Title Slides
02
Topic Characteristics
04
Problem Statement
05
Research Objectives
06
Literature Review
07
Methodology
08
Timeline
11
Impact
12
References
13
Thesis Presentation
Outline
Topic Characteristics
01
Conducting a
research study for
recognize operation
conditions of sucker
rod pump
Using Semisupervised learning
02
Novelty
approach for
automatic well
failure analysis
for the sucker
rod pumping
systems
03
This study based on
monthly historical real
data for different
fields
Problem Statement
Monitoring the working conditions of the sucker
rod pumping system is important to sustain
acceptable productivity levels in oil and gas
industry.
In the case of the analysis of working conditions of
hundreds or thousands of wells in the same field, it
is humanly unfeasible and ineffective to perform
visual classification, in addition to requiring large
experience.
Also with high frequency and streaming of sensors
data (data change every 5 minutes) it will generate
big number of unlabeled dyno card shapes per day.
Labeling those shapes will consume time and cost
in case of anomaly detection the rod pump.
Therefore, it is essential to develop algorithm to
recognize huge number of unlabeled shapes to
identify events of interest, and consequently,
improve asset management.
Sucker rod pump Competent
The main components of sucker-rod pumping wells are the prime
mover, the pumping unit, the rod string and the downhole pump.
The pumping unit transforms the rotary motion of the prime mover
into the reciprocating motion necessary to operate the downhole
pump .
The rod string connects the downhole pump with the pumping unit.
The downhole pump works on the positive displacement principle
and consists of a working barrel (cylinder) and plunger (piston).
The plunger contains the discharge valve (traveling
valve) and the working barrel contains the suction valve (standing
valve).
The two valves operate on the ball-and-seat principle and work like
check valves
Sucker rod pump
How it work
Problem Statement
Dynamometer cards
Dynamometer cards are one of
the main tools for rod-pumping
well performance analysis as
mentioned by Gibbs and
Neely(1966) [1]
By plotting the load (force)
on the y-axis and position
(usually measured in stroke
length or percent of the
pump cycle) on the x-axis.
Normal Condition
Gas Interference
Ideal downhole dynometer card
Fluid Pound
Objective #1
Design a robust framework that incorporates both labeled and
unlabeled data for rod pump failure analysis
Semi-Supervised Learning
Implement advanced anomaly detection algorithms that can identify
Framework
subtle deviations from normal rod pump operation using the labeled
Objective #2
and unlabeled data. This will contribute to the early detection of
potential failures.
Objective #3
Employ semi-supervised classification techniques to
categorize different types of failures once they are
detected. The classification model should be able to
differentiate between various failure modes, such as
mechanical, fluid-related, and operational failures
Objective #4
Quantitatively assess the performance of the proposed
semi-supervised learning approach by comparing it with
traditional supervised methods such as CNN, VGG16 and
Alexnet . Metrics such as accuracy, precision, recall, and
F1-score will be used for evaluation
Research Objectives
Literature review
A study by Ramez Abdalla and Ahmed El-Banbi (2019)
[2] used downhole dynamometer cards as input to
ANN with Genetic algorithms to identify the sucker rod
pumping system conditions .
01
It used historical data 4,467 dynamometer cards as
numerical values for oil wells from an offshore oil field
in Egyptian western desert and china.
It depend on SME which label dynamometer card
samples to categorize the pump failure with for normal
operating conditions and 12 common rod pump failures.
A study by Sayed Ali Sharaf and Patrick Bangert at (2019)
[3] proposed approach Beam Pump Dynamometer Card
Classification Using Gradient Boosting Machines (GBM)
Classifier .
02
Another study by Haibo Chenget at. (2020) [4] used
a AlexNet-SVM algorithm Automatic Recognition of
Sucker-Rod Pumping System Working Conditions.
03
The study using raw data which is collected from a
real oilfield in northern China from sensors of load
and displacement to plot dyno card shapes and
generate images which transferred to AlexNetbased transfer learning technique to classify 8
common rod pump failures .
04
The study used a dataset of wells from Bahrain fields
with 5,380,163 different cards from 297 beam pumps and
35,292 cards are manually labelled by experts into twelve
(12)classes.
A study by João Nascimento et at (2021) [5]
proposed a approach for Diagnostic of Operation
Conditions of Sucker-Rod Pumping Wells.
The study used 50,000 dynamometer cards from
38 wells in the Mossoró, RN, Brazil.
Features are numerical representations derived
from the available data and are linked to the model.
It use three algorithms (decision tree, random
forest and XGBoost), three descriptors (Fourier,
wavelet and card load values
Literature review
05
A different study by Yi Peng(2019) [6] build a general
model that generates the dynamometer card from
electrical power parameter using state-of-art deep
learning algorithms
It collect more than 200,000 labeled images from petro
china.
It depend on SME which label dynamometer card
samples to categorize the pump failure with for normal
operating conditions and 12 common rod pump failures.
06
,
07
Another study by Jeremy Liu at. (2015) [8] used
SVM and auto-encoder to Classify and Predicted
Well Failures
Feature sets are typically selected by subjectmatter experts through experience.
08
A novel study by Lu Chen at (2020) [7] using the motor
power and XGBoost to diagnose working states of a
sucker rod pump
In this study, the motor power curves of seven working
states are obtained by transforming the dynamometer
cards into motor power curves
The study used a dataset of wells from Bahrain fields
with 5,380,163 different cards from 297 beam pumps and
35,292 cards are manually labelled by experts into twelve
(12)classes.
A Conference Paper · January 2009 et shows that
by using Artificial Neural Network (ANN) system
one can establishes pattern classes and sets
standards for training and validation in
Dynamometer Card in oil well rod pump systems .
Literature review
09
In this paper [12] Semisupervised generative
adversarial network (GAN)
approaches are used to learn
from limited labeled data
alongside larger unlabeled
datasets in Medical Images.
10
Also GAN are used in semisupervised learning on graphs
[13] .
This paper investigates the
potential of generative adversarial
nets (GANs) for semi-supervised
learning over graphs and present
a novel method GraphSGAN.
In the domain of Internet of thing (IoT) especially in smart
11
homes Semi-Supervised Learning with GANs are used for
Device-Free Fingerprinting Indoor Localization [14].
This paper proposes a method called semi-supervised deep
convolutional generative adversarial network (DCGAN)
model for device-free fingerprinting indoor localization.
The performance of semi-supervised DCGAN is compared
with supervised CNN
When trained with sufficient labeled data (e.g., 3200 or 6400
labeled real CSI samples), both DCGAN and CNN achieve
comparable performance, around 87% accuracy. When
trained with reduced amount of labeled data, CNN attains
suffered performance while DCGAN retains the performance
12
Another Paper [15] use Triple Generative
Adversarial Networks for enhance the
performance of classification of well-known image
net dataset (MNIST, SVHN, CIFAR10 and Tiny
Image net data) in the semi-supervised learning
problem.
The results show that performance of this method
is outperform the performance of 13-layer CNN
classifier.
Methodology Work Flow for pattern recognition of
pumping system
Raw Data
acquisition
(SCADA
System)
Data
Preprocessing
Dyno Card
image
Generation
Image
Normalization
Training set
Labeled &
unlabeled
Testing set
Labeled &
unlabeled
General
Adversarial
Network
Model
Pump
operation
condition
Semi-supervised general adversarial networks
Study Timeline
Lorem
Ipsum
2017Lorem
Ipsum
Literature
2021Lorem
Ipsum
2019 Data
Review
1- 2
months
Comparative
analysis
1 month
Preparation
and
analysis
1 week
Lorem
Ipsum
Data
collection
1 week
2018
Lorem
Ipsum
Experimental
design &
implementation
20203-6 months
Lorem
Ipsum
Results and
Conclusion
1 week
Expected Contributions
01
The proposed approach will provide a novel
method for Rod Pump failure analysis
Enhanced Failure Analysis: The proposed
02
approach has the potential to improve the
accuracy of rod pump failure detection and
classification, thereby reducing downtime
and maintenance costs.
Reduced Data Labeling Costs and time: By
leveraging unlabeled data, the reliance on
03
labeled failure instances can be reduced, which
is particularly valuable considering the scarcity
and cost of such data and reduce the time
consuming for this failure analysis
References
Beam Pump Dynamometer Card Classification
Using Machine Learning
1
3
Identification of Downhole Conditions in
Sucker Rod Pumped Wells Using Deep Neural
Networks and Genetic Algorithms
Ramez Abdalla , Ahmed El-Banbi
https://doi.org/10.2118/200494-PA
Automatic Recognition of Sucker-Rod Pumping
System Working Conditions Using Dynamometer
Cards with Transfer Learning and SVM
2
Sayed Ali Sharaf, Tatweer Petroleum; Patrick Bangert,
Algorithmica Technologies; Mohamed Fardan, Khalil
Alqassab, Mohamed Abubakr, and Mahmood Ahmed,
Tatweer Petroleum
https://doi.org/10.2118/194949-MS
4
Diagnostic of Operation Conditions and Sensor
Faults Using Machine Learning in Sucker-Rod
Pumping Wells
João Nascimento , André Maitelli , Carla Maitelli and
Anderson Cavalcanti
Haibo Cheng, Haibin Yu, Peng Zeng , Evgeny Osipov ,
Shichao Li and Valeriy Vyatkin
https://doi.org/10.3390/s20195659
https://doi.org/10.3390/s21134546
References
5
Artificial Intelligence Applied in Sucker Rod
Pumping Wells: Intelligent Dynamometer Card
Generation, Diagnosis, and Failure Detection
Using Deep Neural Networks
6
Using the motor power and XGBoost to diagnose
working states of a sucker rod pump
Lu Chen , Xianwen Gao , Xiangyu Li
https://doi.org/10.1016/j.petrol.2020.108329
Yi Peng, PetroChina Riped
https://doi.org/10.2118/196159-MS
7
Autoencoder-Derived Features as Inputs to
Classification Algorithms for Predicting Well
Failures
Jeremy Liu; Ayush Jaiswal; Ke-Thia Yao; Cauligi S.
Raghavendra
https://doi.org/10.2118/174015-MS
8
Pattern Recognition for Downhole Dynamometer
Card in Oil Rod Pump System using Artificial
Neural Networks.
A. M. Felippe de Souza,Marco A. D. Bezerra, M. de A.
Barreto Filho, Leizer Schnitman
https://www.researchgate.net/publication/220710225_Patte
rn_Recognition_for_Downhole_Dynamometer_Card_in_Oil
_Rod_Pump_System_using_Artificial_Neural_Networks
References
9
Semi-Supervised Deep Learning for
Abnormality Classification in Retinal Images
11
Kevin M. Chen and Ronald Y. Chang
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo,
Balagopal Unnikrishnan
https://arxiv.org/pdf/1812.07832v1.pdf
10
Semi-supervised Learning on Graphs with
Generative Adversarial Nets
Ming Ding , Jie Tang , Jie Zhang
https://doi.org/10.1145/3269206.3271768
Semi-Supervised Learning with GANs for DeviceFree Fingerprinting Indoor Localization
https://arxiv.org/pdf/2008.07111v1.pdf
12
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jiashuo Liu, Jun Zhu, Member,
IEEE, and Bo Zhang
https://arxiv.org/pdf/1912.09784v2.pdf
QUESTIONS
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