Smart Oilfield Data Mining Final Project-Rod Pump Failure Prediction

PTE588 – Smart Oilfield Data Mining
December 12th
, 2013
Rod Pump Failure Prediction
Team 2
William Chessum, Marco Jarrin, Max Solanki, Xin Jason Du &
Jeffrey Daniels

Rod Pump Failure Prediction Team 2
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Table of Contents
1 Rod Pump Introduction........................................................................................................... 5
2 Exploring the Problem Space.................................................................................................. 5
2.1 Raw Rod Pump Data........................................................................................................ 6
2.2 Processed Rod Pump Data ............................................................................................... 8
3 Exploring the Solution Space................................................................................................ 12
3.1 Preliminary Approach: ................................................................................................... 12
3.2 First Approach:............................................................................................................... 12
3.3 Second Approach: .......................................................................................................... 12
4 Implementation Specification............................................................................................... 13
5 Data Mining Development:................................................................................................... 13
5.1 Preliminary Approach .................................................................................................... 13
5.1.1 Attribute Selection:................................................................................................. 13
5.1.2 Classifiers:............................................................................................................... 14
5.2 First Approach:............................................................................................................... 16
5.2.1 Data Selection:........................................................................................................ 16
5.2.2 Clusterization:......................................................................................................... 16
5.2.3 Classification: ......................................................................................................... 16
5.2.4 Future failure prediction algorithm:........................................................................ 17
5.2.5 Testing: ................................................................................................................... 18
5.3 Second Approach ........................................................................................................... 19
5.3.1 Data Surveying........................................................................................................ 19
5.3.2 Data Preparation...................................................................................................... 20
5.3.3 Clustering................................................................................................................ 22
5.3.4 Data Modeling ........................................................................................................ 23
5.3.5 Testing Results........................................................................................................ 24
6 Conclusions........................................................................................................................... 34
7 Works Cited .......................................................................................................................... 35
Tables
Table 1. Future Failure Prediction from cluster transitions.......................................................... 17
Table 2 - Results for the models using the different performance evaluation methods for the
failure examples............................................................................................................................ 24
Table 3 - Results for the models using the different performance evaluation methods for the
normal examples........................................................................................................................... 25

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Table of Figures
Figure 2 - Problem definition: Data Sets ........................................................................................ 5
Figure 1 - Surface and pump card................................................................................................... 5
Figure 3 – Raw card area; normal and failure data......................................................................... 6
Figure 4 – Raw peak surface load; normal and failure data ........................................................... 7
Figure 5 – Raw minimum surface load; normal and failure data ................................................... 7
Figure 6 – Raw daily run time; normal and failure data................................................................. 8
Figure 7 - Processed card area; normal and failure data................................................................. 9
Figure 8 - Processed peak surface load; normal and failure data ................................................... 9
Figure 9 - Processed minimum surface load; normal and failure data ......................................... 10
Figure 10 - Processed daily run time trend; normal and failure data............................................ 10
Figure 11 - WEKA visualization results....................................................................................... 11
Figure 12 - Problem process flow diagram................................................................................... 13
Figure 13 - PCA Analysis............................................................................................................. 14
Figure 14 - ClassifierSubsetEval (K nearest neighbor) /GeneticSearch....................................... 14
Figure 15 - Classifier nearest Neighbor........................................................................................ 15
Figure 16 - Classifier Decision/Regression Tree.......................................................................... 15
Figure 17 - (a) CardArea, (b) Clusters, (c) New class labeled, (d) Run time for comparisson ... 16
Figure 18 - Classification Results using K-nearest neighbor........................................................ 17
Figure 19 - Classification Results using Decision tree ................................................................. 17
Figure 20 - Future failure prediction............................................................................................. 18
Figure 21 - Card Area of Failure Example, Well 19 plotted showing missing data before
preprocessing ................................................................................................................................ 19
Figure 22 - Card Area of Normal Example, Well 02 plotted showing noise before preprocessing
....................................................................................................................................................... 19
Figure 23 - Sample of data from Failure Example Well11, showing daily runtime attribute as 0
but the other attributes having non-zero values ............................................................................ 20
Figure 24 - Snippet of MatLab preprocessing code showing how missing data was filled for Well
01................................................................................................................................................... 20
Figure 25 - Snippet of MatLab preprocessing code showing the spans used for the various wells
....................................................................................................................................................... 21
Figure 26 - Snippet of MatLab code used showing the implementation of the mefilt1 function. 21
Figure 27 - Plot of smoothed CardArea attribute for failure example Well 19............................ 21
Figure 28 - Plot of smoothed CardArea attribute for Well 02...................................................... 22
Figure 29 - Snippet of MatLab preprocessing code for normalizing the various well datasets.... 22
Figure 30 - Visualization of various clusters for Card Area plotted against History Date for Well
19................................................................................................................................................... 23
Figure 31 - Results for 10-Fold Cross Validation using Logistic Regression for failure examples
dataset ........................................................................................................................................... 25
Figure 32 - Results for Percentage Split using Logistic Regression for failure examples dataset 26
Figure 33 - Results for learning class of training set instances for Logistic Regression for failure
examples dataset ........................................................................................................................... 26
Figure 34 - Results for predicting class of test set instances using Logistic Regression for unseen
failure examples dataset................................................................................................................ 27

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Figure 35 - Results for 10-Fold Cross Validation using Logistic Regression for normal examples
dataset ........................................................................................................................................... 27
Figure 36 - Results for Percentage Split using Logistic Regression for normal examples dataset
....................................................................................................................................................... 28
Figure 37 - Results for learning class of training set instances using Logistic Regression for
normal examples........................................................................................................................... 28
Figure 38 - Results for predicting class of test set instances using Logistic Regression for unseen
normal examples........................................................................................................................... 29
Figure 39 - Results for 10-Fold Cross Validation using Neural Network for failure examples
dataset ........................................................................................................................................... 30
Figure 40 - Results for Percentage Split using Neural Network for failure examples dataset ..... 30
Figure 41 - Results for learning class of training set instances using Neural Network for failure
examples dataset ........................................................................................................................... 31
Figure 42 - Results for predicting class of test set instances using Neural Network for unseen
failure examples dataset................................................................................................................ 31
Figure 43 - Results for 10-Fold Cross Validation using Neural Network for normal examples
dataset ........................................................................................................................................... 32
Figure 44 - Results for Percentage Split using Neural Network for normal examples dataset .... 32
Figure 45 - Results for learning training set instances using Neural Network for normal examples
dataset ........................................................................................................................................... 33
Figure 46 - Results for predicting class of test set instances using Neural Network for unseen
normal examples dataset............................................................................................................... 33

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1 Rod Pump Introduction
Rod pumps are currently the most widely used artificial lift method within the oil and gas
industry. They are employed to bring fluid to the surface when the reservoir pressure is
insufficient. Rod pump failures which include surface, tubing, and down-hole failures,
commonly occur and will result in production
loss and operational expenses. If you can detect
the precursors of a well failure or the well failure
itself you can minimize downtime and increase
operational efficiency (Liu & Patel, March 26-28,
2013).
There are many different operating variables you can
examine to determine a pumps operating status but
the most telling is the dynamometer card [Figure 1].
This will indicate the maximum and minimum
forces being applied to the rod pump and also the
area inside the pump cycle. For the purpose of this
report we will be using the pump card area to
determine rod well failures.
2 Exploring the Problem Space
The objective of this project is to develop a data mining technique to accurately predict rod pump
failures based on a set of data from an unknown oil field. Rod pump data from normal and
failure wells was given with both raw and processed data. Additionally, each well had four
numeric attributes; card area, peak surface load, minimum surface load and daily run time.
Figure 2 - Problem definition: Data Sets
Figure 1 - Surface and pump card
Card Area

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2.1 Raw Rod Pump Data
The below four figures (Figure 3, Figure 4, Figure 5 & Figure 6) depict the raw data from 20
different wells (10 failure and 10 normal) over the course of roughly one year. Four attributes
were given for each well; card area, peak surface load, minimum surface load and daily run time.
The data is quite noisy with multiple outliers and cannot be used for analysis purposes.
Processing and normalizing is required before data mining techniques can be applied.
Figure 3 – Raw card area; normal and failure data
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CardArea Failure
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Figure 4 – Raw peak surface load; normal and failure data
Figure 5 – Raw minimum surface load; normal and failure data
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MinimumSurface Load Failure
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Figure 6 – Raw daily run time; normal and failure data
2.2 Processed Rod Pump Data
The below four figures (Figure 7, Figure 8, Figure 9 & Figure 10) depict the processed data from
19 different wells (9 failure and 10 normal) over the course of roughly one year. Four attributes
were given for each well; card area, peak surface load, minimum surface load and daily run time.
After processing and normalization, the data shows distinct clustering and an obvious contrast
can be seen between normal and failure wells. This data will be used in MatLab and WEKA
using various data mining techniques to predict rod pump failures.
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Daily Run Time Failure
Normal

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Figure 7 - Processed card area; normal and failure data
Figure 8 - Processed peak surface load; normal and failure data
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PeakSurface Load Failure
Normal

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Figure 9 - Processed minimum surface load; normal and failure data
Figure 10 - Processed daily run time trend; normal and failure data
Figure 11 shows the results from WEKA using the Visualization tool. From this analysis you can
see that the card area is the most important attribute in identifying potential rod pump failures.
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MinimumSurface Load Failure
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Daily Run Time Failure
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On the other hand, daily run time is the least significant attribute due to the inability to determine
why the well is down i.e. regular well maintenance or true failure.
Figure 11 - WEKA visualization results

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3 Exploring the Solution Space
3.1 Preliminary Approach:
• Analyze processed data globally
• Class: Run-time
• Attribute selection:
– Visual, PCA analysis, ClassifierSubsetEval/GeneticSearch
• Classifier:
– K-nearest neighbors, Decision Trees
• Need to define new class from data-set to interpret failures (adding class labels)
3.2 First Approach:
• Heuristic: Card Area trend seems to predict failures
• Only Card Area Clustering: K-means, 5 clusters.
• Define Class labels from cluster numbers
• Training and Test data Sets: holdout split 2/3 & 1/3
• Classifier: K-nearest neighbor, Naïve Bayes, decision/regression tree (predicts current well
status)
• Challenge: Learning from cluster transitions to predict future failures
3.3 Second Approach:
• Raw data for failure and normal wells was processed for: missing data, outliers, then
normalized.
• Added a class label based on clusterization using all the attributes (except well name and
history) by Expectation Maximization Method.
• Model learning using different algorithms, with 10-fold cross validation: Decision Tree,
Neural Network, Logistic Regression, AdaBoost (Decision Stump classifier), Naïve Bayes

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4 Implementation Specification
Figure 12 - Problem process flow diagram
5 Data Mining Development:
5.1 Preliminary Approach
We analyzed the data from normal wells and from failure wells. At this point our objective was
to identify a model that indicates a well failure, which means DailyRunTime = 0. Since we
require recognizing a perturbation from a failure-type well, we decided to use only a unified
dataset from the failure wells. Data Preparation was not required since processed data was
already provided.
5.1.1 Attribute Selection:
PCA analysis, ClassifierSubsetEval/GeneticSearch, were evaluated against the DailyRunTime
Class to determine if a dimension reduction makes sense.
Figure 13 and Figure 14 show the results from Weka.

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Figure 13 - PCA Analysis
Figure 14 - ClassifierSubsetEval (K nearest neighbor) /GeneticSearch
5.1.2 Classifiers:
K-nearest neighbors and Decision/Regression Tree were used for evaluation, as shown in Figure
15 and Figure 16

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Figure 15 - Classifier nearest Neighbor
Figure 16 - Classifier Decision/Regression Tree
From the analysis, we determined we will need to add a class label to the data set.

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5.2 First Approach:
By a heuristic process, using graphical inspection, we determined that card area declining trend
could be used to predict the well failures.
5.2.1 Data Selection:
We selected a training and test data sets using a holdout split 2/3 & 1/3 from the failure wells.
5.2.2 Clusterization:
We attacked this problem using clusterization (K-means) for labeling 5 clusters as follow:
1 = shutdown well, 2 = Prefailure, 4 = Normal, 3 and 5 = quasi normal states.
Figure 17.b shows the colored data of CardArea, once 5 clusters were obtained.
Figure 17.c, the new labeled class, can be related to the run time to predict a failure.
The transition between steps could be used to anticipate a shutdown, once the information is
validated with the run time.
Figure 17 - (a) CardArea, (b) Clusters, (c) New class labeled, (d) Run time for comparisson
5.2.3 Classification:
Once the new label class was added to the data set, we used 3 types of classifiers to predict the
current well status: K-nearest neighbor, Naïve Bayes, and decision/regression tree (with a 10-
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fold cross validation). Figure 18 and Figure 19 show the comparison between K-nearest neighbor
and decision tree results.
K-nearest neighbor
Figure 18 - Classification Results using K-nearest
neighbor
Decision/regression tree
Figure 19 - Classification Results using Decision tree
5.2.4 Future failure prediction algorithm:
We determined that by using a simple logic between transitions we would be able to predict
future failures. We first detected the flank changes in the label class. Then we identified each
negative transition and generated 3 annunciator labels: Warning, Failure Probable and Failure
Imminent, as follow:
Table 1 - Future Failure Prediction from cluster transitions
State 1 State 2 Operator Label Priority (0-100)
From 5 or 4 to 3 Warning 10
From 5 or 4 to 2 Failure probable 50
From Warning to 2 Failure Imminent 100

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5.2.5 Testing:
Figure 20 shows the results for well 16. After the model and prediction algorithm have been
applied, this algorithm was able to predict a failure 13 days in advance.
Figure 20 - Future failure prediction
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Failure Prediction
Well-16
Declining trend
learned
Prediction of
failure alarm
(13 days before
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Warnings (not
imminent failures)
Real Failure

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5.3 Second Approach
5.3.1 Data Surveying
When the unprocessed data from the normal and failure examples are visualized in MatLab, it is
seen that there are some missing values from the data set as seen in Figure 21
Figure 21 - Card Area of Failure Example, Well 19 plotted showing missing data before preprocessing
Moreover, the curves have some noise as seen in Figure 22
Figure 22 - Card Area of Normal Example, Well 02 plotted showing noise before preprocessing
Lastly, when the csv file is checked some instances have their Daily Run Time attribute as a zero
value, while the other attributes in those instances have non-zero values as seen in the second
row of the Figure 23

Figure 23 - Sample of data from Failure Example Well11, showing daily runtime attribute as 0 but the other
attributes having non-zero values
5.3.2 Data Preparation
The datasets were preprocessed individual
the unprocessed data ready for modeling, first the missin
done because since there is no data for those instances we can assume the rod pumps have failed,
so representing those missing instance attributes with a zero value is justifiable.
Figure 24 - Snippet of MatLab preprocessing code showing how missing data was filled for Well 01
After the missing data have been filled, the curves are then smoothed using a moving median
with varying spans to remove noise. This helps to minimize overfitting
to be used. The normal examples are smoothed over varying spans of specifi
seen in Figure 25. The varying spans for the moving median was to avoid distortion of the
individual datasets. The failure examples however were all smoothed using a moving median
span of 11.
Sample of data from Failure Example Well11, showing daily runtime attribute as 0 but the other
The datasets were preprocessed individually using MatLab as shown in Figure
the unprocessed data ready for modeling, first the missing values are filled in with 0. This is
Snippet of MatLab preprocessing code showing how missing data was filled for Well 01
with varying spans to remove noise. This helps to minimize overfitting of the data mining model
to be used. The normal examples are smoothed over varying spans of specifi
. The varying spans for the moving median was to avoid distortion of the
Team 2
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Sample of data from Failure Example Well11, showing daily runtime attribute as 0 but the other
Figure 24. In order to have
g values are filled in with 0. This is
Snippet of MatLab preprocessing code showing how missing data was filled for Well 01
of the data mining model
to be used. The normal examples are smoothed over varying spans of specifically 1, 3 and 9 as
. The varying spans for the moving median was to avoid distortion of the

Figure 25 - Snippet of MatLab preprocessing code showing the spans used for the various wells
After the spans have been chosen, the medfilt1 MatLab function is then applied to all the
numeric attributes of the dataset as shown in
the moving median span is because the medfilt1 MatLab function works better with odd values.
Figure 26 - Snippet of MatLab code used showing the implementation of the mefilt1 function
After smoothing the values, it is seen that the attributes are less noisy as
Figure 28 compared to Figure
Figure 27 - Plot of smoothed CardArea attribute for failure example Well 19
Snippet of MatLab preprocessing code showing the spans used for the various wells
of the dataset as shown in Figure 26. The reason for choosing odd values as
Snippet of MatLab code used showing the implementation of the mefilt1 function
After smoothing the values, it is seen that the attributes are less noisy as shown in
Figure 21 and Figure 22.
Plot of smoothed CardArea attribute for failure example Well 19
Team 2
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Snippet of MatLab preprocessing code showing the spans used for the various wells
. The reason for choosing odd values as
Snippet of MatLab code used showing the implementation of the mefilt1 function
shown in Figure 27 and

Figure 28 - Plot of smoothed CardArea attribute for Well 02
After smoothing all the individual datasets, they are normalized in MatLab in order to make the
attributes comparable to one another due to the
using the Min-Max normalization method, which is plausible because most of the attributes for
the datasets are not skewed either to the left or the right.
Figure 29 - Snippet of MatLab preprocessing code for normalizing the various well datasets
5.3.3 Clustering
With the datasets for each well having better quality, they are then clustered individually using
Weka. The cluster method used here is the Expectation Maximization method, which i
probabilistic cluster method. Rather than using an attribute selection method for clustering, all
Plot of smoothed CardArea attribute for Well 02
attributes comparable to one another due to the varying ranges as seen in Figure
Max normalization method, which is plausible because most of the attributes for
the datasets are not skewed either to the left or the right.
b preprocessing code for normalizing the various well datasets
Weka. The cluster method used here is the Expectation Maximization method, which i
Team 2
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Figure 29. This is done
Max normalization method, which is plausible because most of the attributes for
b preprocessing code for normalizing the various well datasets
Weka. The cluster method used here is the Expectation Maximization method, which is a

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the numeric attributes namely CardArea, PeakSurfLoad, MinSurfLoad and DailyRunTime are
used by the EM Cluster algorithm to cluster the instances.
The number of clusters is set to 3 so that we will know Normal, Pre-failure and Failure as shown
in Figure 30. These 3 clusters then become the class attribute used for failure prediction. Pre-
failure serves as the warning that failure of the rod pump is imminent so that technicians can
service the rod pumps and this increases productivity and saves operational costs.
Figure 30 - Visualization of various clusters for Card Area plotted against History Date for Well 19
5.3.4 Data Modeling
After all the dataset instances have been assigned a class label, suitable data mining models are
selected for failure prediction. Five predictive models were compared namely Naïve Bayes,
AdaBoost (Decision Stump classifier), Multilayer Perceptron (Neural Network), Decision Tree
and Logistic Regression.
In order to evaluate the performance of these models, three separate methods of testing were
developed namely 10-Fold Cross Validation, a percentage split and then a blind test.

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In the 10-Fold Cross validation, all the well datasets for the failure examples, Wells 11-20 were
merged into one dataset and input into Weka for analysis. The datasets for the normal examples,
Wells 1-10 were also merged into a different single dataset, for analysis in Weka.
Secondly, In the Percentage Split, all the well datasets for the failure examples, Wells 11-20
were merged into one dataset and input into Weka for analysis. The datasets for the normal
examples, Wells 1-10 were also merged into a different single dataset. However, we decided to
use an extreme case test of using 5% of the dataset for training the models and then using the rest
of the 95% for testing. This would was done in order to give us a feel of how well the datasets
were preprocessed and clustered.
Finally in the blind test, for the failure examples, Wells 11-16 were merged into one dataset and
used to train the models while Wells 17-20 were merged into a single dataset supplied to the
model as a test set. For the normal examples, Wells 1-6 were merged into a single dataset and
used to train the models while Wells 7-10 were merged into a single dataset and supplied to the
models as a test set.
5.3.5 Testing Results
After testing we see that the models most of them have very good predictive performances above
95%. However, we see that in the blind test for the failure examples, the Naïve Bayes model had
a training accuracy of 99.05% and a testing accuracy of 76.21%, which suggests the model, is
severely overfitted. Likewise in testing the normal examples, with the Decision Tree model, it is
seen that it has a training accuracy of 98.63% and a testing accuracy of 63.31%, which suggests
it, is severely overfitted.
Considering the performance of the models for both the normal wells and the failure wells, the
Neural Network and the Logistic Regression are the preferred models for failure prediction.
Table 2 - Results for the models using the different performance evaluation methods for the failure examples
AdaBoost NaiveBayes Neural
Network
Decision
Tree
Logistic
Regression
10-Fold
Cross
Validation
99.31% 98.39% 99.86% 99.77% 99.82%
Percentage
Split
99.32% 99.13% 99.32% 97.19% 99.32%
Blind Test 99.12%
99.13%
99.05%
76.21%
100%
99.88%
99.9%
92.81%
100%
92.69%

Table 3 - Results for the models using the different performance evaluation methods for the normal examples
AdaBoost
10-Fold Cross
Validation
88.44%
Percentage
Split
87.69%
Blind Test 91.16%
81.97%
The Logistic Regression Models and the Neural Network models are our preferred models for
failure prediction so we will now take an in
Weka analysis. While these are our two preferred models, a look
different tests suggests that the Neural Network is the most superior model out of all the models
tried.
5.3.5.1 Logistic Regression
The confusion matrices show that it was able to predict all the classes quite accurately
Failure Examples Dataset
10-Fold Cross Validation
Figure 31 - Results for 10-Fold Cross Validation using Logistic Regression for failure examples dataset
Results for the models using the different performance evaluation methods for the normal examples
NaiveBayes Neural
Network
Decision
Tree
84.91% 90.54% 96.11%
84.2% 89.08% 87.46%
86.65%
81.97%
91.61%
86.02%
98.63%
63.31%
failure prediction so we will now take an in-depth look into the results of these models from our
these are our two preferred models, a look at the confusion matrices of the
Logistic Regression
Fold Cross Validation using Logistic Regression for failure examples dataset
Team 2
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Results for the models using the different performance evaluation methods for the normal examples
Decision Logistic
Regression
92.91%
89.87%
94.21%
89.03%
depth look into the results of these models from our
at the confusion matrices of the
Fold Cross Validation using Logistic Regression for failure examples dataset

Percentage Split
Figure 32 - Results for Percentage Split using Logistic Regression for failure examples dataset
Blind Test
Training Set
Figure 33 - Results for learning class of training set instances for Logistic Regression for failure examples
dataset
Results for Percentage Split using Logistic Regression for failure examples dataset
Results for learning class of training set instances for Logistic Regression for failure examples
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Results for Percentage Split using Logistic Regression for failure examples dataset
Results for learning class of training set instances for Logistic Regression for failure examples

Test Set
Figure 34 - Results for predicting class of test set instances using Logistic Regression for unseen failure
examples dataset
Normal Examples Dataset
Figure 35 - Results for 10-Fold Cross Validation using Logistic Regression for normal examples dataset
Results for predicting class of test set instances using Logistic Regression for unseen failure
Fold Cross Validation using Logistic Regression for normal examples dataset
Team 2
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Results for predicting class of test set instances using Logistic Regression for unseen failure
Fold Cross Validation using Logistic Regression for normal examples dataset

Percentage Split
Figure 36 - Results for Percentage Split using Logistic Regression for normal examples dataset
Blind Test
Training Set
Figure 37 - Results for learning class of training set instances using Logistic Regression for normal examples
Results for Percentage Split using Logistic Regression for normal examples dataset
Results for learning class of training set instances using Logistic Regression for normal examples
Team 2
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Results for Percentage Split using Logistic Regression for normal examples dataset
Results for learning class of training set instances using Logistic Regression for normal examples

Test Set
Figure 38 - Results for predicting class of test set instances using Logistic Regression for unseen normal
examples
5.3.5.2 Neural Network (Multilayer Perceptron)
From the confusion matrices for the failure examples, the model performs well predicting each
class accurately however, when we look at the normal examples, it can be seen that it performs
badly predicting the failure class. However, going back to visualizing all the normal examples
dataset, it can be seen that there are hardly
zero for an extended amount of time as seen with the failure examples datasets. Therefore
we labeled those instances as failures from our clustering
the failure class rightfully as pre
prediction
Failure Examples Dataset
Results for predicting class of test set instances using Logistic Regression for unseen normal
(Multilayer Perceptron)
the confusion matrices for the failure examples, the model performs well predicting each
ly however, when we look at the normal examples, it can be seen that it performs
seen that there are hardly any failures since the attributes
zero for an extended amount of time as seen with the failure examples datasets. Therefore
those instances as failures from our clustering, the Neural Network actually predicts
the failure class rightfully as pre-failures. For this reason alone, it is the best model for failure
Team 2
29 | P a g e
Results for predicting class of test set instances using Logistic Regression for unseen normal
the confusion matrices for the failure examples, the model performs well predicting each
ly however, when we look at the normal examples, it can be seen that it performs
since the attributes are almost never at
zero for an extended amount of time as seen with the failure examples datasets. Therefore while
, the Neural Network actually predicts
For this reason alone, it is the best model for failure

Figure 39 - Results for 10-Fold Cross Validation using Neural Network for failure examples dataset
Percentage Split
Figure 40 - Results for Percentage Split using Neural Network for failure examples dataset
Blind Test
Training Set
Fold Cross Validation using Neural Network for failure examples dataset
Results for Percentage Split using Neural Network for failure examples dataset
Team 2
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Fold Cross Validation using Neural Network for failure examples dataset
Results for Percentage Split using Neural Network for failure examples dataset

Figure 41 - Results for learning class of training set instances using Neural Network for failure examples
dataset
Test Set
Figure 42 - Results for predicting class of test set instances using Neural Network for unseen failure examples
dataset
Results for learning class of training set instances using Neural Network for failure examples
Results for predicting class of test set instances using Neural Network for unseen failure examples
Team 2
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Results for learning class of training set instances using Neural Network for failure examples
Results for predicting class of test set instances using Neural Network for unseen failure examples

Normal Examples Dataset
Figure 43 - Results for 10-Fold Cross Validation using Neural Network for normal examples dataset
Percentage Split
Figure 44 - Results for Percentage Split using Neural Network for normal examples dataset
Blind Test
Training Set
Fold Cross Validation using Neural Network for normal examples dataset
s for Percentage Split using Neural Network for normal examples dataset
Team 2
32 | P a g e
Fold Cross Validation using Neural Network for normal examples dataset
s for Percentage Split using Neural Network for normal examples dataset

Figure 45 - Results for learning training set instances using Neural Network for normal examples dataset
Test Set
Figure 46 - Results for predicting class of test set instances using Neural Network for unseen normal
examples dataset
Results for learning training set instances using Neural Network for normal examples dataset
Results for predicting class of test set instances using Neural Network for unseen normal
Team 2
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Results for learning training set instances using Neural Network for normal examples dataset
Results for predicting class of test set instances using Neural Network for unseen normal

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6 Conclusions
Wells should be pre-processed on an individual basis to ensure a consistent dataset. We
attempted to analyze the data on an individual and global basis and without a doubt, individual
analysis resulted in better prediction accuracy.
We evaluated multiple data mining algorithms and in the end Neural Network and Logistic
Regression were the two preferred algorithms for failure prediction. However a look at the
detailed results of the two models shows that the Neural Network is the best model for failure
prediction.
Machine learning with expert domain knowledge is critical for training the model. A lot of
assumptions were made regarding the data, especially when a failure was observed. Having the
knowledge expert on hand during training would result in the highest possible model accuracy.
For true validation of the model, additional testing is required with the corroboration of the
knowledge expert. This would ensure that all failures observed are actually well failures. Also,
additional well data from another field would be a great test set to validate the model.

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7 Works Cited
Liu, F., & Patel, A. (March 26-28, 2013). Well Failure Detection for Rod Pump Artificial Lift
System through Pattern Recognition. Beijing, China: International Petroleum Technology
Conference.
Liu, Y. (December 2013). Failure Prediction for Rod Pump Artifiicial Lift Systems.
S. Liu, Y. L. Automatic Early Fault Detection for Rod Pump Systems. Society of Petroleum
Engineers (SPE).

Smart Oilfield Data Mining Final Project-Rod Pump Failure Prediction

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