Digital Transformation Framework for Geophysical Data | Katalyst DM

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Digital Transformation Framework for Geophysical Data

Digital Transformation for Geophysical Data

You’ve made the decision to begin (or at least take the next step in) your company’s digital transformation. You realize there are benefits to be had, like organizing volumes of geophysical data that hold valuable insights, taking advantage of big data analytics and finding value in existing assets. But where to begin? Katalyst Data Management has undertaken a digitization strategy framework for geophysical data that includes the four key components of digital transformation, no matter the size of the company – Digitize, Manage, Analyze and realize Value.

This article will provide a step-by-step process to achieving digital transformation for your subsurface data. If you’re searching for more detailed information for each stage of digital transformation, read the linked articles in each section.

What a Framework Should Achieve

The simple goal of a digital transformation framework is to establish a blueprint for growth, guiding companies through their digital transformation journey. As an added benefit, a digital transformation framework will help connect the (usually disparate) digital technologies already implemented in your company and create a sense of order while bridging the gaps. The new framework should consolidate:

  • Legacy apps and services that run on dedicated servers and require a local network connection
  • Cloud applications that IT departments often don’t even know about
  • A diverse array of endpoints ranging from smartphones to old laptops
  • Data workflows that cross organizational, geographical and regulatory boundaries on a regular basis


For E&P companies, the digital transformation journey begins with digitizing your subsurface data.

Data Digitization

Transferring data from legacy media into digital formats can seem like a daunting task when you consider the sheer quantity of data that has been collected over the years. So where do you begin? Start with tape transcription.

Companies can work with a data management expert to devise a tape transcription plan, a detailed roadmap for digitizing legacy media to standard SEGY formats on modern media. Digitizing comprises standardizing the data and assessing data quality. Once transformed, your data is future proofed and won’t be forgotten or lost. Digitization is preservation.

Domain Knowledge

Knowing your geological and geophysical data is critical to determining the right digital transformation plan and identifying gaps in your transformation efforts. That’s why it’s important to pick a digital transformation partner who is an expert in your subsurface data types. Domain knowledge coupled with technical skill in data digitization will drive your digital plan, ensuring it aligns with your business process and objectives. For example, metadata capture during digitization is essential to maintaining data quality and ensures accessibility within the subsurface database. Domain experts will be integral in the process to monitor quality control and help create the most effective subsurface data management plan.


Data management comprises quality control, metadata capture, verification, spatial representation, data recovery and ongoing management.

Data Management

Prior to starting a legacy data digitization project, you should begin by considering how the data will be managed. How will data be collected going forward? Will you use on premise storage? Cloud or multi-cloud storage? Who will have access to the data? What programs can be used to access it? What interface is most effective to facilitate discoveries? How can your products and services be enhanced through data management? Ideally, you’ll be able to view and spatially interact with your organized subsurface data within a structured metadata model such as PPDM.

Although you’ll make critical decisions about how data is managed in this stage,data management spans the entire lifecycle of digital transformation, from data acquisition to analytics and everything in between. Subsurface data management is always evolving and has no end.

Data Quality and Data Integrity

How do you manage data quality and integrity with big data? Start with a subsurface data audit. Often, a data audit is performed to determine the quality and accuracy of data sets, and can potentially save companies millions of dollars. The data audit process will pose the right questions to help you develop a data quality plan and preserve data integrity. Accurate data leads to rapid decision making, reduced cost of operations and improved safety, reliability and marketability.

Data Security

Data security – the elephant in the room of every discussion about digital technologies and increasing digital business models. With more subsurface data than ever stored in the cloud, continuous improvements in data security are required. Customer expectations demand bulletproof security, from encrypted communications to constant monitoring. At Katalyst, we operate private Tier 2 datacenters, making sure data is as secure as it is convenient to access. If data has been “lost” or damaged, data recovery services can bring back geological and geophysical data from myriad issues with legacy media, like stiction, file corruption or electronic damage.


By integrating historical subsurface data with new data on a single platform, operators have access to incredible insights.

Data Analytics

At this stage, your legacy data and incoming data live together on the same platform, crucially tagged, categorized and organized with consistency. Now, you are really ready to take advantage of data analytics. Big data analytics can reveal trends and issues that lead to major operational changes and improvements in efficiency. With data analytics, you are now able to optimize exploration and production with machine learning and predictive analysis, leading to new insights.

AI and Automation

Automation is taking over the oil and gas industry. Companies that aren’t looking at automation are already considered behind. AI and automation take analysis and turn it into real value by making sense of all that data, enabling smaller and more streamlined teams. Additionally, AI, automation and the standardization of the subsurface data enables predictive analysis. Learn more about these technologies in the “Value” section, where the full benefits are realized.


Value is realized throughout the process, but once the transformation is complete, a company will experience efficiencies and results previously thought impossible.

Digital Transformation

The full digital transformation results in countless benefits to companies willing to put in the work. Benefits such as:

  • Using big data analytics
  • Trustworthy data for better decisions
  • Automated exploration and interpretation
  • Faster discoveries
  • Production optimization
  • Reduced risk and non-productive time
  • Data security
  • Data future proofing


Cloud computing brings the benefits of digital transformation and data analytics to your local network – as well as a network of remote internet-hosted servers – to store, manage, process and manipulate through an online interface.

Now you must make a choice between hybrid-cloud architectures and multi-cloud solutions. A hybrid-cloud computing environment uses a mix of on-premises, private cloud and third-party services, with orchestration between the two. By allowing workloads to move between them as computing needs and costs change, hybrid cloud gives you greater flexibility and more subsurface data deployment options.

A multi-cloud environment refers to the ability to leverage two or more cloud computing platforms but not necessarily requiring connectivity or orchestration between them.

Data Access

The digital future can mean instant access to any piece of data, but sometimes that’s easier said than done. Even if the technology is there, companies must contend with regulations, contractual laws and other obligations to government agencies regarding to data sharing.

Data scientists with immediate access to integrated data now make discoveries that were previously unattainable. Real time analytics enable real time adjustments that save money and prevent incidents. Access brings value.

AI and Automation

Discussed in the analyze stage, AI and automation also belongs here. The value of your transformation is realized through these technologies, which enable incremental efficiencies even in a “lower-for-longer” oil environment. Operators are now returning to legacy assets that have steadily produced, but not been visited for years. By applying AI and automation, they have created new value in old plays, contributing directly to the bottom line. They’ve also reduced risk and NPT by identifying production problems sooner. These technologies reinvent your old data, support small data teams and enable predictive analytics.

That concludes Katalyst’s 4 stages of Digital Transformation. This process is important for E&P companies that expect to be sustainable into the future. A McKinsey Report (CNBC 20154; 2015) says the oil and gas industry currently generates value from only one percent of all the data it creates. Companies that use more of their data will win.

Get more value out of your data. To get started, give Katalyst Data Management a call. We provide the only integrated, end-to-end data management solution and consulting services specifically designed to help companies embark on their digital transformation.

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