A major challenge faced by the industry when it comes to asset management, is to analyze large amount of data which can be useful in fetching insightful information for decision making. It is inefficient and humanly infeasible to analyze the data manually, hence it becomes rather essential to develop algorithms which can identify the events of interest, and ultimately will result in effective assets management.
Now gaining a global recognition, machine learning is identified as an opportunity to utilize large volume of real-time information and translate massive data into actionable insights. The algorithms are powered by ability to process a huge chunk of data in real-time. This helps in facilitating quick identification of trends and pattern, which otherwise would be difficult and time-consuming. Algorithms are domain-specific and vary from each, Drilling, Reservoir, and Production.
- Drilling Operation Optimization
- Reservoir Management
- Production Parameter Optimization
- Field Equipment Management
Drilling process in upstream sector is one of the riskiest and expensive ventures, hence this needs to be accomplished with detailed planning and effective execution. Applying ML and AI in the operational planning and execution stages can help improve the well planning process significantly, real-time drilling optimization, friction drag estimation, and other well prediction. In addition to this, the technique can also be applied in the field of geophysical aspect, where the well variables like the Rate Of Penetration (ROP) improvement, well integrity, drilling equipment condition recognition, real-time drilling risk recognition, and of course the operational decision making.
A lot of factors need to be taken into consideration while applying techniques like machine-learning in drilling. Few to name are the traditional data such as pressure differentials at various points, thermal gradient, permeability, porosity, and seismic vibrations.
Reservoir being the core of the entire operation in upstream, needs to be maintained and optimized to increase the longevity of the whole production cycle. Therefore, reservoir, facility and the well need to be managed, and this requires an integration of multiple disciplines, such as geology, seismic interpretation, reservoir engineering, production techniques and various other operations. AI techniques are applied in activities like reservoir characterization, modelling and field surveillance.
Fuzzy logic, expert systems and artificial networks are used to accurately characterize reservoir for optimum production output. Complex logics are required to derive a relationship between critical functions like algorithms defining relationship between seismic attributes, and target lithological properties such as well logs and sand properties.
With the oil prices fluctuating new venture in exploration seems to be slowed down, in such a scenario, companies need to optimize the production and manage the decline in production. Decline Curve Analysis (DCA) is one technique used to estimate future production based on the historic data. However, the well’s decline towards the end of its life, follows a nonlinear pattern, and usually declines quicker at its depletion stage. Production optimization basically deals with the smart management of parameters that will enhance the well’s life such as Pressure, flowrates, and thermal characteristics of injected fluid mixture. Machine learning algorithms can be used to analyze the sensor data, which is collected in massive volumes to determine the health of the system and to identify the optimum operating environment.
The value of AI and machine learning can be applied in a different statistical model, which can help improving asset management decision. Effective adoption of these learning technique will be dependent on the integration, with data visualization and effective user interface design.
Often the fields and the wells are in remote locations and gathering data on the status of the pumps and well physically, becomes a tedious job. This would sometimes make the data sparse and difficult to access; however, the cost of equipment and the potential it must deliver, needs to be optimized. With the advance in technology, Oil & Gas IoT sensor data is collected at the server level. This data is enriched with other well information and geological complexities to create a more complete picture with the help of various algorithms of what’s happening in the field, or might happen if certain parameters are to be ignore, for example, situations like equipment failure, its reason for failure, how it arrived at that stage. If it failed sooner than expected, and can it be avoided in the future? Such techniques help in increasing the life of the equipment with less risk of accidents and lower maintenance cost for best possible results.
The Oil and Gas industry has evolved itself in the past few decades. Adoption of new technologies like directional drilling and hydraulic fracturing has helped, the industry still needs to continue to strive for innovation to survive in this low-price market. The impact of ML and AI has already been realized in the industry. Early adopters are taking advantage with a head-start in the competition to protect their assets.
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