QUANTITATIVE HYDRAULIC FRACTURING SURVEILLANCE FROM FIBER OPTIC SENSING USING MACHINE LEARNING

Abstract

A system and methods for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning is described herein. An exemplary method provides capturing distributed acoustic sensing (DAS) data, distributed temperature sensing (DTS) data, and microseismic data over monitored stages. Operation states and variables at a respective stage are predicted, based on, at least in part, the DAS data, DTS data, or microseismic data. At least one event associated with the predicted operation states and variables is localized at the respective stage.

Description

TECHNICAL FIELD

The present disclosure describes quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning.

BACKGROUND

Generally, fiber optic sensing is implemented in a wellbore environment via a logging tool that includes at least one fiber optic sensor for capturing a measurement in the wellbore environment. A fiber optic line is in optical communication with the fiber optic sensor. The data captured by the fiber optic sensor is transmitted through the fiber optic line in real time, and the fiber optic sensor can be a passive sensor not requiring electrical or battery power.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system that enables quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning.

FIG. 2 is a 2D histogram of an example pumping data set

FIG. 3 example preprocessing workflow for DAS.

FIG. 4 is an example single channel DAS spectrogram.

FIG. 5 is an illustration of DTS data: multichannel (vertically distributed) DTS data (top) and averaged DTS data (bottom).

FIG. 6 is an illustration of an example single channel microseismic spectrogram.

FIG. 7 is an illustration of an example hydrofracturing pumping data (pressure and slurry rate).

FIG. 8 is an illustration of (a) Basic structure of ResNet, (b) ResNet-18 (convolutional network) model for production data prediction from DAS, DTS and or microseismic data.

FIG. 9 is an illustration of ResNet-18 (convolutional network) plus Long short-term memory (LSTM) model for production data prediction from DAS, DTS and or microseismic data.

FIG. 10 is an illustration of a workflow for model training and testing over different stages.

FIG. 11 is an illustration of the performance of the model pretrained on stage 9, and blind tested on stage 8.

FIG. 12 is an illustration of the performance of the model pretrained on stage 9, and tested on stage 8, with 20% retraining.

FIG. 13 is an illustration of fracking/cluster localization.

FIG. 14 is an illustration of a DAS/DTS/microseismic data synced with pressure and slurry rate data for one example stage. DAS and microseismic data are plotted in spectrogram for one channel each

FIG. 15 is an illustration of the DAS frequency band extracted data over the well depth synced with pressure and slurry rate data for one example stage.

FIG. 16 is a process flow diagram of a process for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning.

FIG. 17 is a schematic illustration of an example controller (or control system) for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning according to the present disclosure.

DETAILED DESCRIPTION

Embodiments described herein enable quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning. The present techniques include fiber-optic distributed sensing. Fiber optic distributed sensing, such as distributed acoustic sensing (DAS), distributed temperature sensing (DTS) and microseismic, has been increasingly used in unconventional fields for intelligent completions, production monitoring, and optimization. Advancement in fiber-optic distributed sensing technology in the past decades has made it possible to reveal critical operational information in situ and in real time. The analysis of a large volume of fiber-optic sensing data and their association with operation states remains mostly qualitative, correlative and after-fact descriptive. The present techniques include deep learning based methods that directly predict operation states and variables, including the pumping variables, the production flow pressure and rates, and the fracking cluster locations, from all the available fiber-optic measurements. Additionally, the present techniques establish an automated quantitative framework for intelligent completion and production monitoring, with minimal manual interpretation or intervention. When combined with efficient pre-processing of the raw measurement data, this will enable DAS/DTS based field monitoring to improve real-time operation decision making. For example, real time decisions can be made to correct issues discovered based on the predicted data.

DAS measurements tend to accumulate significant amount raw data and even simple spectral analysis can be computationally costly and complex, due to the large channel numbers, high sampling frequency, and long time duration over which the measurements are taken. Traditionally, associating the processed DAS information with operation variables and states of interest are generally qualitative, correlative and after-fact descriptive, requiring significant amount of human intervention and interpretation. This can be cumbersome and even infeasible for applications involving long duration monitoring. Accordingly, the present techniques enable automated and quantitative processing frameworks capable of predicting operation states or variable values directly from fiber optic DAS and/or DTS data, with minimal manual intervention. When combined with efficient pre-processing of the raw measurement data, this DAS/DTS based field monitoring to improve real-time operation decision making in enabled. In some embodiments, time traces of DAS at each depth location are transformed into the time-spectral domain. The magnitude of the transformed data in certain frequency bands most correlated with operation variables of interest are identified and collated with operation states as the so-called frequency band extracted (FBE) signals. The present techniques enable direct modeling from DAS and/or DTS data to hydraulic fracturing characteristics or production variables.

FIG. 1 is a block diagram of a system that enables quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning. In the example of FIG. 1, the system 100 obtains as input DAS, DTS, and microseismic data. Accordingly, the data is loaded (102), and the data is preprocessed (104) to obtain a set of sample data 106. The sample data includes, for example, DAS, DTS, and microseismic snapshot images. Deep learning models 108 are trained, tested, and validated. Once trained, the deep learning models 108 execute on unseen DAS, DTS, and microseismic snapshot images to predict operation states and variables. In some embodiments, the events are localized. Accordingly, in some embodiments, machine learning based methods are developed for quantitatively predicting (110) hydraulic fracturing profiling from fiber optic distributed acoustic sensing (DAS), distributed temperature sensing (DTS) and/or microseismic data, or any combination of these data, and identify and localize the events. FIG. 1 shows direct prediction 110 of pressure and slurry rate from DAS/DTS/microseismic data using deep neural networks.

DAS and DTS is used to record vibration and temperature around a fiber, respectively. To determine operational and completion design efficiency, DAS/DTS and microseismic data are monitored during the perforation and the actual hydraulic fracturing pump phases. In some embodiments, the pump data (e.g., slurry rates, pressures) is predicted using the DAS/DTS and microseismic measurement over the monitored stages. The inputs to the deep learning models 108 are the preprocessed data samples 106 partitioned from these measurements and their transformed results. In examples, the DAS and microseismic data are converted into spectrogram and then time segmented during preprocessing 104. The present techniques include, at least in part, three types of deep learning models 108: I) A multimodal ResNet network, which maps time snapshots of these measurement samples to the synced pump data independently; II) a multimodal ResNet followed by convolutional LSTM sequence learning model maps time segments of these measurement samples into the synced hydraulic fracturing flow data; and III) constrained version of I and II by enforcing the prediction to be consistent with the learned relationship between the flow pressure and rates. In examples, the models and their constrained versions are trained over a randomly partitioned subset of samples before applied to the remaining testing samples.

In examples, the models and their constrained versions are trained and tested over a DAS/DTS and microseismic dataset acquired over one hour duration with known flow data during hydraulic fracturing process and production phase. The trained models perform robustly over the testing samples and produce accurate prediction. The LSTM sequence-learning model as described herein produces a smoother and more consistent pressure and slurry rate prediction. The trained deep learning models enable an accurate prediction of the pump data from the fiber optic measurements, and also enable an automated and quantitative way to analyze and predict stage isolation state, cluster locations, and determine the fluid profile. In examples, stages here refer to a specific hydraulic fracking process which starting from bottom of the well, perforates and fracks over a certain well depth range (along the well, not necessarily vertical) before sealing it and moving up to perforate and frack the next depth range above; each one of these depth range is called stage. Stages are different well depth ranges and their operations are also separated over time.

In examples, the input data includes DAS input, DTS input, and microseismic input. In examples, the DAS data is sampled at a sample rate f_s,das (e.g. 2 kHz), from a total of N_das (e.g. 493) DAS channels recorded vibrations along the lateral with a spacing d_das (e.g. of approximately 16.74 ft (5.1 m)). The DAS data can be in sgy files, HDF5, or other data file format. Each of the files store a certain time duration T_das (e.g. 3000 second long) signal of N_das (e.g. 493) traces. In examples, the DTS data is recorded at a sample rate of f_s,dts (e.g. 30 Hz). The space resolution d_dts (e.g. approximately 1 ft or 30.48 cm) during hydraulic fracture stimulation. The DTS data can be from a csv file, an HDF5 file or some other format, where each row represents one depth value with total number of channels N, and each column represent one time point, with the total number of columns T_dts. In examples, the micro-seismic data is sampled at a sample rate of f_s,ms (e.g. 2 kHz). A total of N_ms (e.g. 36) channels are recorded in the monitoring well. The micro-seismic data can be from sgy files, HDF5, or other data file format, where each file stores a certain time duration T_ms (e.g. 3-second) long signal of N_ms (e.g. 36) traces.

In examples, the output data includes injection or pumping data output. FIG. 7 is an illustration of an example hydrofracturing pumping data (pressure and slurry rate). The plot 700 shows time along the x-axis 702 and pressure along the y-axis 704. In examples, injection or pumping data consists of the pressure and the flow/slurry rate, typically measured at the surface. Injection data flow rates are generally provided in terms of different phases such as CO₂ or water, as applicable. The injection/pumping data is recorded as a sample rate of f_s,pmp (e.g. 1 Hz). The injection/pumping data is saved in a csv file or other data format. In examples, the output data includes production data output: production data consists of pressure and multiphase production flow rates, sampled at sample rates of f_s,prod,p and f_s,prod,r, respectively. The rates are provided in terms of oil, gas and water. The production data may be measured from the surface or downhole.

Referring again to FIG. 1, data loading 102 is shown and preprocessing 104. In data loading 102, data can be loaded from data files in offline mode or online streaming mode from data acquisition database. Since the inputs and outputs consist of multiple measurements and variables, the availability of various data over the time axis is determined. The time windows over which measurements are available or of acceptable quality can vary. In examples, a common time window or durations where all measurements are available is determined.

For the DAS and micro-seismic data in the offline mode, each file only contains a small segment of data. Data is iteratively read from the files within the determined time window, the data segments are concatenated. DTS, injection, pumping, and production data are also read from files within a determined time window. However, for the DTS injection/pumping or production data, due their significantly smaller data size compared to DAS or microseismic, the entire data set can be directly loaded if each is provided in a single file for the entire duration.

At data processing 104, a workflow is implemented. In examples, the workflow is based on the type of data being processed. An example preprocessing workflow is provided in FIG. 3 for DAS. The same workflow also applied to microseismic data. FIG. 3 shows a multi-trace DAS data 300A example; trace selection 300B based on the fracking induced DAS amplitude, short-time spectral analysis 300C of each DAS channel, and a partition 300D of DAS spectrogram into 1 second samples. Microseismic data follows the same preprocessing workflow.

Referring again to FIG. 1, the preprocessing 104 includes data cleaning 112. Many of the input data and output variables are measured and can be either noisy, missing, or corrupted by outliers due to instrument malfunction or errors. Given that all these measurements are for physical quantities with expected value ranges, range criteria is applied to each type of data to remove out-of-range samples, after removing missing or NaN data entries via data cleaning 112. Various statistical outlier detection algorithms are applied to remove outlier samples. This is done to both the input data, as well as the output variables for training and validation purpose. A quick way to detect and visualize the data quality is via multidimensional histogram or cross plots. FIG. 2 shows an example histogram for the output variables. In particular, FIG. 2 is a two dimensional (2D) histogram of a pumping data set. In the case of outliers or samples with value way out of the bounds, that will cause the distribution to be extremely skewed.

Referring again to FIG. 1, during preprocessing 104 spectral analysis and weighted averaging 114 is shown. In examples, the plot 300C of FIG. 3 shows a short-time spectral analysis of each DAS channel. The plot 300D shows a partition of the DAS spectrogram into 1 second samples. The spectral analysis and weighted averaging 114 begins by computing the short time Fourier transform of the DAS and/or microseismic data as follows:

$\text{?}$ $\text{?}\text{indicates text missing or illegible when filed}$

Here (ω,) is the spectrogram of the ith channel DAS data at frequency ω and sampled at every Δ seconds for m=1, . . . M. (n) is the window function such as the hamming window or blackman window of length L.

Take the average of the spectral signal from traces that have the strongest signal:

$S\left(\omega ,m\right)=\frac{1}{N_{\mathrm{DAS}}}{\sum }_{i=\mathrm{bottom}}^{\mathrm{top}}{S}_i\left(\omega ,m\right)=\frac{1}{N_{\mathrm{DAS}}}{\sum }_{i=\mathrm{bottom}}^{\mathrm{top}}{X_i\left(\omega ,m\right)}^2$

Generally raw DAS data has very high sampling frequency but the most informative frequency band are under certain cutoff frequency f max. If the frequency sampling interval is Δf, then the number of frequency samples is Nf=f max/Δf. The resulting DAS spectrogram is of dimension Nf×M×Ndas, the averaged DAS spectrogram is of dimension Nf×M.

For ease of explanation, the following Example 1 is provided. Example 1, consider a set of Ndas=493 channel DAS data of duration Tdas=3000 s, sampled at fs,das=2 kHz, for spectrogram with a window length L=2000, time step every 10 samples or 5 ms, f max=100 Hz, and Δf=1 Hz. In this example, spectrogram of dimension 101×(200×3000) for a single channel is determined, with a total 493 of these spectrograms. This contributes a significant amount spectral data. FIG. 4 shows an example single channel DAS spectrogram 400. In the spectrogram 400, time is plotted on the x-axis 402 and the frequency is plotted on the y-axis 404. The example of FIG. 4 shows a spectrogram 400 of microseismic data for 3000 seconds. The frequency components above 100 Hz are discarded. FIG. 6 shows an example single channel microseismic spectrogram. In the spectrogram 600, time is plotted on the x-axis 602 and the frequency is plotted on the y-axis 604. The example of FIG. 6 shows respective spectrograms of microseismic data for 3000 seconds. The frequency components above 1000 Hz are discarded.

In some embodiments, the DTS data is directly used as the network input without the Fourier transformation. For example, the DTS data in the study range is averaged along the measured depth, resulting in one dimensional (1D) vectors as the input data. The DTS data is recorded in a much lower frequency and compared to the DAS/microseismic data, so the DTS is linearly interpolated to have the same time resolution as the DAS and micro-seismic spectrograms. In examples, linear interpolation is applied to the DTS data such that is has the same number of time points as the Short-time Fourier transform (STFT) of the DAS and micro-seismic data. Then the DTS data according to the depths of chosen traces is determined, the average value computed as follows:

${\mathrm{DTS}}_{1D}\left(t\right)=\frac{1}{N_{\mathrm{DTS}}}\sum _{i=\mathrm{bottom}}^{\mathrm{top}}{\mathrm{DTS}}_{1D,i}\left(t\right)$

FIG. 5 shows an example of multichannel DTS data 500A, as well as the averaged DTS plot 500B. In the multichannel DTS data 500A, the data is vertically distributed, with data in the y direction averaged. The x-axis 502 represents time, while the y-axis 504 represents a measured depth. In the averaged DTS plot 500B, the x-axis represents time, while the y-axis 506 represents temperature.

In embodiments, the DTS vector is expanded to 2D matrices to be consistent with the DAS and micro-seismic spectrograms. This is done by the outer product:

DTS_2D=υ ⊗ DTS_1D

where DTS1D∈Nt is the DTS data after the linear interpolation, υ∈^Nf is a randomly initialized vector and N_f is the number of frequency points of the STFT results. After the outer product, the DTS data has size of N_f×N_t. The outer product vector v will be updated during the training process.

Referring again to FIG. 1, during preprocessing 104 trace/channel selection 116 is performed. Trace/channel selection 116 locates traces most relevant to the events of interest. For example, during a specific stimulation or production stage, data recorded in the area close to the fracking location or inflow location contains the most useful information. To locate the corresponding traces, the average amplitude of the DAS signal along the time axis is calculated. Referring to FIG. 3, plots 300B and 300C show trace selection based on the fracking induced DAS amplitude and a short-time spectral analysis of each DAS channel. As shown in the example of FIG. 3, some traces have much larger amplitudes than the others. In some embodiments, for the DAS data, four traces of the larges average amplitude are fetched as the input data. The STFT of each trace is computed. For the DTS data, the data within the measured depth covered by the 4 DAS traces is fetched.

Referring again to FIG. 1, during preprocessing 104 sample data generation 118 is performed. FIG. 3 shows sample data generation at the spectrogram 300D. In some embodiments, the DAS (and microseismic as well if available) spectrograms are truncated into snapshot images of size N_f×N_t where N_f is the number of frequency sample points, N_t is the number of time samples. In the Example I above, taking a snapshot of length 1 second without overlap, this gives 3000 sample snapshot spectrogram images of dimension 101×200.

The DTS data, and the output data will be sampled at the same rate as the DAS and microseismic spectrograms, synchronized, before they are broken into snapshot of the same length every time step of Δ seconds which, after converting into images as described above, will yield sample images of the same dimension and numbers as that of DAS above.

During preprocessing 104 of FIG. 1, normalization/scaling 120 is performed. Both the inputs and outputs data are recorded at each Δ seconds, including pressure and slurry rate for pumping data and pressure and flow rated for production. FIG. 6 shows 3000 seconds of outputs from the stimulation stage 10. The output will be linearly normalized to [−1, 1] during the training and testing of the neural network, based on the maximum and minimum values detected from the training dataset.

During preprocessing 104 of FIG. 1, sample data partition 122 is performed. The resulting set of sample data (DAS, microseismic and DTS snapshot images) are then randomly partitioned into a training set, validation set and testing set, according to, for example, 60%, 10% and 30% ratio. In some examples, the models were trained over 60% of the samples, validated over 20% of the samples, and tested over the remaining 20% of samples. This can be done using uniform distribution in the independent identically distributed (i.i.d) sample set model where different sample snapshot images are assumed independent (for instance, in convolutional neural network (CNN) type of models), or grouped into time sequences before being randomly partitioned in Recurrent Neural Network (RNN) models.

In the Example I above, with 60%, 10% and 30% ratio this will generate 1800 training samples, 300 validation samples, and 900 testing samples in the i.i.d. case. Data samples 106 are illustrated in FIG. 1. The data samples 106 are provided as input to machine learning models 108. In examples, the data samples 106 are preprocessed for training the machine learning models. In examples, the data samples 106 are preprocessed for input the trained machine learning models 108 for prediction of operation states and variables. In some embodiments, the machine learning models 108 are residual neural network (ResNet) models. FIG. 8 shows a ResNet deep learning neural network 800B. FIG. 9 shows a ResNet based model 902 followed by a recurrent neural network (RNN) model 904.

In examples, a ResNet is a network based on a structure called a “residual block” that uses skip connections, or shortcuts to jump over some layers, as shown by the residual block 800A. In particular, a skip connection 802 bypasses Layer I-2, directly connecting Layer I and Layer I-2. The ResNet extracts features from the input data. In examples, the ResNet-18 is a network based on a residual block with a total of eighteen layers. Multiple ResNets can be defined and used as well such as ResNet18, ResNet34, ResNet50 and etc.

In FIG. 8, a ResNet-18 model 800B is illustrated. ResNet-18 model 800B shows how the DAS data samples 810, microseismic data sample 812 and DTS data samples 814 are concatenated 816. The concatenated data samples are provided as input 818 into the ResNet network 820 before connecting to a regression layer 822 for prediction. In model 800B, the ResNet network 820 is directly connected to a regression output 822 for prediction. In this framework all samples are considered effective i.i.d.

In some embodiments, there are strong temporal dynamics in both the DAS, DTS and microseismic data as governed by the event physics (e.g. pumping, fracturing, injection or production). To explore the temporal dynamics, an RNN model is used to model sequential memories and dynamics, instead of learning the samples independently. Accordingly, in FIG. 9, DAS data samples 910, microseismic data samples 912 and DTS data samples 914 are concatenated 916. The concatenated data samples are transformed into a ResNet feature sequence 918, and provided as input to the ResNet-18+RNN network 920.

As shown in FIG. 9, ResNet-18 network 902 takes the ResNet feature sequence 918 as the input, and conducts prediction by connecting the state of the last hidden cell is connected to a fully connected layer to predict the outputs.

The whole network 920 structure is shown in FIG. 9, where the left part is the ResNet18 802 and the right part is a single-layer RNN 904. Each time a data sequence of length S is sent to the ResNet-18 902, it extract features of each sample in the data sequence, resulting in a sequence of feature vectors 924. The RNN 904 takes the sequence of feature vectors 924 as the input and predict the desired outputs. In some embodiments, the RNN 904 contains one layer based on LSTM cells. Note that in the left, the input DTS matrix is the outer product of the DTS vector and a parameter vector. The parameter vector is trained during the network training. The network can handle input of different channel numbers. Therefore, different combinations of the measurements and different ways of using multiple data traces are used. For the RNN 904, a larger sequence size will increase the memory usage while keeping the batch size. In some embodiments, the sequence size is set to be 10.

Accordingly, in an example, the ResNet+RNN model 920 includes a sequence length S=10. The number of time samples of 1 training sample is T=200. The number of frequency components under 100 Hz is set as F=101. D_Θ is the ResNet-18 network without the output layer. A sequence of output features of ResNet-18 is the input of the LSTM-RNN. The LSTM initial state (h₀, c₀) is trained as parameters, and the last state h_s of the LSTM is the prediction of the network.

In some embodiments, training the deep learning networks of FIGS. 8 and 9 includes normalizing the output data to the range of [−1, 1] based on a maximum and minimum value in the training set. Each sample output is a vector, e.g. in the pumping data case, of length 2, consisting of the values of both pressure and slurry rate. A sample data is a pair of input data (DAS, DTS and microseismic or any combination of these) and the output data (pumping pressure and rates, injection pressure and rates, or production pressure and rates). As discussed previously, the entire set of sample data are normalized, and randomly partitioned into training, validation and testing subsets.

The loss function is defined as the root mean squared error (RMSE) between the predicted output variables and the respective ground truth. The RMSE loss of the prediction:

$\text{?}$ $\text{?}\text{indicates text missing or illegible when filed}$

γ_i is the ground truth, γ_i^p is the network prediction. N is the number of samples in the batch. The training as well as testing performance in terms of RMSE for both models after 12 training epochs is provided in Table I. During training, various combinations of inputs were used, with and without DTS involved. Based on the pre-trained models, one could also retrain the network with different training/testing datasets.

TABLE 1
Prediction performance of both models
The RMSE (of normalized data) after 12 epochs of training
Dataset	Resnet18	Resnet18 + LSTM
DAS + MS	Training Set	5.5e−4	1.5e−4
	Test Set	1.3e−2	4.8e−3
DAS + DTS + MS	Training Set	6.0e−5	9.3e−5
	Test Set	3.0e−3	1.4e−3

In some embodiments, there are several different workflow combinations using the deep learning models of FIGS. 8 and 9. For example, in some embodiments, the deep learning neural network model is ResNet18 as shown in FIG. 8 or ResNet18-RNN as shown in FIG. 9. In examples, the ResNet18-RNN using sequence data shown in FIG. 9 can smooth the prediction and improve the predicting accuracy. In some embodiments, the inputs provided to the deep learning models is DAS or both DAS and DTS data. For example, the present techniques can train and test the deep learning neural network with two input combinations DAS data only and DAS data+DTS data. In some embodiments, the present techniques implement averaging traces or stacking traces. For example, two different ways of processing the multi-trace data is used, averaging the traces or stacking them as multiple channels. Additionally, in some embodiments the present techniques generalize the deep learning neural network within the same stage or over different stimulation stages.

In some embodiments, the present techniques enable constrained machine learning prediction. Note that the output quantities the machine learning models are trained to predict are physical properties of the pumping, injecting or production flows, e.g. the pressure and flow rates, and they are governed by the dynamics of the flow regimes and therefore are expected to be constrained rather than completely independent from each other. This has motivated the constrained machine learning prediction where the constraints are learned among the output variables as an additional training step. The learned constraints are then enforced onto the predicted outputs so that they satisfy the learned physics constraints.

In this case the loss of the prediction network is L_RMSE. The RMSE loss of the constraint network:

$\text{?}$ $\text{?}\text{indicates text missing or illegible when filed}$

γ_i^c is the output from the constraint network. The final loss function becomes:

l=l _RMSE +αL _RMSE ^Ľ

α is adjusted to control the importance of the constraint network. The loss function reverse back to L_RMSE when α=0.

In some embodiments, the deep learning neural network is tested and generalized. FIG. 10 shows a testing and generalization workflow. To evaluate the generalizability of the ResNet18-RNN network, the network is trained with data from one stimulation stage and use the trained network to predict outputs from a different stage, with and without retaining. It is essentially is a blind test without retraining. In some embodiments, 20% of the data from the testing stage is used to retrain the model. FIG. 11 shows blind test performance is given in FIG. 11, and FIG. 12 shows the prediction performance after retraining.

In some embodiments, the present techniques enable event localization. For example, for various applications, including hydraulic fracturing profiling, injection or production monitoring, it is important to be able to localize the events triggering or sustaining the measurement signals. For example, the fracking cluster locations, the production inflow distribution along the laterals, and the like. The present techniques integrate DAS, DTS and microseismic data if available, as well as the measured pressure and flow rates data, to identify and localize the events. Localizing the events determines a location, identified my coordinates, distance, or the like, of the event. FIG. 13 shows a workflow to predict the location of clusters in a stage and the fracking events. The DTS data temporal gradients 1302 and DAS data temporal gradients 1304 are obtained and synchronized with the measured pressure and flow rates (in this case they were measured on surface). The DAS spectrogram 1306 is integrated over a given frequency band and analyzed its spatial and temporal structure as shown in FIG. 13. The spectrogram 1306 ranges from 100-1000 Hz. The negative temperature calculated from DTS is shown as a negative DTS 1308. The DAS spectrogram and negative DTS are then integrated over time to generate respective depth profiles 1310 and 1312, which can then be used to pick the event locations (e.g., cluster locations). In this manner, machine learning based methods are used to quantitatively predicting hydraulic fracturing profiling from fiber optic DAS data, DTS data, microseismic data, or any combination of these data. The associated events are identified and localized.

In some examples, DAS data, DTS data, and microseismic data measured during the actual hydraulic fracturing pump phases is obtained, and the pump data (e.g., pressure and slurry rate) directly predicted from the DAS/DTS and microseismic measurements over the monitored stages. In some embodiments, microseismic data is optional. FIG. 14 shows an example plot of these data in single channel time (DTS 1402, pressure 1408 and slurry rates 1410) or time-spectral domain (DAS 1404 and microseismic 1406). FIG. 14 is an illustration of the DAS/DTS/microseismic data synced with pressure and slurry rate data for one example stage. DAS and microseismic data are plotted in spectrogram for one channel each.

FIG. 15 shows the averaged DTS 1504 and the DAS FBE 1502 over the entire well depth synced in time with the pressure and slurry rates. The deep learning approach described herein includes inputs to the deep neural network model as the DAS/DTS and microseismic data, and the outputs the pumping pressure and slurry rates at each respective stage, as shown in FIG. 1. The overall approach is to train the deep neural network models to map the input DAS/DTS and the optional microseismic measurements into the pressure and rate values over the same time period, so that the prediction is as close as possible to the actual measured pressure and rates. Once trained, the models are then applied to the testing data within the same stage or to different stages as blind test. In some embodiments, the developed model and the training/testing procedure are extended to predict production data from DAS/DTS measurements. The benefit of choosing to work on pumping data are two folds: a. relatively accurate measurements of these data on the surface are known, therefore the labels can be expected to have high quality; b. the hydraulic fracturing process hence the pumping data have relatively short time span compared with production data and therefore the training process has relatively lower complexity and can be done in reasonably short time.

Specifically, the DAS/DTS/microseismic measurements and pumping data are collected from a large number of hydraulic fracture stages together, before randomly partitioning the samples into the training, the validation and the testing subsets. The training and validation sets are used to fit the deep learning models before they are applied to the testing set for performance evaluation.

The DAS and microseismic data are converted into spectral domain segmented over time. As shown in FIG. 8 and FIG. 9, two types of deep learning models are used according to the present techniques: I) A ResNet network, which maps time snapshots of these measurement samples to the synced pump data independently; and II) a convolutional LSTM sequence learning model maps time segments of these measurement samples into the synced pump data. Both models were trained over a randomly partitioned subset of samples before applied to the remaining testing samples.

FIG. 16 is a process flow diagram of a process for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning.

At block 1602, distributed acoustic sensing (DAS) data, distributed temperature sensing (DTS) data, and microseismic data is captured over monitored stages. At block 1604, operation states and variables are predicted at a respective stage, based on, at least in part, the DAS data, DTS data, or microseismic data. At block 1606, at least one event associated with the predicted operation states and variables at the respective stage is localized. In some embodiments, the predictions are made using machine learning models. Deep learning based models and algorithms are deployed to directly predict pressure and slurry rates during hydraulic fracturing process from measured fiber-optic sensing data sets, including DAS, DTS and optically microseismic data. The deep learning models provide accurate and reliable prediction of these operation variables. When combined with efficient preprocessing of the large volume of fiber optic DAS/DTS data. This will enable and provide the first step towards an automated quantitative framework for intelligence completion and production monitoring, with minimal manual interpretation.

FIG. 17 is a schematic illustration of an example controller 1700 (or control system) for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning according to the present disclosure. For example, the controller 1700 may include or be part of the system of FIG. 1, the process of FIG. 16, and the like. The controller 1700 is intended to include various forms of digital computers, such as printed circuit boards (PCB), processors, digital circuitry, or otherwise parts of a system for supply chain alert management. Additionally the system can include portable storage media, such as, Universal Serial Bus (USB) flash drives. For example, the USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device.

The controller 1700 includes a processor 1710, a memory 1720, a storage device 1730, and an input/output interface 1740 communicatively coupled with input/output devices 1760 (e.g., displays, keyboards, measurement devices, sensors, valves, pumps). Each of the components 1710, 1720, 1730, and 1740 are interconnected using a system bus 1750. The processor 1710 is capable of processing instructions for execution within the controller 1700. The processor may be designed using any of a number of architectures. For example, the processor 1710 may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.

In one implementation, the processor 1710 is a single-threaded processor. In another implementation, the processor 1710 is a multi-threaded processor. The processor 1710 is capable of processing instructions stored in the memory 1720 or on the storage device 1730 to display graphical information for a user interface on the input/output interface 1740.

The memory 1720 stores information within the controller 1700. In one implementation, the memory 1720 is a computer-readable medium. In one implementation, the memory 1720 is a volatile memory unit. In another implementation, the memory 1720 is a nonvolatile memory unit.

The storage device 1730 is capable of providing mass storage for the controller 1700. In one implementation, the storage device 1730 is a computer-readable medium. In various different implementations, the storage device 1730 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.

The input/output interface 1740 provides input/output operations for the controller 1700. In one implementation, the input/output devices 1760 includes a keyboard and/or pointing device. In another implementation, the input/output devices 1760 includes a display unit for displaying graphical user interfaces.

The features described can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, for example, in a machine-readable storage device for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application specific integrated circuits).

To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer. Additionally, such activities can be implemented via touchscreen flat-panel displays and other appropriate mechanisms.

The features can be implemented in a control system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), peer-to-peer networks (having ad-hoc or static members), grid computing infrastructures, and the Internet.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. For example, example operations, methods, or processes described herein may include more steps or fewer steps than those described. Further, the steps in such example operations, methods, or processes may be performed in different successions than that described or illustrated in the figures. Accordingly, other implementations are within the scope of the following claims.

Other implementations are also within the scope of the following claims.

Claims

1. A computer-implemented method for quantitative hydraulic fracturing surveillance from fiber optic sensing using machine learning, the method comprising: capturing, with one or more hardware processors, distributed acoustic sensing (DAS) data, distributed temperature sensing (DTS) data, and microseismic data over monitored stages;predicting, with the one or more hardware processors, operation states and variables at a respective stage, based on, at least in part, the DAS data, DTS data, or microseismic data; andlocalizing, with the one or more hardware processors, at least one event associated with the predicted operation states and variables at the respective stage.

2. The computer-implemented method of claim 1, wherein the monitored stages are perforation and actual hydraulic fracturing pump phases.

3. The computer-implemented method of claim 1, wherein localizing the at least one event comprises determining a location of the event.

4. The computer-implemented method of claim 1, wherein the capturing, predicting, and localizing are performed in situ and in real time.

5. The computer-implemented method of claim 1, wherein the variables comprise pumping variables, production flow pressure and rates, and fracking cluster locations.

6. The computer-implemented method of claim 1, wherein the variables comprise slurry rates or pressures formulated from the DAS data, DTS data, and microseismic data over the monitored stages.

7. The computer-implemented method of claim 1, wherein the monitored stages occur over different well depth ranges.

8. An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: capturing distributed acoustic sensing (DAS) data, distributed temperature sensing (DTS) data, and microseismic data over monitored stages;predicting operation states and variables at a respective stage, based on, at least in part, the DAS data, DTS data, or microseismic data; andlocalizing at least one event associated with the predicted operation states and variables at the respective stage.

9. The apparatus of claim 8, wherein the monitored stages are perforation and actual hydraulic fracturing pump phases.

10. The apparatus of claim 8, wherein localizing the at least one event comprises determining a location of the event.

11. The apparatus of claim 8, wherein the capturing, predicting, and localizing are performed in situ and in real time.

12. The apparatus of claim 8, wherein the variables comprise pumping variables, production flow pressure and rates, and fracking cluster locations.

13. The apparatus of claim 8, wherein the variables comprise slurry rates or pressures formulated from DAS data, DTS data, and microseismic data over the monitored stages.

14. The apparatus of claim 8, wherein the monitored stages occur over different well depth ranges.

15. A system, comprising: one or more memory modules;one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations comprising:capturing distributed acoustic sensing (DAS) data, distributed temperature sensing (DTS) data, and microseismic data over monitored stages;predicting operation states and variables at a respective stage, based on, at least in part, the DAS data, DTS data, or microseismic data; andlocalizing at least one event associated with the predicted operation states and variables at the respective stage.

16. The system of claim 15, wherein the monitored stages are perforation and actual hydraulic fracturing pump phases.

17. The system of claim 15, wherein localizing the at least one event comprises determining a location of the event.

18. The system of claim 15, wherein the capturing, predicting, and localizing are performed in situ and in real time.

19. The system of claim 15, wherein the variables comprise pumping variables, production flow pressure and rates, and fracking cluster locations.

20. The system of claim 15, wherein the variables comprise slurry rates or pressures formulated from DAS data, DTS data, and microseismic data over the monitored stages.