How do you leverage digital technologies and data analytics for EOR?

1 Data acquisition and integration

One of the key challenges of EOR is to obtain reliable and comprehensive data about the reservoir characteristics, fluid properties, and injection and production rates. Data acquisition and integration involve using various sensors, devices, and systems to collect, transmit, store, and combine data from different sources, such as wells, pipelines, surface facilities, satellites, and seismic surveys. Data acquisition and integration can help you create a more accurate and dynamic model of the reservoir, identify the best locations and methods for EOR, and detect any anomalies or changes in the reservoir behavior.

2 Data analytics and visualization

Another challenge of EOR is to analyze and interpret the large and complex data sets that are generated by data acquisition and integration. Data analytics and visualization involve using advanced algorithms, software, and platforms to process, filter, correlate, and extract insights from the data. Data analytics and visualization can help you understand the reservoir performance, optimize the EOR parameters, evaluate the EOR outcomes, and communicate the results to stakeholders. Data analytics and visualization can also enable you to use artificial intelligence (AI) and machine learning (ML) to automate and enhance the EOR decision-making process.

3 Data-driven optimization and control

A third challenge of EOR is to optimize and control the EOR operations in real time, taking into account the uncertainties, constraints, and trade-offs involved. Data-driven optimization and control involve using data analytics and visualization to generate and implement optimal solutions for the EOR objectives, such as maximizing oil recovery, minimizing costs, or reducing environmental impacts. Data-driven optimization and control can help you adjust the EOR parameters, such as injection rates, pressures, volumes, or compositions, based on the feedback from the data. Data-driven optimization and control can also help you monitor and manage the EOR risks, such as water breakthrough, reservoir damage, or well failure.

4 Data collaboration and sharing

A fourth challenge of EOR is to collaborate and share data among different stakeholders, such as operators, contractors, regulators, or researchers. Data collaboration and sharing involve using digital platforms and networks to facilitate the exchange, access, and use of data among different parties. Data collaboration and sharing can help you improve the EOR knowledge, innovation, and best practices, as well as comply with the regulatory and contractual requirements. Data collaboration and sharing can also help you build trust and transparency among the stakeholders, as well as foster a culture of learning and improvement.

5 Data security and governance

A fifth challenge of EOR is to ensure the security and governance of the data, especially when it is sensitive, proprietary, or confidential. Data security and governance involve using digital technologies and policies to protect, manage, and regulate the data from unauthorized or malicious access, use, or modification. Data security and governance can help you safeguard the EOR assets, operations, and reputation, as well as respect the data ownership, privacy, and ethics. Data security and governance can also help you comply with the legal and ethical standards and obligations related to data.

6 Data value creation and capture

A sixth challenge of EOR is to create and capture value from the data, especially when it is abundant, diverse, or dynamic. Data value creation and capture involve using digital technologies and strategies to monetize, leverage, or enhance the data for the benefit of the EOR business, operations, or stakeholders. Data value creation and capture can help you generate new revenue streams, reduce costs, improve efficiency, or create competitive advantages from the data. Data value creation and capture can also help you measure and demonstrate the return on investment (ROI) or the value added by the data for the EOR project or portfolio.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?