“Data is the new oil.” Nearly two decades ago, British mathematician Clive Humby made this statement, noting that both of these valuable resources needed to be “extracted, refined and processed to be truly useful.”
For oil and gas companies, geospatial data is the new oil, and it’s growing in value. Surveying has been around since the industry’s early days. But what has changed is the amount and accuracy of the data, how we consume it and the decisions we base on it. The geospatial data derived from exploring our oilfields, pipelines and surrounding areas is a vital asset that can help us improve operational efficiency and make strategic decisions
Integrating accurate geospatial data across a company is going to be a key competitive advantage as artificial intelligence (AI) becomes more engrained in our industry. However, the journey towards seamless data integration is not without challenges. Data remains bottled up in departmental silos, inaccessible for widespread use across the entire organization. And the data can be plagued with errors, which is being exposed as companies begin to apply AI more widely in their operations.
A Hurdle to Realizing the Potential of AI
The oil and gas industry has been gradually embracing AI and advanced analytics. Traders and financial analysts have been quick to adopt AI tools to handle vast amounts of data and assist them in navigating the volatile nature of oil and gas prices to produce more precise forecasts. Now engineering and drilling teams are also beginning to see the benefits. AI allows them to easily analyze vast amounts of data being generated to optimize drilling paths, predict potential equipment failures and become more proactive about maintenance, monitor operations in real-time for safety concerns and make more informed decisions about where and how to drill, and how to manage production. Use of AI will enable oil and gas companies to improve efficiency, reduce costs and do their jobs more safely.
However, the full potential of AI can only be realized with high-quality, integrated geospatial data that flows bidirectionally. The problem now is that data in the oil and gas company systems may not be accurate. In many cases they may have used mathematical modeling instead of as-built data, leaving substantial room for error. AI will expose these weaknesses as it uses the data to train models, resulting in analysis that is incorrect, and potentially dangerous.
To ensure that AI delivers the benefits expected, weak data needs to be cleaned up, reworked and standardized. This can seem like a herculean, expensive task, but the time and cost is worth it in the long run when accurate data delivers AI insights that can positively impact the business.
Disparate systems also need to be connected so that all organizations within the company have access to the same data. Working across these systems, AI tools can help identify patterns and insights that were previously hidden in silos.
Achieving AI Benefits Organizationwide
With the promise of AI in mind, let’s take a look at how your company can effectively integrate geospatial data and see benefits across every department. Consider these steps to ensure integration projects progress smoothly and deliver the results you anticipate:
1 – Evaluate Your Situation and Needs
One of the primary challenges your company could face is accurately assessing your current situation and needs. You may recognize the importance of geospatial data, but overlook key aspects such as data quality and each department’s specific requirements.
Identify who needs the data and what they need it for. For example, the engineering team will use geospatial data much differently than the financial team. One will leverage the data to inform precise drilling and safe operations, while the other is more concerned about pricing and forecasting.
2 – Get Buy In to Create a Data-Driven Culture
Achieving successful data integration requires buy in from multiple stakeholders, including executives, financial teams and line-of-business (LOB) groups. For executives and those who manage budgets, it is important to show the strategic value of geospatial data and the benefits delivered by integrating it across the organization. Emphasize how it can enhance operational efficiency, improve safety and support compliance with regulatory requirements. Demonstrating a clear return on investment (ROI) can also be a powerful motivator.
Engaging the various departments is equally important. These teams are typically the primary geospatial data users, and their support is needed for a successful integration. Involve them in the planning process, address their specific needs and ensure the data available meets the appropriate specifications.
3 – Consider the Sources and Quality of Data
Geospatial data can be sourced from various places, including satellite imagery, drones and field surveys. However, the quality of this data can vary widely. Poor data quality can lead to significant errors and inefficiencies.
Confirm that the data is accurate, up-to-date and relevant to your organization’s needs. One often overlooked aspect of geospatial data integration is the health of your data. This has become even more pronounced with use of AI, which can expose data issues that previously went unnoticed, leading to a constant cycle of identifying and fixing errors. Regular data health checks and validation processes will help maintain optimal data quality.
4 – Address Integration Challenges
The integration of geospatial data involves connecting multiple systems, each with its own data formats and standards. This complexity can be daunting, but it’s essential for creating a unified data environment. Most organizations use Esri, the leading GIS software, but even with this standardization, integration challenges persist.
Each department has their own data silos, which need to be connected to run models that deliver impactful insights. Additionally, there may not be data on every aspect of the operation, which leaves significant room for error. For example, companies may have detailed pipeline data, but when that terminates on the pad, they may not have detailed as-built information about how the oil flows through the facilities. Using lidar scans, companies can build a digital twin of these facilities to have a full, accurate view of end-to-end operations.
Effective integration requires a robust strategy that includes data validation, system compatibility checks and continuous monitoring. Importantly, there must be a bidirectional data flow. Updates in one system should be reflected across all connected systems, to minimize discrepancies and provide a single source of truth
5 – Provide Training to Foster Widespread Adoption
Once integration is complete, it’s imperative to provide training to create an organization wide data-driven culture. Foster a mindset in which data is considered just as valuable an asset as the oilfields and pipelines. Your employees should feel confident that they can understand the data and then act on the insights derived through the power of AI. Integration also encourages collaboration among different departments toward a common goal.
And, perhaps most importantly, there needs to be transparency about data quality issues. Have employees collectively commit to continuous data improvement. Doing so will build trust in, and enhance the usefulness of, the geospatial information at hand.
With a greater quantity, and more detailed, geospatial data available, oil and gas companies are sitting on a gold mine of riches that can enhance their business with the power of AI…but only if systems are integrated and data is accurate. As the industry continues to evolve, those who successfully navigate these complexities and successfully integrate geospatial data across their organization will be well-positioned to take the lead in the new frontier for the oil and gas industry.