[Editor's Note: Majid Hussain is a member of the TWA Editorial Board and the author of previous TWA articles.]
Petroleum operations depend upon complicated machinery that is expected to work in challenging conditions. Drilling rig, pump, compressor, pipeline, separator, lift equipment, and injection systems often operate at high pressures, temperatures, vibrations, and corrosive conditions while also handling multiphase flows. Malfunctioning of any vital piece of machinery could result in a disruption of production, increased operational expenses, unsafe conditions, and sometimes environmental accidents.
Machinery reliability is necessary for safe and effective petroleum operations, rather than just a maintenance issue. It helps maintain production levels, lower the number of unplanned disruptions, increase equipment lifespan, and increases the profitability of the operation. It is even more important in offshore, deepwater, and remote oil fields.
Furthermore, the impacts of equipment failure extend beyond production losses. Failure of pressure-control systems, pipeline systems, compressors, or integrity elements may lead to the escape of hydrocarbons, an uncontrollable discharge of pressure, or damage to facility infrastructure. In operations that involve carbon capture, utilization, and storage (CCUS), the equipment reliability of compressors, injection wells, monitoring tools, and facilities is very relevant since failure affects the process of injection and the storage itself.
Traditionally, the approach of preventive maintenance has been the most popular strategy, which involves carrying out inspections at regular intervals or performing maintenance activities upon occurrence of a failure. Although the above maintenance approaches are effective, they have limitations because the approach is unable to identify quickly evolving equipment faults or changes in operating conditions. The oil industry generates vast amounts of data during operations through the generation of pressure, temperature, vibration, flow rates, sound, and electric currents.
Hence, this type of data could be used to predict failure of equipment before it happens. Thus, reliability management should move toward condition-based and predictive approaches.
Limitations of Traditional Maintenance Strategies
Conventional maintenance the industry is usually based on two strategies, namely reactive maintenance and preventive maintenance. Reactive maintenance carries out repair work only after an equipment malfunction. Meanwhile, preventive maintenance involves inspection, service, or replacement of equipment components at fixed periods of time. Even though both strategies are still common practice, there are shortcomings associated with them in highly technical and data-driven working conditions.
The application of reactive maintenance can lead to unscheduled plant stoppages, lost production, higher costs emergency repair costs, and safety problems. A spontaneous malfunction of devices such as pumps, compressors, valves, drilling equipment, or artificial-lift systems not only causes a breakdown of equipment, but also impacts other units involved in production. Offshore emergency repairs are also costly.
Although preventive maintenance addresses several of these issues, it frequently assumes deterioration of equipment follows a pre-planned schedule. However, this is not always true as factors like operating pressure, temperature, loading, corrosion, vibration, fluids, and equipment history influence deterioration. Thus, maintenance may occur too early when equipment is still operating normally or too late when parts wear out faster than expected. Either way, both approaches lead to additional expenses and decreased efficiency.
Another drawback is that traditional practices depend on manual inspection, operator knowledge, and pre-defined warning limits. This type of maintenance might miss weak signals of equipment deterioration buried in the huge amount of operation data. In addition, a certain level of pressure, temperature, or vibration may fail to provide an accurate picture of the overall condition of equipment and be mistaken for normal operation variations.
Finally, standard maintenance techniques do not generally take into account an interconnected approach to asset management, as they are asset-oriented and are often disconnected from operations planning. That means all relevant information about equipment performance and status is gathered by different departments using independent databases.
These constraints emphasize the necessity for adopting more-adaptive methods in maintenance. Through constant analysis of operating data, both past and present, condition monitoring, and prediction will identify failure precursors, determine the status of equipment, and make maintenance decisions according to operating realities.
How Machine Learning Supports Fault Diagnosis
Machine learning (ML) contributes to fault detection through discovering patterns within data that might be hard to discern either by human observation or by static threshold-based alarms. Oil-industry equipment generates constant data related to pressure, temperature, vibrations, flow rates, acoustics, RPMs, and electric current. This data allows an ML algorithm to differentiate between normal operations and the early stages of equipment deterioration.
This usually starts with collecting raw data from sensors, maintenance logs, and operation systems. Afterwards, the collected data is preprocessed, synchronized, and feature extraction takes place. The traditional ML technique implies feature extraction based on human expertise by choosing specific attributes like vibration amplitude, frequency domain properties, pressure variation, or temperatures.
Deep-learning techniques can lessen the requirement for manual feature extraction through learning from unprocessed or lightly processed data. For instance, convolutional neural networks recognize patterns within vibration signals or sensor images, whereas recurrent neural networks capture temporal dependency within sequential operations data. Unsupervised learning methods like autoencoders prove useful in cases where there is insufficient failure data, since they are capable of learning normal operational patterns and identifying any deviations from these.
ML aids in fault diagnosis at multiple stages. It can, first of all, determine whether there is abnormal operational behavior in equipment. Moreover, ML helps in recognizing the type and location of the problem, i.e., bearing wear, valve malfunction, pump cavitation, compressor instability, as well as pipeline leakage.
Such tools are beneficial in operations by aiding better decision-making about pumps, compressors, drilling machines, artificial-lift systems, pipelines, and injection systems. With data-driven techniques, engineers can detect early signs of problems before triggering alarms or breakdowns and plan for maintenance in advance. However, it is essential that ML outcomes be employed to supplement, not override, engineering expertise. The accuracy of such results depends heavily on data quality and appropriate modeling.
Applications in Drilling, Production, Pipelines, and CCUS
The application of ML techniques for fault diagnosis is gaining importance across the whole spectrum of the supply chain, including drilling operations, production processes, pipelines, and CCUS. The equipment used and the environment may vary greatly between these fields, but the overall goal is to detect abnormalities, prevent unplanned downtime, and improve safety during operations.
For example, during drilling, various measurements can be performed using ML, such as weight on bit, torque, speed of rotation, standpipe pressure, rate of penetration, and vibration. These parameters can then be analyzed in order to detect any dysfunctions in the drilling process, including stick/slip, bit bounce, excessive vibration, losses of circulation, and impending failure of machinery.
In production systems, there is the potential for applying data-driven modeling to monitor pumps, compressors, separators, valves, and artificial-lift devices. For instance, variations in parameters such as pressure, flow, electric current through the motor, temperature, and vibrations could serve as indicators of problems such as damage to the pump, gas interference, scaling, leaking, or compressor instability. ML models are capable of integrating several parameters to differentiate between normal fluctuation and a developing fault, thereby allowing planning of maintenance operations before the onset of production losses.
ML-based analysis could also be used in pipeline systems. Pressure, flow rate, acoustic wave, and temperature sensor data may be used to provide information to identify conditions such as leakage, corrosion-based deterioration, obstruction, or anomalies in the pressure profile. ML models can link several different variables, which means potentially faster detection of problems, compared to threshold values used by standard alarm systems.
The reliability of equipment is a key issue for the constant CO2 capture, transportation, and injection in CCUS operations. ML can be used to identify abnormal pressure behavior, compressor efficiency issues, signs of leakage, loss of injectivity, and well-integrity issues. ML data analysis also has the potential to aid in plume monitoring and pressure management. In any case, the main advantage of ML is its ability to process large amounts of operational data and transform them into relevant information.
At the same time, it should be acknowledged that the effective implementation of ML techniques in the discussed context calls for cooperation between data scientists, petroleum engineers, equipment experts, and CCUS operators. The optimal solution involves the combination of ML algorithms with physics-based models and conventional safety practices.
Barriers to Industrial Implementation
There are still many challenges for the application of fault diagnostic systems based on ML techniques. Even though many academic studies have used clean and well-defined data sets for model development, real field data sets in petroleum engineering are noisy, uncertain, and heterogeneous. The reasons include the presence of sensor drift, faulty communication between sensors, inconsistent sampling rate, data loss, and variation of operational parameters.
One of the most crucial problems associated with ML fault detection models is the availability of data. Equipment is designed to operate flawlessly and therefore experiences faults infrequently.
Consequently, it is common to encounter an imbalance in data sets, where there are considerably more records of normal operation than faults.
Another problem that arises in building ML fault diagnostic models is related to the transferability of such models. They might be built for a particular piece of equipment and might not work with other equipment because of differences in design, type, and location of sensors, characteristics of fluid, or operational modes.
It is also important to note that interpretable models will be more accepted by industry practitioners. Field engineers and operators will be less confident in a model's predictions if it raises an alert without providing a justification for this action. For instance, for critical operations, predictions from models must come with evidence that can be explained, such as abnormal vibration, pressure, temperature, or operating performance.
Furthermore, there are other issues associated with integration into existing systems. Facilities typically use legacy control and database systems, as well as data in various formats. ML systems require investments in data infrastructure, cybersecurity, and communication capabilities. Companies also need to define responsibilities for acting on model alerts and integrating these into their maintenance processes.
Last but not least, implementation is not only dependent on the software used. Multidisciplinary collaboration, training, and managerial oversight is critical, as data scientists may not be trained in field operation, and engineers and technicians may have little knowledge of ML. New solutions should be introduced as supporting tools in controlled pilots, not to replace engineering judgment.
Skills Young Professionals Should Develop
The increased use of artificial intelligence and data-driven technologies in oil and gas requires a certain set of skills in the new generation of engineers. It is still necessary to have profound knowledge in the field of petroleum engineering, since the ML output will never make sense if not analyzed against the physical behavior of wells, reservoirs, pipelines, and the equipment on the surface.
A minimum level of competency in data analysis is increasingly relevant. Young professionals must feel confident enough using production data, identifying missing and inconsistent variables, detecting correlations and anomalies, and building visualizations. Some familiarity with programming languages, such as Python, along with ML techniques, used for classification, regression, anomaly detection, and time-series analyses, allows engineers to be more active in their digital projects.
Moreover, some understanding of condition monitoring and various types of sensors is useful. An engineer must know what kind of physical processes lie at the heart of measuring vibration, pressure, temperature, acoustic emissions, and electric currents, and how good sensors influence the accuracy of model results.
Young professionals also need to understand model validation and uncertainty assessment. It is not uncommon that a good ML model would work well with historical data but not be capable of functioning correctly when conditions in the field change. This means that an engineer needs to understand how to assess the model's accuracy, false alarm probability, misses, strength, and transferability.
Thirdly is communication and cooperation skills. There are numerous specialists participating in development and implementation of reliability initiatives based on data analytics: petroleum engineers, data analysts, maintenance workers, equipment suppliers, and field workers. All of these specialists need to communicate and collaborate.
Fourth, engineering intuition should always be the core of everything. The role of ML should be supportive rather than a replacement of physical insight, safety processes, and field experience. Those who understand this are likely to become the leaders of the next generation of oil operations.
Future Outlook
In the years to come, the success of equipment reliability programs will be contingent upon the implementation of artificial intelligence, real-time monitoring, and physics-based modeling. With technological advances making sensors cheaper and the ability to acquire and process data better, asset managers will gain the capability to monitor the condition of equipment around the clock and to spot potential issues much sooner than it would have been feasible with traditional means.
One such advancement will likely be a rise in the use of digital twins, where the live data from the plant or pipeline will be used along with information about the specific machinery to create an accurate depiction of its present state. Then, through ML algorithms, the system can detect anomalies from the expected performance and evaluate various actions without needing physical involvement. Another expected development will be physics-based ML, which may prove superior in terms of predicting the actual behavior under changing conditions and preventing unlikely results.
One of the potential directions for future developments will be creating even more autonomous maintenance systems. Machines will not only be able to identify problems but also assess their severity, predict remaining useful life, make recommendations about actions, and prioritize maintenance based on the impact on safety, manufacturing processes, and economics. Nevertheless, human control will be needed, especially for applications in which wrong decision-making can lead to catastrophic consequences.
Within CCUS, intelligent reliability systems might assist with monitoring the condition of compressors, pipelines, injection wells, pressure-management systems, and even detecting abnormal activity underground. Additionally, similar systems may help in monitoring emissions of methane gas, operating geothermal plants, transporting hydrogen, and managing other new technologies.
A shift from research activities in universities to practical industrial application will require reliable models, good data, cybersecurity measures, proper regulations, and cooperation between engineers, operators, and data scientists. The most efficient solutions would not necessarily involve the most sophisticated algorithms; rather, those that resolve real-life problems, offer reliable outcomes, and provide tangible value would succeed.
It is safe to say that ML is not likely to replace petroleum engineers and maintenance personnel; rather, it will enhance their capability to comprehend the behavior of the equipment and make timely decisions. The future generation of engineers combining petroleum engineering skills and data knowledge will have opportunities to be part of this revolution.
