AI-Driven Maintenance Optimisation for Natural Gas Liquid Pumps in the Oil and Gas Industry: A Digital Tool Approach
Abstract
1. Introduction
2. Method
2.1. Dynamic Optimisation of Maintenance Intervals
2.2. Risk-Based Prioritisation Using FMEA
2.3. Integrated Maintenance Optimisation Logic
2.4. Handling Unplanned Failures
2.5. Tool Development and Validation
3. Digital Tool Development
3.1. Functional Design of the Digital Tool
3.2. Data Structure: Inputs and Outputs
3.3. AI Algorithm Determination for Processing Inputs
3.3.1. Input Data Sources and Limitations
3.3.2. Algorithm Allocation by Function
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- MTBFs Estimation
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- Bearing RUL Estimation
- -
- Component Lifespan Calculation
- -
- Component Condition Assessment
- -
- Diagnostic Functions and Maintenance Recommendations
- -
- Maintenance Schedule Optimisation
3.4. Development Platform and Validation
4. Case Study Results and Discussion
4.1. MTBFs of Components
4.2. Optimised Component Replacement Frequencies
4.3. Component Condition
4.3.1. Bearings
4.3.2. Mechanical Seals
4.3.3. Coupling
4.4. Maintenance Optimisation
4.5. Diagnosis and Recommended Actions
4.6. Comparison with Existing Studies
5. Validation Questionnaire Results and Discussion
5.1. The Need for an AI Tool to Optimise NGL Pump Maintenance
- -
- Question 1: There is a business need to deploy AI for NGL pump maintenance optimisation instead of manual optimisation.
5.2. Assumption Technical Validation
- -
- Question 2: The assumptions regarding bearing conditions are accurate.
- -
- Question 3: The correlation used to estimate bearing RUL based on vibration readings is applicable and provides a fair estimation.
- -
- Question 4: The adopted approach reflects mechanical seals’ condition accurately.
5.3. Tool Practicality
- -
- Question 5: The proposed methodology helps reduce the total cost significantly.
- -
- Question 6: The tool is simple and user-friendly.
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- Question 7: The methodology and developed tool address the business need for using AI to optimise NGL pump maintenance.
5.4. Tool Diagnostic Functionality
- -
- Question 8: The tool provides useful diagnoses and recommended actions that reduce pump downtime.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
AI | Artificial Intelligence |
API | American Petroleum Institute |
ANN | artificial neural network |
CCW | Counter-Clockwise |
CNN | Convolutional Neural Network |
C-MAPSS | Commercial Modular Aero-Propulsion System Simulation |
FMEA | Failure Modes and Effects Analysis |
IMS | Inboard Mechanical Seal |
ISO | International Organization for Standardization |
LSTM | Long Short-Term Memory |
MOB | Motor Outboard Bearing |
MEP | mechanical, electrical, and plumbing |
MIB | Motor Inboard Bearing |
MTBF | mean time between failures |
NGL | Natural Gas Liquid |
OB | outboard |
OEM | Original Equipment Manufacturer |
OMS | Outboard Mechanical Seal |
PIB | Pump Inboard Bearing |
POB | Pump Outboard Bearing |
RAM | Reliability, Availability, and Maintainability |
RPN | risk priority number |
RUL | remaining useful life |
SAP | Systems, Applications, and Products (in Data Processing) |
SVM | support vector machine |
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Pump Components | Specification Data | |
---|---|---|
Pump Inboard Bearing (PIB) | Manufacturer | Byron Jackson® (Woodland, CA, USA) |
Pump Outboard Bearing (POB) | Type–size | DSJH-10X14X20L |
Motor Inboard Bearing (MIB) | Pump type | Horizontal |
Motor Outboard Bearing (MOB) | Capacity | 4715 usgpm |
Inboard Mechanical Seal (IMS) | Rated speed | 1770 rpm |
Outboard Mechanical Seal (OMS) | Maximum allowable speed | 1800 rpm |
Coupling | Total weight | 4873 lbs |
Output | Description | Related Input |
---|---|---|
MTBFs | Components’ mean time between failures | Failures Record (history) |
Bearing RUL Function | The function relates the vibration reading with the bearings’ RUL | Failures Record (history) |
Estimated Lifespan/Shutdown Frequency | This is the estimated replacement time for each component | MTBFs, Reliability Threshold |
Bearing RUL | The estimated bearings’ RUL based on the current vibration reading | Bearing RUL Function, Sensor Data |
Components’ Current Condition | The current health condition of components based on the sensor data | Senor Data |
Diagnosis and Recommended Actions | Failures diagnosis and recommended actions to reduce pumps’ downtime | Current Components’ Condition, Sensor Data |
Optimised Maintenance Routine | The optimum maintenance routine in terms of pumps’ availability and cost | Estimated Lifespan/Shutdown Frequency, Bearings’ RUL |
Input Data | Features | Limitations |
---|---|---|
Components’ Failure Record | Accurate | Very limited size |
Used easily to calculate MTBFs | ||
Single dimensional | ||
Sensors’ Historical Data | Shows certain patterns | Very limited size |
Single dimensional | ||
Real-Time Sensor Data | Accurate | Limited databases |
Detailed to support diagnosis | ||
Single dimensional |
Route 1 | Route 2 | |
---|---|---|
Pump IB Bearing | 1,507,364 | 1250 |
Pump OB Bearing | 2,261,046 | 2500 |
Motor IB Bearing | 2,261,046 | 2500 |
Motor OB Bearing | 2,261,046 | 2500 |
IB Mechanical Seal | 3,023,763 | 10,000 |
OB Mechanical Seal | 3,023,763 | 10,000 |
Coupling | 1,508,267 | 2000 |
Total | 1,584,6293 | 2,530,750 |
Route 1/Route 2 | 6.26 |
Number of Experts | Expertise Field | Employer |
---|---|---|
5 | Maintenance engineers | Saudi Aramco |
4 | Maintenance engineers | SABIC |
3 | Maintenance Managers | Saudi Aramco |
3 | Maintenance Technicians | Saudi Aramco |
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Almuraia, A.; He, F.; Khan, M. AI-Driven Maintenance Optimisation for Natural Gas Liquid Pumps in the Oil and Gas Industry: A Digital Tool Approach. Processes 2025, 13, 1611. https://doi.org/10.3390/pr13051611
Almuraia A, He F, Khan M. AI-Driven Maintenance Optimisation for Natural Gas Liquid Pumps in the Oil and Gas Industry: A Digital Tool Approach. Processes. 2025; 13(5):1611. https://doi.org/10.3390/pr13051611
Chicago/Turabian StyleAlmuraia, Abdulmajeed, Feiyang He, and Muhammad Khan. 2025. "AI-Driven Maintenance Optimisation for Natural Gas Liquid Pumps in the Oil and Gas Industry: A Digital Tool Approach" Processes 13, no. 5: 1611. https://doi.org/10.3390/pr13051611
APA StyleAlmuraia, A., He, F., & Khan, M. (2025). AI-Driven Maintenance Optimisation for Natural Gas Liquid Pumps in the Oil and Gas Industry: A Digital Tool Approach. Processes, 13(5), 1611. https://doi.org/10.3390/pr13051611