statistical noise, correcting erroneous data, and identifying unusable production well data. Element 16: wherein the processing the final second data set further includes computing the KPI utilizing the smoothed second data set, wherein the KPI is computed from a well production time interval. Element 17: wherein the processing the final second data set further includes generating the final second data set utilizing the smoothed second data set and the KPI. Element 18: wherein the KPI is computed from one of a 30-day, 90-day, 180-day, 365-day, maximum initial production, or estimated ultimate recovery. Element 19: wherein the selecting design parameters further includes identifying the design parameters utilizing one or more of forward step-wise selection, backward step-wise selection, correlation analysis, lasso regression, ridge regression, elastic net regression, and subject matter expert knowledge. Element 20: wherein the selecting design parameters further includes removing design parameters from the final second data set and the features where the design parameters are associated with non-relevant features of the well. Element 21: wherein the building the model for the final geospatial model further includes splitting an aggregate of the geospatial data set into a training data set, a validation data set, and a test data set. Element 22: wherein the building the model for the final geospatial model further includes building one or more preliminary geospatial models utilizing the training data set. Element 23: wherein the building the model for the final geospatial model further includes evaluating each of the preliminary geospatial models utilizing the validation data set. Element 24: wherein the building the model for the final geospatial model further includes analyzing each of the preliminary geospatial models utilizing the test data set. Element 25: wherein the building the model for the final geospatial model further includes comparing each of the preliminary geospatial models utilizing results from the evaluating and the analyzing. Element 26: wherein the building the model for the final geospatial model further includes selecting the final geospatial model from the preliminary geospatial models utilizing the comparing, wherein the final geospatial model results in a higher predictive accuracy compared to other of the preliminary geospatial models. Element 27: wherein the building the model for the final predictive model further includes splitting an aggregate of the predictive data set into a training data set, a validation data set, and a test data set. Element 28: wherein the building the model for the final predictive model further includes building one or more preliminary predictive models utilizing the training data set. Element 29: wherein the building the model for the final predictive model further includes evaluating each of the preliminary predictive models utilizing the validation data set, wherein the evaluating utilizes a cross-validation technique. Element 30: wherein the building the model for the final predictive model further includes analyzing each of the preliminary predictive models utilizing the test data set. Element 31: wherein the building the model for the final predictive model further includes comparing each of the preliminary predictive models utilizing results from the evaluating and the analyzing. Element 32: wherein the building the model for the final predictive model further includes selecting the final predictive model from the preliminary predictive models, wherein the final predictive model results in a higher predictive accuracy, or best fitted to the predictive data set, compared to other of the preliminary predictive models, wherein the final predictive model is utilized to generate predictions for production for the well. Element 33: wherein the designing the final HF job plan further includes determining an operation plan objective. Element 34: wherein the designing the final HF job plan further includes selecting an operation plan feature set utilizing the geospatial data set and the predictive data set. Element 35: wherein the designing the final HF job plan further includes applying constraints to the operation plan feature set, wherein each of the constraints is pre-defined or defined by the operation plan objective. Element 36: wherein the designing the final HF job plan further includes generating one or more preliminary HF job plans, utilizing an optimization algorithm and the final geospatial model, the final predictive model, the operation plan feature set, and the constraints. Element 37: wherein the designing the final HF job plan further includes identifying a final HF job plan from the preliminary HF job plans. Element 38: further comprising evaluating one or more preliminary HF job plans, wherein the evaluating utilizes the operation plan objective, operational feasibility factors, and economic factors. Element 39: wherein the generating utilizes user supplied data ranges and constraints. Element 40: wherein the constraints are one or more of operational feasibility, cost factors, environmental factors, legal factors, and equipment factors. Element 41: wherein the building a model utilizes an ensemble model utilizing a single stage predictive model or a multiple stage predictive model. Element 42: wherein the ensemble model consolidates one or more modeling techniques and data sources. Element 43: wherein the modeling techniques are one or more of physics-based methods, numerical estimation methods, mathematical methods, statistical methods, earth models, geospatial models, geological models, reservoir models, and data-driven machine learning models. Element 44: wherein the data sources are one or more of geospatial location, completion parameters, stimulation parameters, reservoir data, well logs, well directional surveys, drilling parameters, production data, fracture topology, fluid flow and dynamics, distributed acoustic sensing, distributed temperature sensing, well header data, customer data, operator data, and service company data. Element 45: a receiver, operable to receive the received data sets. Element 46: a communicator, operable to transmit the final geospatial model, the final predictive model, and the HF job plan. Element 47: a storage medium, operable to store the received data sets, the features, the geospatial models, the predictive models, and the HF job plan. Element 48: a well controller, operable to receive the geospatial models, the predictive models, and the HF job plan, and to execute the HF job plan. Element 49: wherein the received data sets comprise public, confidential, proprietary, well site location, and historical data sources.