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Analytics Explorer

Helping Exploration and Production (E&P) companies create business value through dynamic data science and analytics

Analytics Explorer is <span/>designed to help companies involved in exploration, development, and production activities leverage data science and analytics to help drive their business strategies and make investment decisions based on predictive outcomes and probabilities. It is particularly well-suited for organizations managing a large portfolio of wells, assets, and reservoirs who are actively investing in digital transformation initiatives and have a strong desire to get the most out of their data investment to improve operational efficiency.

Analytics Explorer provides a workspace for data scientists, engineers, geoscientists and strategic planners to rapidly integrate, visualize, validate, and optimize large amounts of disparate data. It offers focused analysis to collaborate and improve predictions with templates and guided workflows for:

  • Data imputation
  • Facies analysis, integrating log and seismic data
  • Production prediction, providing transparency in the results with clear error metrics and machine learning explicability tools
  • Completion optimization
  • Well spacing computes
  • Property predictions from seismic and log and/or engineering data

What's Included


Working together with strategic partners, TIBCO® and The University of Texas, our internal Upstream Energy Data Science team has tested and validated existing machine learning algorithms using the vast IHS Markit data to solve common E&P challenges, the team has also created proprietary algorithms, workflows and data models to ensure your asset team spends more time understanding the outcomes of machine learning rather than implementing, building, and training AI technology. With thousands of manhours invested in pairing domain experts with data scientists, Analytics Explorer was purpose-built to fast track big data analysis while incorporating robust visualization tools so your teams can see the data in the way that resonates most with their investigation.

Benefits include:

  • Next-level collaboration with a cross-disciplinary dataset allowing for reduced uncertainty and risk
  • Improved productivity with dynamic data connections and automated workflows
  • Optimized analysis by applying algorithms from leading-edge data science research
  • Enhanced value of data by combining IHS Markit data solutions and insights with your proprietary data


Figure 1. Analytics Explorer dashboard, powered by TIBCO Spotfire®, visualization results from Principal Component Analysis and Self-Organizing Map techniques applied to seismic attributes workflows for facies delineation, resulting in more accurate interpretations.

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Figure 2. 3D view of wellbores colored by formation zone penetrated by horizontal wells. This visualization is made possible through the integration between KingdomTM Geoscience and Analytics Explorer percentage in zone computes through proprietary algorithms.

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Figure 3. KingdomTM Geoscience map view of enhanced geological features in the North Sea basin, resulting from Analytics Explorer machine learning techniques applied to seismic data.

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Figure 4. Analytics Explorer dashboard, powered by TIBCO Spotfire®, visualization results using multi-variate techniques for identification of multiple lithology types and fluid variations across a reservoir.

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Data Science vs. Data Analytics
Data Analytics is the visualization of data results, while Data Science is the extraction and prediction of knowledge and information from data. Companies rely on data to make predictions and understand risk. In a volatile marketplace, there are significant pressures to transform data into decisions quickly. It is important to know and trust the data that is driving your decisions. Identifying and correcting bad data before you begin your analysis is critical to avoiding poor decisions made on bad or biased data.
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