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Accelerate and Optimize Onshore Activity Planning with Cognite Data Fusion®

A leading independent energy company based in the U.S. faced challenges juggling planned tasks and unforeseen production constraints, leading to missed production targets and investor scrutiny. The onshore operator needed a better solution for obtaining data from systems like SAP, CMMS, data lakes, and time series historians. Using Cognite Data Fusion®, the operator developed a plan for a cloud-based collaboration tool to deliver AI-driven analytics.

Industry challenge

Limited accessibility to crucial data; hindered collaboration

Onshore operators face several challenges when attempting to accelerate and optimize onshore activity planning.

  • Gaining access to accurate and up-to-date data is often challenging, hindering the efficiency of decision-making processes
  • Aligning their diverse objectives and schedules of multiple stakeholders, including drilling teams, engineering personnel, and regulatory authorities, leads to delays and suboptimal resource utilization.
  • The dynamic nature of onshore environments requires operators to develop robust contingency plans to effectively and proactively mitigate potential risks and adapt to changing circumstances.
  • Strict regulations and permit requirements must be navigated meticulously, and environmentally responsible practices must be integrated into the planning process, adding complexity and time to the optimization efforts.
  • Inefficient communication and data-sharing practices can further slow down planning processes, leading to information gaps and errors in decision-making.

This particular operator was struggling to execute planned tasks among unforeseen production constraints, leading to missed production targets and investor scrutiny. They needed a more agile solution that could efficiently obtain data from systems such as  SAP, CMMS, data lakes, and time series historians to deliver effective change.

Recognizing  that AI-driven analytics and cloud-based collaboration tools are crucial to enhancing data accessibility and promoting real-time collaboration among teams, the operator turned to Cognite Data Fusion® to address their specific data challenges:

  • Manually managed schedule changes via Excel
  • Limited accessibility to crucial data resulting in inefficient collaboration
  • Inefficient means for planning of activities/resource allocation
  • Constrained resources limit the time available for monitoring well site data


Automated dashboards powered by contextualized data

The operator is using Cognite Data Fusion® to develop a series of automated dashboards powered by contextualized data that would enable real-time prioritization and operator response in the field to manage production constraints.  The solution contextualizes data from the applicable asset systems and automates the health and actions of the asset so the operations team would not have to do this manually. Datasets/systems include:

  • Equipment information from SAP - Location and equipment type
  • Exception Based Surveillance Signals (EBS)
  • Timeseries Data from PI
  • Operations Activities (work over activities)
  • Actions - Business logic to identify existing procedures for the asset
  • Weather Data - Temperature and Wind (avg/max)


Less time spent; more money saved

By integrating asset data, trends, and operational metrics from numerous sources into a single visual interface, planning time was significantly reduced across all assets and operators will be able to address their asset needs faster.

Simple access to data frees up valuable time to effectively plan and dynamically optimize schedules, leading to increased efficiency and improved operational performance. This can result in real time management of production constraints which enables the company to set realistic production targets for their investors. It is estimated that, for 40 Reliability Operators + 5 Production Engineers planning work on >1000 onshore wells, an onshore operator could expect:

  • $500k/yr FTE savings
  • 20% less time spent planning

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