PreprintPDF Available

Prediction of the continuous probability of sand screen-out based on a deep learning workflow

Preprints and early-stage research may not have been peer reviewed yet.
* Corresponding author.
E-mail address: (L. Hou).
Prediction of the continuous probability of sand screen-out based on a deep
learning workflow
Lei Hou a, *, Yiyan Cheng b, Derek Elsworth c, Honglei Liu d, Jianhua Ren b
a School of Engineering, The University of Warwick, Coventry CV4 7AL, UK
b Research Institute of Exploration & Development, East China Company of SINOPEC, Nanjing 210011, China
c Energy and Mineral Engineering, EMS Energy Institute and G3 Center, Pennsylvania State University, University Park,
16802, USA
d SINOPEC Research Institute of Petroleum Engineering, Beijing 100101, China
Sand screen-out is one of the most serious and frequent challenges that threaten the efficiency and
safety of hydraulic fracturing. Current low prices of oil/gas drive operators to control costs by using
lower viscosity and lesser volumes of fluid for proppant injection - thus reducing the sand-carrying
capacity in the treatment and increasing the risk of screen-out. Current analyses predict screen-out as
isolated incidents based on the interpretation of pressure or proppant accumulation. We propose a
method for continuous evaluation and prediction of screen-out by combining data-driven methods
with field measurements recovered during shale gas fracturing. The screen-out probability is updated,
redefined and used to label the original data. Three determining elements of screen-out are proposed,
based on which four indicators are generated for training a deep learning model (GRU Gated
Recurrent Units, tuned by the Grid search and Walk-forward validation). Training field records
following screen-out are manually trimmed to force the machine learning algorithm to focus on the
pre-screen-out data, which then improves the prediction of the continuous probability of screen-out.
The Pearson coefficients are analyzed in the STATA software to remove obfuscating parameters from
the model inputs. The extracted indicators are optimized, via a forward selection strategy, by their
contributions to the prediction according to the confusion matrix and root mean squared error
(RMSE). By optimizing the inputs, the probability of screen-out is accurately predicted in the testing
cases, as well as the precursory predictors, recovered from the probability evolution prior to screen-
out. The effect of pump rate on screen-out probability is analyzed, defining a U-shaped correlation
and suggesting a safest-fracturing pump rate (SFPR) under both low- and high-stress conditions. The
probability of screen-out and the SFPR, together, allow continuous monitoring in real-time during
fracturing operations and the provision of appropriate screen-out mitigation strategies.
Keywords: hydraulic fracturing; screen-out; deep learning; continuous probability; pump rate
1 Introduction
Sand screen-out occurs where the proppant in the fracturing fluid creates a bridge across the
evolving fluid-driven fracture, constricts the flow area (eg. the perforation hole, fractures, etc.) and
results in a rapid rise in injection pressure during hydraulic fracturing (Sun et al. 2020; Economides
and Nolte 1989). Screen-out is considered to be one of the most serious problems that threaten the
safety of equipment and individuals during fracturing, reduce the stimulated production and result in
cost overruns due to the time and material consumption related to wellbore clean-up operations and
equipment maintenance (Cleary et al. 1993; E.V. Dontsov and Peirce 2014).
Previous research on the fracturing of conventional reservoirs suggests that the near-wellbore
tortuosity of fractures may play an important role in causing screen-out, in addition to fluid leak-off,
proppant beach-formation and other factors, according to experiments and field measurements (Barree
and Conway 2001; Aud et al. 1994; Daneshy 2007). Based on these mechanisms, screen-out may be
potentially predicted by interpreting time histories of fracturing pressure based on log-log plots (Nolte
and Smith 1981), calculating the configuration of the proppant distribution in fractures (Cai et al.
2017), diagnosing the connection between wellbore and formation through the perforations based on
acoustic measurements (Merry and Dalamarinis 2020), or inspecting the variation with time of the
treatment pressures (Massaras and Massaras 2012), among other indicators. Data-driven approaches
have also been introduced for prediction by integrating the inverse slope method with deep learning
algorithms (CNN-LSTM) (Sun et al. 2020), predicting the fracturing pressure using the locally-
weighted linear regression and CNN-RNN (J. Hu et al. 2020; Ben et al. 2020), and learning pre-
screen-out patterns in the simulated pressure signals (Yu, Trainor-Guitton, and Miskimins 2020).
For unconventional reservoirs, the screen-out mechanism may be more complex, due to larger
volumes of injection, higher pumping rates, elevated injection pressures and multi-staged operations
in the horizontal wellbore (Weng et al. 2011; Li et al. 2015). The resulting complex fracture networks,
compared with the single high-conductivity bi-wing fracture typical in conventional vertical wells, are
required to maximize the stimulated reservoir volume (Warpinski et al. 2009; Qi et al. 2012). The
pressure fluctuations are also more severe than in conventional reservoirs. Rapid rises and drops in
pressure are commonly observed in shale gas fracturing (Roussel, Manchanda, and Sharma 2012),
making the previous recovery of diagnostics from the pressure curve more difficult to recover.
Besides, most of the previous efforts provide the screen-out prediction as a discrete classification
result (i.e. screen-out or non-screen-out). The sudden appearance doesn’t reflect the inducements
before the screen-out and also may reserve limited time for the operator to make judgments and
We, therefore, focus on the continuous diagnosis of screen-out by field data processing, attempting
to improve the fidelity of the early warning and define the form of signals that are precursory and
robustly diagnostic of an impending incident. Since mild screen-out could be useful in increasing the
net pressure in the driven fracture and thereby enhance the stimulated volume of the reservoir
(Massaras and Massaras 2012; E. Dontsov and Peirce 2015), the definition of screen-out is first
constrained into the most critical level where fracturing operations are suspended in the open well by
the release of pressure and cleaning of the wellbore is mandated. The determining-element
mechanisms of screen-out are proposed and used to redefine and label the screen-out probability,
which considers a greater number and broader variety of influence factors compared with previous
efforts that have mainly relied on pressure interpretation or proppant accumulation (Massaras and
Massaras 2012; Cai et al. 2017). The pump rate, one of the determining elements, is fitted for the
safest value where the probability of screen-out approaches the minimum. STATA software is used
for the optimization of the model inputs with a deep learning workflow trained then tested against
rigorously selected field examples with known outcomes. The resulting continuous screen-out
probability monitoring and the safest fracturing pump rate may then be used to improve early warning
of screen-out and allow mitigation in real-time to promote safe and efficient fracturing operations.
2 Methodology
New hypotheses of screen-out are proposed to redefine the screen-out probability and extract
indicators for the screen-out predicting. In this work, the definition of screen-out is constrained to the
most serious condition where open-well flushing is necessary since this is the basis on which our
field cases are collected. STATA software and a GRU model are applied to these datasets for
parameter analyses and predictions of screen-out probability.
2.1 Hypotheses of screen-out
(i) Determining elements hypothesis: Three determining elements of screen-out for shale gas
fracturing are proposed namely, pump rate, fracture volume/capacity and proppant accumulation
(Novotny 1977; Patankar et al. 2002; Dahi-Taleghani and Olson 2011; Aud et al. 1994). The
mechanism of screen-out is presumed to result from parameter mismatch among the determining
elements. New indicators are generated based on the element hypothesis to aid machine interpretation
of the raw data. Screen-out probability is redefined by fixing two of the elements.
(ii) Linear-correlation hypothesis: For shale gas fracturing, the pump rate for proppant injection is
near-constant. We assume that the probability of screen-out has a linear relationship with the injected
volume of proppant under constant pump rate and fixed fracture capacity. Then, the screen-out
probability is defined as the ratio of injected proppant volume to the maximum capacity (total
cumulative volume of injected proppant before the restricted screen-out), which is then used to label
the original data. According to this hypothesis, the ideal screen-out probability curve (probability-vs-
fracturing time) is a smooth and continuous increasing line. However, the fracture is considered to
continue propagating during the fracturing operation, which results in a continuous increase in
fracture volume with time (Manchanda et al. 2020; Dahi-Taleghani and Olson 2011). Therefore, the
actual probability may fluctuate regularly around the ideal curve, but with relatively small departures,
due to this continuing fracture propagation.
2.2 Data preparation
2.2.1 Data collection
The field data are collected based on the restricted definition of screen-out (the fracturing
operation is suspended to release pressure and clean the wellbore). 25 stages of fracturing data are
collected from shale gas wells in the Sichuan basin, China, including ~120,000 groups of field
measurements. The parameters in each group consist of both the geological features (well depth,
vertical depth, minimum horizontal stress) and treatment/fracturing records (pump rate, fluid and
proppant type, wellhead pressure, proppant concentration, shut-in pressure and stage length). The
original fluid and proppant types are non-numeric parameters and are replaced by the fluid viscosity
and proppant diameter, as representative parameters of performance, respectively.
The data are split into the training (21 stages) and testing datasets (4 stages) for model training and
prediction, as shown in Table 1. The testing stages are rigorously selected by well locations. The
geological and in-situ stress conditions vary as the increasing well distance, which may increase the
difficulty of model prediction, thus providing a more reliable evaluation of the model performance
and application range. One of them is from the same well (Well A) that is also used for model
training. The other three stages come from new wells different from the training wells, one of which is
from a neighbouring well (Well B), and the other two from remote wells (Wells C and D are more
than 200 km distant from all other wells). All wells, noted in Table 2, are drilled within the same
Table 1 Division of training and testing datasets
Training sets / stage
Testing sets / stage
Training wells
Well A
From one of the training wells
Well B
From a neighbouring well next to
training wells
Well C
From a well in a different location
of the basin
Well D
2.2.2 Data labelling, trimming and extracting
Screen-out probability is calculated and used to label the original field measurements according to
our most severe definition. We truncate the field record following screen-out to direct the machine to
the experience pre-screen-out. Although the training results may be influenced by the missing data, it
boosts the interpretation of the pre-screen-out data, then benefits the continuous monitoring for the
generation of screen-out. It may also prevent the model from cheating by recognizing the overpressure
or pump-off for screen-out, which bypasses the deeper interpretation of data. Similar data processing
is also reported by previous research (Yu, Trainor-Guitton, and Miskimins 2020).
Four new indicators of screen-out are proposed based on the element hypothesis to assist the
model prediction the downhole pressure after hole perforation (DPP), the ratio of the accumulated
volume of injected proppant to that of injected fluid (Vs/Vf), the wellhead pressure change in a unit
volume of injected fluid (ΔP/ΔVf) and the ratio of minimum-horizontal stress to shut-in wellhead
pressure (σmin/Ps). The DPP, calculated from the wellhead pressure, may reflect the pressure variation
induced by fracture propagation and new fracture generation (E.V. Dontsov and Peirce 2014;
Willingham, Tan, and Norman 1993). Details of pressure conversion can be found in Appendix A.
The Vs/Vf ratio represents proppant accumulation in fractures. The ΔP/ΔVf ratio is similar to the
pressure slope returned under a constant pump rate (Massaras and Massaras 2012). The σmin/Ps ratio
reflects the change in in-situ stress due to the material injection (Hayashi and Haimson 1991).
2.3 Data processing tools
The parameter analyses are carried out in STATA 17.0 to assist the selection of model inputs. The
data training and testing are performed using a GRU model built in the Spyder environment. The
GRU algorithm, designed for extracting information from time sequences (Cho et al. 2014), has
performed well in various petroleum engineering applications (Wang et al. 2019; Sun et al. 2020).
According to previous experience (Fan et al. 2021; Sagheer and Kotb 2019), a three-layer (including
the output layer) GRU is established with the activation function ‘ReLu’ operating in each layer (Gal
and Ghahramani 2015). The regularization is performed to avoid overfitting by setting a dropout rate
(of 0.2) behind the first and hidden layers. The Adam is selected as the optimizer to compile the
model, where a callback function is applied to return and automatically update the learning rate
(Kingma and Ba 2014; Zeiler 2012). Other hyperparameters, including the number of neural units
(30), epochs (30) and batch size (100) are optimized by the Grid search and Walk-forward validation
(Bergstra and Bengio 2012; M.Y. Hu et al. 1999; Stein 2002). Details of model tuning can be found in
Appendix B.
The workflow of data processing is graphically illustrated in Fig. 1. The original field
measurements and extracted indicators are analyzed by the Pearson correlation coefficients in
STATA. The GRU model is initially trained using the selected original parameters, and the results are
used as a reference. The indicators are appended to the inputs successively for optimization based on
the forward selection strategy. The model performance is promoted by indicator optimization and is
verified by the testing cases. The deep learning workflow proposed in this study has high feasibility
for different application scenarios if provided with the corresponding datasets for the model training.
It is also possible to promote the performance of the workflow by feeding into new data.
Fig. 1 The workflow of data processing, consisting of data collection, pre-processing and model training.
3 Results
Interfering parameters are removed from the model inputs according to the Pearson coefficient
analyses. The confusion matrix and the root mean squared error (RMSE) are used as criteria for the
model evaluation, in which the RMSE has the same unit as the prediction (Chai and Draxler 2014).
The optimized indicators improve the prediction by halving the reference RMSE and eliminating the
false-negative errors. Successful reports of screen-out and accurate predictions of its probability are
achieved and may be used in continuous monitoring of fracturing operations.
3.1 Pearson correlation coefficient analyses
The Pearson correlation coefficient is the covariance of the two variables divided by the product of
their standard deviations and is used to quantify the potential interference between variables (Benesty
et al. 2009). The inputs of the deep learning model are analyzed in STATA, and the results are
summarized in Table 2. The higher the coefficient is, the stronger the interference that may exist
between the two variables. Using the coefficient as a reference, we remove the variable that may
significantly interfere with other multiple variables. The Well depth shows high coefficients with the
Vertical depth and Stage length, as well as the Vertical depth with Minimum-horizontal-stress,
Wellhead pressure and DPP. Therefore, the Well depth and Vertical depth are removed from the
inputs of the model training.
The optimization of model inputs is performed with care and with appropriate reference to
engineering experience and preserving as much parameter/information as possible for the GRU
neuronal units. For instance, the large coefficient linking proppant diameter and proppant
concentration may be due to the characteristics of the data (they are always present in pairs). These
variables are reserved because the diameter and concentration of proppant may each contribute to
screen-out but by different mechanisms (sealing the hydraulic fracture alternately by bridging or
filling, respectively.). There is also a concern that removing the proppant-related features may mislead
future works when collecting the original data.
Table 2 The Pearson correlation coefficients between training variables
Notes: The original measurements: (1) Well depth; (2) Vertical depth; (3) Min-horizontal stress; (4) Stage
length; (5) Fluid viscosity; (6) Pump rate; (7) Proppant concentration; (8) Proppant diameter; (9) Wellhead
pressure. The extracted indicators: (10) DPP; (11) Vs/Vf; (12) ΔP/ΔVf; (13) σmin/Ps. The dependent variable: (14)
Screen-out probability.
3.2 Screen-out prediction based on original measurements
The original field measurements, parameters (3) to (9) in Table 2, are used as inputs to the GRU
model for screen-out probability prediction. The results are shown in Fig. 2, where the time histories
of pump rate, proppant concentration and wellhead pressure are also presented. The confusion matrix
and the RMSE are used as dual criteria for evaluating the predictions. We define that a predicted
screen-out probability higher than 0.9 generates a report of screen-out. The workflow based on the
original parameters fails to give the correct screen-out warning in three of four testing cases. Three
false-negatives (FN error, in Wells A, C and D cases, as shown in Figs. 2 a, c and d), four false-
positives (FP error, in Wells B and D cases, as shown in Figs. 2 b and d) and only one correct
prediction are reported, as shown in Table 3. There is essentially no pattern for the predicted curves
that produce RMSEs of 0.230 (Well A), 0.236 (Well B), 0.150 (Well C) and 0.369 (Well D),
respectively, compared with the labelled probability.
Fig. 2 Prediction of screen-out probability using the original measurements from (a) Well A, (b) Well B,
(c) Well C and (d) Well D. The blue dashed circle marks the false alarm of screen-out in Wells B and D.
The solid orange line is the labelled screen-out probability curve. The dashed line is the predicted
probability curve. The wellhead pressure, pump rate, proppant concentration are presented to show the
operation procedures.
Table 3 The confusion matrix of the predictions based on original parameters
Predicted events
1 (Correct)
3 (FN error)
4 (FP error)
3.3 Prediction promotion by optimizing indicators
The predictions are improved by optimizing the extracted indicators compared with the results
based on original measurements. The forward selection strategy is applied for the optimization, during
which the indicator is appended sequentially to the model inputs. The primary indicator that promotes
the prediction is selected, and the others decreasing model performance are abandoned.
The first testing indicator is decided by regression analyses using the OLS (ordinary least squares)
method (executed in STATA) to produce the best prediction with the least indicators. The results are
summarized in Table 4. The Vs/Vf ratio increases the R-squared value more than do the other
indicators (from 0.483 to 0.799), indicating the most significant contribution of Vs/Vf to the screen-out
probability prediction. Therefore, the Vs/Vf is chosen as the first testing parameter for the forward
Table 4 Regression results between indicators and screen-out probability
Screen-out probability
Min-horizontal stress
Stage length
Fluid viscosity
Pump rate
Proppant concentration
Proppant diameter
Wellhead pressure
The results of the forward selection are presented in Table 5, using the confusion matrix and
RMSE as the dual criteria. The introduction of Vs/Vf boosts the model performance by significantly
reducing the RMSE and increasing the number of correct predictions by a single instance. The
combination of Vs/Vf and DPP in the second round selection predicts the ultimate occurrence of
screen-out events in three of four testing cases and halves the number of false-positives. By adding the
ΔP/ΔVf ratio, the workflow successfully reports all of the screen-out events and produces the lowest
RMSE in all testing rounds. Therefore, the indicators in the first three rounds are all selected. The
σmin/Ps in the last round is abandoned due to the increasing RMSE and false-negative error, as shown
in Table 5.
Table 5 Results of the indicator optimization by the forward selection
Indicators appended
to model inputs
Correct report
/Total event
Round 1
Round 2
Vs/Vf + DPP
Round 3
Vs/Vf + DPP +
Round 4
Vs/Vf + DPP +
ΔP/ΔVf + σmin/Ps
The predictions in the third-round test are shown in Fig. 3. Three false-positives are observed for
Wells C and D that are located distant from the training cases (wells), as marked by dashed circles in
Figs. 3 (c) and (d). The false-positive error (the false alarm) is considered less serious than the false-
negative error (the missing alarm) (Pounds and Morris 2003). Besides, the false alarms are reported at
the end of the fracturing operation, and about 15 minutes ahead of the actual screen-out, which may
be acceptable. Therefore, the GRU-based workflow, trained by the original and extracted parameters,
reports accurate screen-out warnings and exerts stable performance in rigorous applications. The
continuous monitoring of screen-out probability allows the field operator to receive pre-warning and
allow rapid response in near-real-time, which is more efficient and practical than the previous discrete
Fig. 3 Promoted predictions of screen-out probability by model inputs optimization based on (a) Well
A, (b) Well B, (c) Well C and (d) Well D. The inputs of the model are consist of the original measurements
and three optimized indicators (Vs/Vf + DPP + ΔP/ΔVf). The blue dashed circle marks the false alarm of
the screen-out in the Wells C and D cases. The solid orange line is the labelled screen-out probability
curve. The dashed line is the predicted probability curve.
4 Discussion
4.1 Inducement analysis based on continuous probability
The probability curve before the screen-out also provides clues for the inducement analysis. In Fig.
3 (a), the long proppant injection slug between 4000s and 5000s increases the screen-out probability
near to the value of 0.8, indicating that the continuous-proppant-injection length may be one of the
key factors causing the screen-out in this case. In Fig. 3 (b), a vertical ascent of probability is
observed around 3000s, when the 40/70 mesh proppant is injected right after the 100 mesh one. The
fracture may not be well cracked with a relatively narrow width that is sensitive to larger proppant
grain. The underdeveloped fracture may be an important inducement for Well B. The pressure
fluctuations in Well A and B also show similar periods to the probability variations.
For Wells C and D, the sign showing in pressure is inconspicuous because it deviates only slightly
from the norm, as shown in Figs. 3 (c) and (d). The probability can still define distinct indications
precursory to screen-out. A long proppant slug may be one of the main reasons causing the screen-out
in Well C, according to the rapid climb in probability between 6000s and 8000s in Fig. 3 (c).
Meanwhile, a different mechanism prompting screen-out is disclosed in Well D. The probability
begins increasing after 2000s when four slugs of fine proppant (100 mesh) are injected. In the 7th, 8th,
11th and 12th proppant slugs, 100 and 40/70 mesh proppants are successively injected (the short
vertical lines in the middle of those concentration curves denote the switching between proppants),
which all cause significant fluctuations in the probability curve at ~4000s and ~6000s. Conceivably,
fine proppant may have filled micro- and secondary fractures, which should be beneficial to the
stimulation. However, the 30/50 mesh proppant, not commonly used due to its large size, is injected
during the last slug. The larger diameter proppant builds pressure in the main fractures, which may be
difficult to be relieved as a result of staunched flow through the fully-packed minor fractures - hence
triggering a screen-out.
According to the inducement analyses, the proppant may be injected slower to control the slug
length for Wells A and C. Larger proppant may be switched later to allow a sufficient propagation of
fracture for Well B. The amount of fine proppant should be controlled, especially when large diameter
proppant is employed for Well D. However, adopting fine-grained proppant can be a double-edged
sword. Certainly, it fills the minor fractures and boosts the growth of main fractures by increasing the
net pressure. However, conversely, it may also reduce the pressure tolerance and increase the
probability of screen-out, thus should be used with caution.
4.2 Support of the screen-out hypotheses
The predicted probability of screen-out varies in a similar period to the wellhead pressure
variations (Fig. 3), indicating that the GRU model interprets the relationship between wellhead
pressure and screen-out events when the data are manually trimmed. The predicted curves fluctuate
around the labelled curves due to the finite and substantial variation in evolving fracture volume,
which supports the linear hypothesis of screen-out probability. In addition, the Vs/Vf ratio represents
the proppant accumulation in downhole fractures. The DPP and ΔP/ΔVf variations could result from
the pressure response to fracture creation and propagation. The combination of these indicators
significantly promotes predictions, which supports the determining elements hypothesis.
Additionally, the variation of underground fracture is currently difficult to detect and is a non-
directly-adjustable parameter. Proppant accumulation may be the most significant indicative element
considering the RMSE improvement in Table 5. Pumping rate is the single most important element
that may be adjusted to ensure the operation safety. Emergency termination or reduction in pumping
rate is commonly used to control the pressure when the first signs of screen-out appear (Yew and
Weng 2014). Therefore, it is essential to reveal the effect of pump rate on screen-out probability based
on both training and testing datasets.
4.3 Effect of pump rate on screen-out probability
A U-shaped correlation between pump rate and screen-out is proposed according to the
observation that both extremely low (aggravating the proppant settling and dune building-up) and
extremely high (increasing the frictions and reshape the proppant dune) pump rates may result in sand
screen-out (Harris and Pippin 2000; Willingham, Tan, and Norman 1993). This is demonstrated by
fitting the correlation between the square-of-pump-rate with screen-out probability in STATA bases
on both training and testing datasets. A small p-value (a smaller p-value reflects stronger evidence in
favour of the hypothesis) and a positive coefficient are obtained, as shown in Table 6, which supports
the U-shaped form hypothesis.
Table 6 Summary of regressions between the squared pump rate and screen-out probability
Screen-out probability
Pump rate
Squared pump rate
Stage length
Fluid viscosity
Proppant concentration
Proppant diameter
Wellhead pressure
Notes: *** p<0.01, ** p<0.05, * p<0.1.
A flat U-shaped curve is observed with a minimum where the screen-out probability is lowest, as
shown in Fig. 4. The pump rate at this minimum probability is 5.94 m3/min based on the collected
data in this work, which is defined as the safest fracturing pump rate (SFPR). An adjustment in pump
rate is one of the most essential and frequent measures to maintain pressure and ensure safety during
shale gas fracturing (Yew and Weng 2014). The newly defined SFPR can be used as a reference for
the real-time pump rate adjustment, which may aid in rapidly reducing the pump rate to the safest
range to prevent or mitigate screen-out.
Fig. 4 U-shaped correlation between squared pump rate and screen-out probability. The vertical
dashed line marks the pump rate at the turning point, which is 5.94 m3/min.
The moderating effects of in-situ stress on the SFPR are studied based on both the low(er) and
high(er) stress datasets, split by the median value (55 MPa) of the minimum-horizontal stress. These
results are presented in Fig. 5. The SFPR moves rightward under the low-stress condition and grows
to 8.33 m3/min at the minimum probability. This reduces to 4.25 m3/min for high(er)-stress
conditions. In addition, the U-shaped curve is steeper for the low-stress condition, indicating that the
probability of screen-out is more sensitive to a variation in pump rate and that the lifting of pump rate
should be pursued with moderation. It is noteworthy that the SFPR may not be the most effective
choice in practice since the operator may be willing to take certain risks to improve the economics of
the stimulation job. However, it could be an important index to prompt pump rate adjustment to
minimize loss when the first signs of the screen-out appear.
Fig. 5 Moderating effects of in-situ stress on the U-shape correlation. The vertical dashed line marks
the extreme point excursion under (a) low-stress and (b) high-stress conditions. The pump rates at the
turning points are 8.33 and 4.25 m3/min, respectively.
5 Conclusions
A new awareness of screen-out mechanisms is highlighted by proposing and testing hypotheses
based on a deep learning (GRU algorithm) workflow and the STATA software. Shale gas fracturing
measurements are collected with a strict definition of screen-out applied (when the operation is
suspended for pressure-release and wellbore-cleaning) to allow the binning of data. Time histories of
fracturing parameters are truncated after screen-out to force the GRU model to interpret the pre-
screen-out data this is shown to improve the prediction of screen-out occurrences. A forward
selection strategy is applied to optimize the model inputs, based on which accurate screen-out
probability predictions are obtained. The major conclusions may be generalized as follows:
(1) By tuning the GRU model and optimizing the inputs, an accurate screen-out probability is
produced in the testing cases for wells at different distances from the training wells. The GRU-based
workflow successfully reports all the screen-out events and constrains the RMSE to ~0.089. The
resulting continuous probability curve aid the field engineer to effectively monitor the evolution of
screen-out, make rapid pre-judgements and prescribe mitigating actions in real-time. Besides, the
variation in probability precursory to screen-out provides valuable clues regarding triggering
mechanisms. For example, the length of the proppant slug, the timing of the switching of proppant
sizes and the use of fine proppant are analyzed for the testing cases based on the evolution of the
probability curves. Explicit strategies are proposed for optimizing the proppant selection and
(2) Fracture volume, proppant accumulation and pump rate are proposed as three discriminating
elements potentially contributing to screen-out, with these applied in data pre-processing in the
workflow. This presumption is supported by producing the smallest prediction errors in the
optimization of model inputs. Unique features of the mismatch among these elements may provide
simple diagnostics for the mechanism of screen-out for shale gas fracturing. Fracture volume is a non-
directly-adjustable parameter that may be reflected by the pressure variations. Proppant accumulation
may be a significant indicative parameter for screen-out. Pumping rate is shown to be the most
important adjustable element that controls the fracturing pressure. This three-determining-element
mechanism extends the characteristic parameters for screen-out study compared with traditional
methods depending on pressure interpretation or proppant accumulation.
(3) A safest-fracturing pump rate (SFPR) is defined by fitting the U-shaped correlation between
the screen-out probability and pump rate this is 5.94 m3/min based on the collected data. The
moderating effect of in-situ stress on the SFPR shows that conditions of higher-stress reduce the
SFPR. Conditions of lower-stress increase the SFPR, but also increase the relief of the U-shaped
relation, indicating a higher sensitivity to pump rate that requires only a moderate adjustment. The
SFPR may be used as a reference to guide the rapid adjustment of the pump rate into the safest range
to potentially mitigate screen-out.
This research has received funding from the European Union's Horizon 2020 research and
innovation programme under the Marie Sklodowska-Curie grant agreement No. 846775.
Aud, WW, TB Wright, CL Cipolla, and JD Harkrider. 1994. The effect of viscosity on near-wellbore
tortuosity and premature screenouts. Paper presented at the SPE annual technical conference
and exhibition, New Orleans, Louisiana, USA, 2528 September. SPE-28492-MS.
Barree, RD, and MW Conway. 2001. Proppant holdup, bridging, and screenout behavior in naturally
fractured reservoirs. Paper presented at the SPE Production and Operations Symposium,
Oklahoma City, Oklahoma, USA, 2427 March. SPE-67298-MS.
Ben, Yuxing, Michael Perrotte, Mohammadmehdi Ezzatabadipour, Irfan Ali, Sathish Sankaran,
Clayton Harlin, and Dingzhou Cao. 2020. Real time hydraulic fracturing pressure prediction
with machine learning. Paper presented at the SPE Hydraulic Fracturing Technology
Conference and Exhibition, The Woodlands, Texas, USA, 46 February. SPE-199699-MS.
Benesty, Jacob, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation
coefficient. In Noise reduction in speech processing, 1-4. Springer.
Bergstra, James, and Yoshua Bengio. 2012. Random search for hyper-parameter optimization.
Journal of machine learning research 13 (2).
Cai, Xiao, Boyun Guo, Gao Li, and Xu Yang. 2017. A Semi Analytical Model for Predicting
Proppant Screen-Out During Hydraulic Fracturing Unconventional Reservoirs. Paper
presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Jakarta,
Indonesia, 1719 October. SPE-186174-MS.
Chai, Tianfeng, and Roland R Draxler. 2014. Root mean square error (RMSE) or mean absolute error
(MAE)?Arguments against avoiding RMSE in the literature. Geoscientific model
development 7 (3): 1247-1250.
Cho, Kyunghyun, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares,
Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN
encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Cleary, MP, DE Johnson, HH Kogsbøll, KA Owens, KF Perry, CJ De Pater, Alfred Stachel, Holger
Schmidt, and Tambini Mauro. 1993. Field implementation of proppant slugs to avoid
premature screen-out of hydraulic fractures with adequate proppant concentration. Paper
presented at the Low Permeability Reservoirs Symposium, Denver, Colorado, USA, 2628
April. SPE-25892-MS.
Dahi-Taleghani, Arash, and Jon E Olson. 2011. Numerical modeling of multistranded-hydraulic-
fracture propagation: accounting for the interaction between induced and natural fractures.
SPE journal 16 (03): 575-581. SPE-124884-PA.
Daneshy, A Ali. 2007. Pressure variations inside the hydraulic fracture and their impact on fracture
propagation, conductivity, and screenout. SPE Production & Operations 22 (01): 107-111.
Dontsov, E. V, and A. P Peirce. 2014. Slurry flow, gravitational settling and a proppant transport
model for hydraulic fractures. Journal of Fluid Mechanics 760: 567-590.
Dontsov, EV, and AP Peirce. 2015. Proppant transport in hydraulic fracturing: crack tip screen-out in
KGD and P3D models. International Journal of Solids and Structures 63: 206-218.
Economides, Michael J, and Kenneth G Nolte. 1989. Reservoir stimulation. Vol. 2. Prentice Hall
Englewood Cliffs, NJ.
Fan, Dongyan, Hai Sun, Jun Yao, Kai Zhang, Xia Yan, and Zhixue Sun. 2021. Well production
forecasting based on ARIMA-LSTM model considering manual operations. Energy 220.
Gal, Yarin, and Zoubin Ghahramani. 2015. A theoretically grounded application of dropout in
recurrent neural networks. arXiv preprint arXiv:1512.05287.
Harris, PC, and PM Pippin. 2000. High-rate foam fracturing: fluid friction and perforation erosion.
SPE Production & Facilities 15 (01): 27-32. SPE-60841-PA.
Hayashi, Kazuo, and Bezalel C Haimson. 1991. Characteristics of shutin curves in hydraulic
fracturing stress measurements and determination of in situ minimum compressive stress.
Journal of Geophysical Research: Solid Earth 96 (B11): 18311-18321.
Hu, Jinqiu, Faisal Khan, Laibin Zhang, and Siyun Tian. 2020. Data-driven early warning model for
screenout scenarios in shale gas fracturing operation. Computers & Chemical Engineering
Hu, Michael Y, Guoqiang Zhang, Christine X Jiang, and B Eddy Patuwo. 1999. A crossvalidation
analysis of neural network outofsample performance in exchange rate forecasting. Decision
Sciences 30 (1): 197-216.
Kingma, Diederik P, and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv
preprint arXiv:1412.6980.
Li, Quanshu, Huilin Xing, Jianjun Liu, and Xiangchon Liu. 2015. A review on hydraulic fracturing of
unconventional reservoir. Petroleum 1 (1): 8-15.
Manchanda, Ripudaman, Shuang Zheng, Sho Hirose, and Mukul M Sharma. 2020. Integrating
reservoir geomechanics with multiple fracture propagation and proppant placement. SPE
Journal25 (02): 662691. SPE-199366-PA.
Massaras, Leon V, and Dimitri V Massaras. 2012. Real-time Advanced Warning of Screenouts with
the Inverse Slope Method. Paper presented at the SPE International Symposium and
Exhibition on Formation Damage Control, Lafayette, Louisiana, USA, 1517 February. SPE-
Merry, Hoagie, and Panayiotis Dalamarinis. 2020. Multi-Basin Case Study of Real-Time Perforation
Quality Assessment for Screen Out Mitigation and Treatment Design Optimization Using
Tube Wave Measurements. Paper presented at the SPE Annual Technical Conference and
Exhibition, Virtual, 2629 October. SPE-201686-MS.
Nolte, Kenneth G, and Michael B Smith. 1981. Interpretation of fracturing pressures. Journal of
Petroleum Technology 33 (09): 1767-1775. SPE-8297-PA.
Novotny, EJ. 1977. Proppant transport. Paper presented at the SPE Annual Fall Technical Conference
and Exhibition, Denver, Colorado, USA, 719 October. SPE-6813-MS.
Patankar, Neelesh A, DD Joseph, J Wang, RD Barree, M Conway, and M Asadi. 2002. Power law
correlations for sediment transport in pressure driven channel flows. International Journal of
Multiphase Flow 28 (8): 1269-1292.
Pounds, Stan, and Stephan W Morris. 2003. Estimating the occurrence of false positives and false
negatives in microarray studies by approximating and partitioning the empirical distribution
of p-values. Bioinformatics 19 (10): 1236-1242.
Qi, Wu, Xu Yun, Wang Xiaoquan, Wang Tengfei, and Shouliang Zhang. 2012. Volume fracturing
technology of unconventional reservoirs: Connotation, design optimization and
implementation. Petroleum Exploration and Development 39 (3): 377-384.
Roussel, Nicolas Patrick, Ripudaman Manchanda, and Mukul Mani Sharma. 2012. Implications of
fracturing pressure data recorded during a horizontal completion on stage spacing design.
Paper presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands,
Texas, USA, 68 February. SPE-152631-MS.
Sagheer, Alaa, and Mostafa Kotb. 2019. "Time series forecasting of petroleum production using deep
LSTM recurrent networks." Neurocomputing 323: 203-213.
Stein, Roger M. 2002. Benchmarking default prediction models: Pitfalls and remedies in model
validation. Moody’s KMV, New York 20305.
Sun, Jianlei John, Arvind Battula, Brandon Hruby, and Paymon Hossaini. 2020. Application of Both
Physics-Based and Data-Driven Techniques for Real-Time Screen-Out Prediction with High
Frequency Data. Paper presented at the SPE/AAPG/SEG Unconventional Resources
Technology Conference, Virtual, 2022 July. URTEC-2020-3349-MS.
Wang, Jinjiang, Jianxing Yan, Chen Li, Robert X. Gao, and Rui Zhao. 2019. Deep heterogeneous
GRU model for predictive analytics in smart manufacturing: Application to tool wear
prediction. Computers in Industry 111: 1-14.
Warpinski, Norman Raymond, Michael J Mayerhofer, Michael C Vincent, Craig L Cipolla, and EP
Lolon. 2009. Stimulating unconventional reservoirs: maximizing network growth while
optimizing fracture conductivity. Journal of Canadian Petroleum Technology 48 (10): 39-51.
Weng, Xiaowei, Olga Kresse, Charles Edouard Cohen, Ruiting Wu, and Hongren Gu. 2011. Modeling
of hydraulic fracture network propagation in a naturally fractured formation. SPE Production
& Operations 26 (04): 368380. SPE-140253-PA.
Willingham, JD, HC Tan, and LR Norman. 1993. Perforation friction pressure of fracturing fluid
slurries. Paper presented at the Low Permeability Reservoirs Symposium, Denver, Colorado,
USA. 2628 April. SPE-25891-MS.
Yew, Ching H, and Xiaowei Weng. 2014. Mechanics of hydraulic fracturing. Gulf Professional
Yu, Xiaodan, Whitney Trainor-Guitton, and Jennifer Miskimins. 2020. A Data Driven Approach in
Screenout Detection for Horizontal Wells. Paper presented at the SPE Hydraulic Fracturing
Technology Conference and Exhibition, The Woodlands, Texas, USA, 46 February. SPE-
Zeiler, Matthew D. 2012. Adadelta: an adaptive learning rate method. arXiv preprint
ResearchGate has not been able to resolve any citations for this publication.
Accurate and efficient prediction of well production is essential for extending a well’s life cycle and improving reservoir recovery. Traditional models require expensive computational time and various types of formation and fluid data. Besides, frequent manual operations are always ignored because of their cumbersome processing. In this paper, a novel hybrid model is established that considers the advantages of linearity and nonlinearity, as well as the impact of manual operations. This integrates the autoregressive integrated moving average (ARIMA) model and the long short term memory (LSTM) model. The ARIMA model filters linear trends in the production time series data and passes on the residual value to the LSTM model. Given that the manual open-shut operations lead to nonlinear fluctuations, the residual and daily production time series are composed of the LSTM input data. To compare the performance of the hybrid models ARIMA-LSTM and ARIMA-LSTM-DP (Daily Production time series) with the ARIMA, LSTM, and LSTM-DP models, production time series of three actual wells are analyzed. Four indexes, namely, root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and similarity (Sim) values are evaluated to calculate the prediction accuracy. The results of the experiments indicate that the single ARIMA model has a good performance in the steady production decline curves. Conversely, the LSTM model has obvious advantages over the ARIMA model to the fluctuating nonlinear data. And coupling models (ARIMA-LSTM, ARIMA-LSTM-DP) exhibit better results than the individual ARIMA, LSTM, or LSTM-DP models, wherein the ARIMA-LSTM-DP model performs even better when the well production series are affected by frequent manual operations.
In shale gas fracturing operation, proppant screenout is generally recognized as a hazardous operational issue. It affects the performance of hydraulic fracturing horizontal well completion and may lead to downhole accidents. This paper proposes a data-driven early warning method for screenout scenarios based on multi-step forward prediction. Two key contribution of the present work are: development of a prediction model for fracturing pressure by Locally Weighted Linear Regression (LWLR) approach, which parameters are optimised by the integrated PF-ARMA model combining the particle filter (PF) algorithm and the autoregressive moving average (ARMA) model together; proposing a delicate early warning scheme of fracturing screenout event(s) for practical application in the field. The proposed method is tested and fully validated to predict screenout events with satisfying results, which helps to extend the response time for screenout treatment and ensure the long-term safety and integrity of shale gas fracturing operation.
Conference Paper
The scope of this work is to introduce a technology that measures perforation effectiveness on a stage by stage basis before the hydraulic fracturing process begins. The measurement utilizes surface generated tube waves to interrogate the perforated section of the wellbore. We present three case studies demonstrating how "low perforation quality alerts" mitigate operational issues, achieving the deployment of appropriate stimulation treatments for several operators in the Eagle Ford, Haynesville, and Niobrara formations. The reflections of tube waves are analyzed to characterize perforation effectiveness on a stage by stage basis. This methodology is non-intrusive, infinitely repeatable and can be performed in the short time interval between removing the perforating equipment from the well and beginning the fracture treatment process. The operators in our study were alerted to stages with a high likelihood of pumping issues such as high treating pressure, screen outs, or low proppant volume placement. Real-time measurements flagged these stages as having poor wellbore connectivity to the reservoir. Prior to pumping, the operator and crew in the field were alerted to the stages that showed low perforation quality. This allowed modifications to the stage design, such as additional acid, sweeps, finer proppant, gel, increased pad, lower rate, or an additional perforation run. In the cases where changes were made to the design, the objective of mitigating pumping problems due to poor perforation performance was a success. In all the cases where preventive design changes were not implemented, downhole difficulties were experienced resulting in sub-optimal stage execution and/or screen outs. Cost savings in the range of $300,000 on some wells were achieved due to the mitigation of pumping problems associated with poor wellbore to reservoir connectivity. Perforation quality and reservoir rock geomechanics play a dominant role in hydraulic fracturing operational success or failure. Although there have been extensive studies focusing on the role that perforation diameter plays on treatment efficiency, these studies do not adequately consider the importance of the perforation tunnel (depth and quality of perforation penetration into the near well region). Having real-time, non-intrusive, field-based data that provides a direct measurement of this essential element can influence the execution of the hydraulic fracture design. This mitigates the costly exercise of recovering from a screen out and improves the likelihood of a productive stage.
This paper presents the formulation and results from a coupled finite-volume (FV)/finite-area (FA) model for simulating the propagation of multiple hydraulically driven fractures in two and three dimensions at the wellbore and pad scale. The proposed method captures realistic representations of local heterogeneities, layering, fracture turning, poroelasticity, interactions with other fractures, and proppant transport. We account for competitive fluid and proppant distribution between multiple fractures from the wellbore. Details of the model formulation and its efficient numerical implementation are provided, along with numerical studies comparing the model with both analytical solutions and field results. The results demonstrate the effectiveness of the proposed method for the comprehensive modeling of hydraulically driven fractures in three dimensions at a pad scale.
Conference Paper
During hydraulic fracturing jobs, engineers must monitor the wellhead pressure and adjust the pumping schedule in real time to avoid screenout, optimize the proppant and fluid amounts, and minimize cost. In this paper, we use machine learning to predict wellhead pressure in real time during hydraulic fracturing. The new algorithm can assist engineers in monitoring and optimizing the pumping schedule. We explored several neural network models. For each hydraulic fracturing stage, we train a machine learning (ML) model with the data from the first several minutes and predict the wellhead pressure for the next several minutes; we then add the data for the next several minutes, train a second ML model and predict the pressure for the next couple of minutes; and so on. We used several performance metrics to compare different models and select the best model for deployment to the Cloud, where a real-time completions platform is developed and hosted. We selected more than 100 hydraulic fracturing stages from several wells completed in the Delaware Basin and tested several ML methods on the historical data. The wellhead pressure can be predicted with an acceptable accuracy by a slightly nonlinear machine learning model. We tested the ML model on the Cloud, where real-time streaming data such as slurry rate and proppant concentration are gathered. The computation is fast enough that a real-time wellhead pressure can be predicted.
Conference Paper
Hydraulic fracturing is one of the most critical techniques to enhance oil and gas recovery in unconventional reservoirs. Screen-out is considered as one of the most serious problems against a safe, effective and cost-saving hydraulic fracturing treatment in unconventional reservoirs. A simple method for predicting the probability of a screen-out is highly desirable. A semi-analytical model is derived in this paper to describe the configuration of proppant pile within the fracture and to predict the time of screen-out. Case study demonstrates a good consistency between the model calculation result and field observations. By conducting sensitivity analysis with the new model, some major factors that affect proppant screen-out are identified such as fluid viscosity, injection rate, proppant density, proppant size, proppant size distribution and the ratio of proppant volume to fracturing fluid volume. Screen-out can be avoided or at least delayed through optimizing these parameters.