The oil and gas industry faces significant challenges such as energy security, affordability, profitability, and the shift towards a cleaner and sustainable future. Generative AI for oil and gas holds promising potential to address these issues and to drive cost avoidance, operational efficiencies, and resilience.
Tovie Data Agent is a fast and flexible AI tool for enterprise data search. It is instrumental in the energy, resources, and industrial sectors, especially for enterprises with extensive databases and complex documentation.
Data Agent is a generative AI chatbot that can be built quickly and easily on your data. It provides a fast and easy way for teams to access information, leading to significant cost and time savings when searching for specific documents and information. Let’s discover how our generative AI data querying solution can benefit the energy sector.
Data Agent can be configured to provide step-by-step instructions to help users troubleshoot equipment and processes following established policies and procedures. It allows users to navigate typical problem-solving scenarios and simplify procedures, such as the Job Safety Analysis (JSA) process.
Troubleshooting complex equipment and processes requires specialised expertise, which can be scarce. Data Agent can accelerate troubleshooting by quickly and efficiently accessing relevant logs and manuals. This reduces the time needed to diagnose and rectify problems, making it easier for teams to address equipment issues effectively.
Engineers working in remote or challenging environments may lack manuals or need to localise the source of a problem. Data Agent can be implemented as a virtual field assistant, providing engineers with on-demand access to engineering knowledge and support in troubleshooting. This increases efficiency, productivity, and decision-making.
For instance, if a problem crops up in the field, the engineer can describe it to the AI assistant, which will respond with appropriate questions to identify the cause or guide the engineer through a step-by-step process to resolve it.
Using generative AI for the energy sector can help enhance operational efficiency and achieve cost savings by providing a support system for their engineers. However, it’s important to note that the accuracy of any AI chatbot depends on the quality of the data used to train it. The outputs are only as good as the data used; inaccurate or obsolete data can cause harm, damage to equipment, or operational downtime.
2. Asset maintenance planning and predicting machinery failures
Data Agent can play a crucial role in diagnosing and forecasting issues in plant machinery. It can use historical and real-time data from machinery logs and manuals to predict and analyse potential risks and failures associated with specific machine models.
Risk analysis often requires comparing data from various sources and systems, a process that is usually manual and labour-intensive. This manual work can introduce errors, making it less reliable. Additionally, the wide variety of source data and its automated consumption can be challenging, especially when dealing with sensitive information that requires strict data security compliance.
Data Agent optimises maintenance schedules by considering data on various operational factors such as equipment use, production requirements, and maintenance costs. It recommends efficient and cost-effective maintenance schedules and analyses equipment use and performance info to reduce downtime and increase equipment availability.
However, it is essential to note that generative AI for the oil and gas industry cannot completely replace the knowledge, experience, and expertise of human asset maintenance planners. Relying solely on AI-generated outputs without critical human review may overlook important contextual factors and valuable insights.
Generative AI in the supply chain can increase its optimisation by simulating, modelling, and generating data-driven insights. Supply chain management in the oil and gas industry is complex, with numerous dependencies and multiple stakeholders. Quick data analysis from internal and external sources is needed to identify patterns and spaces needing improvement. In this context, Data Agents can identify and model potential disruptions or risks in the supply chain.
Supply chain managers can use Data Agent to run “what-if” scenarios. By simulating a change in the demand pattern, production capacity, inventory strategies, or supplier reliability, risks can be assessed effectively, and real-time conditions can be used to improve the effectiveness of proactive decisions.
Data Agent also aids in supplier evaluation and relationship management. It analyses financial reports, performance metrics, customer feedback, and other data to generate analytics and predictions on supplier performance, risk factors, and collaboration opportunities. This helps supply chain professionals make informed decisions when selecting, negotiating, and managing suppliers.
However, one crucial aspect of supply chain digital transformation that needs to be addressed is the biases in data or models when applied to supplier evaluation, negotiation, and contracting. Biases may give rise to unjust recommendations and discriminatory practices. Organisations can thus ensure their fair and transparent decision-making by considering fair contract terms or social responsibility and ethical sourcing practices.
4. Documents and compliance assistance for the legal department
Data Agent helps the legal department streamline its work by enabling it to draft most routine legal documents, saving time and reducing errors. It can also summarise lengthy financial papers or reports, providing concise, easy-to-read summaries that help finance professionals make quicker decisions.
In addition, Data Agent can review and analyse contracts to identify crucial terms, obligations, and potential risks. This ensures compliance with legal standards, regulations, and internal policies. It can also monitor changes in laws and regulations and notify the legal department about any developments that may impact the organisation.
Furthermore, Data Agent will further improve the efficiency of legal research. It will automatically sift through case laws, statutes, and regulations for research purposes. This supports legal arguments and decision-making, making the research process faster and more accurate. Effective data management in the oil and gas industry is crucial, and leveraging generative AI can significantly enhance efficiency and decision-making processes.
A frequently asked questions (FAQ) bot can greatly benefit the finance team by extracting information from various financial documents, information sources, and agreements. It will efficiently handle common and repetitive queries related to financial transactions, account details, billing, and other routine matters.
This bot can be deployed over the entire organisation and act as a central tool for answering standard questions and supplying solutions to personnel from different departments.
Data Agent can also summarise lengthy financial documents or reports, which is typically very time-consuming. This results in significant time and efficiency gains, allowing finance professionals to quickly understand the contents of reports. Moreover, enhanced data management for oil and gas leads to operational cost reductions through improved role efficiency.
Data Agent can significantly streamline the onboarding process for new team members by providing easy access to relevant documents, training manuals, policies, procedures, and equipment user manuals. Serving as a unified source of truth, it helps new employees quickly acclimate to their roles, filling knowledge gaps and boosting productivity.
Onboarding involves numerous procedures and processes, which can be overwhelming for new colleagues. Data Agent simplifies this by offering a centralised, easily accessible resource for the company’s databases.
As a result, new team members become more efficient and quicker, increasing throughput. Moreover, the HR department can onboard and upskill new colleagues more efficiently. And teams get a universal, easily consumable source of truth for procedures and policies.
According to McKinsey, new GenAI use cases can significantly impact the global economy, ranging from 15% to 40% within the next several years. This is especially true for organisations focused on innovation, data analysis, and process automation, such as those in the energy and materials sector.
Data Agent is an invaluable tool for driving oil and gas digital transformation. It swiftly retrieves necessary instructions or details from technical manuals, boosting staff efficiency in problem-solving tasks and ensuring smooth operations.
Exploring and implementing Data Agent and other AI-powered tools now allows companies to adapt to new technological nuances and stay ahead as the technology evolves. This proactive approach ensures innovation in the oil and gas industry and full leverage of generative AI’s capabilities as it matures.
Establishing these capabilities early and securing a foothold in GenAI enables rapid adoption of more advanced models in the future. If you need assistance determining the most impactful generative AI use cases and solutions for the oil and gas industry, consider consulting with our AI experts. They will analyse your organisation’s processes, brainstorm practical use cases with your teams, and identify applications that will benefit your organisation the most.
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