Nick King on improving oil & gas exploration processes and profitability through applied AI.
Applied AI stands at the forefront of technological innovation, holding transformative potential across various industries. In the realm of oil and gas, AI emerges as a powerful tool, promising enhanced exploration processes, operational efficiency, and profitability. However, navigating the integration of this transformative technology presents its challenges, requiring a strategic approach to unlock its full potential.
Strategic Focus on Outcomes
Embarking on the journey of AI integration in the oil and gas sector necessitates a meticulously strategic approach, where the roadmap is clearly defined by prioritising outcomes. The initial phase involves a profound focus on mapping desired business outcomes. This requires a comprehensive understanding of the objectives that the integration aims to achieve, ensuring that the AI initiatives are precisely aligned with the business goals and operational enhancements sought.
Following the outcome mapping, the focus should shift towards alignment with team workflows and expertise. It is essential to ensure the AI technologies integrated are in harmony with existing workflows, enhancing rather than disrupting operational processes. Collaboration becomes key in this phase, requiring a synergy of expertise, knowledge, and skills among internal teams, ensuring that the AI technologies are seamlessly woven into the organisational fabric, leveraging the collective expertise to drive innovation and efficiency.
Technical alignment is crucial. The chosen technologies should not only be robust and innovative but also synergistic with the mapped outcomes and organisational workflows. This involves selecting technologies that resonate with the business’s strategic objectives, ensuring that they augment the existing technological ecosystem, driving enhanced capabilities, and fostering a conducive environment for innovation and growth.
Embracing Declarative AI
The adoption of ‘declarative AI’ models is pivotal in navigating the complexities of AI integration and application in the oil and gas sector. Declarative AI represents a paradigm where the focus is on specifying the ‘what’ rather than the ‘how’. In simpler terms, it involves declaring the desired outcomes, and the system autonomously determines the best approach to achieve those outcomes.
These models are grounded in preconfigured capabilities, enabling them to autonomously orchestrate tasks to achieve specific objectives without necessitating explicit, step-by-step instructions. This approach fosters a more intuitive and efficient development process, allowing for the rapid composition and deployment of applied AI applications.
Declarative AI streamlines the application development process, reducing time-to-market, and allowing teams to concentrate more on strategic, outcome-focused aspects of projects. It facilitates a more agile and adaptive application development process, enabling the oil and gas industry to swiftly respond to evolving demands and challenges with innovative AI-driven solutions.
In the dynamic landscape of oil and gas exploration and production, embracing declarative AI equips industry players with the agility and innovation necessary to drive enhanced operational efficiencies, strategic decision-making, and profitability.
Platform-Agnostic Approach
A flexible, platform-agnostic approach is essential in the dynamic landscape of applied AI. This flexibility allows organisations to leverage the best tools and technologies across various platforms, ensuring adaptability, and fostering innovation tailored to industry-specific needs and challenges.
Key Applied AI Features in Oil & Gas
Applied AI can be used in oil and gas applications in a number of different ways. One application is for predictive maintenance. The objective here is to minimise unplanned downtime and optimise maintenance schedules. How this can be achieved is by using AI algorithms to analyse equipment data, identifying patterns and anomalies that precede failure. This approach enhances operational efficiency, reduces maintenance costs, and improves overall equipment effectiveness.
Pipeline Monitoring
When using applied AI for pipeline monitoring, the aim is to ensure the integrity of pipeline infrastructure and prevent leaks or failures. AI analyses data from sensors along pipelines, detecting anomalies and predicting potential failure points. This improves environmental safety, reduces the risk of costly leaks, and ensures continuous operation.
Exploration data analysis is another good example of how applied AI can be used in this sector. Here, it can enhance the discovery and evaluation processes of new energy reserves by deploying AL algorithms to analyse geological and seismic data, identifying patterns and insights that guide exploration decisions. This helps to streamline the exploration process, improves decision-making, and increases the likelihood of discovering viable reserves.
Supply Chain Optimisation
Improving the efficiency and responsiveness of the supply chain is also possible. Here, AI analyses supply chain data, optimising routes, inventory levels, and supplier interactions.
Impact: Reduces operational costs, minimises waste, and improves the adaptability of the supply chain.
AI-Powered Project Design and Delivery
The objective here is to enhance the planning, design, and execution of oil and gas projects.
AI supports project managers in scheduling, risk management, and resource allocation, utilising historical data and predictive analytics.
Ultimately, this approach improves project outcomes, reduces risks, and ensures projects are delivered on time and within budget.
Summary
Applied AI holds transformative potential in the oil and gas sector, promising enhanced operational efficiencies, innovation, and profitability. A strategic, outcome-focused approach, coupled with the adoption of declarative AI models and a platform-agnostic strategy, is essential for navigating the integration journey successfully. By harnessing the power of AI, industry players can unlock new realms of possibility, driving forward innovation, efficiency, and success in the oil and gas exploration landscape.
Nick King is CEO and founder of Data Kinetic.