OAG Analytics demystifies the subsurface and improves the

predictability of field development planning.


Add OAG to Your Well Planning Workflows

Proprietary Reservoir Depletion and Well Interference Algorithms

  • Data

  • QC

  • Change Text

  • AutoML

  • Simulation

  • Visualization

  • Decision

Planning New Drilling Units

Planning a new drilling unit requires evaluating how localized geology and geophysics interact with well designs. Machine learning models trained with geo, well, and production data are faster than type curves for estimating the production of wells that are not within hundreds of feet of other wells. Unfortunately, drilling wells far enough apart to isolate them from the effects of depletion and interference does not maximize drilling unit economics. 


Oil, gas, and water production in a new drilling unit is a complex function of geo properties, well designs, well spacing, and drill timing. Modeling well spacing and timing requires combining oil & gas-specific algorithms with machine learning to create models that understand enough about reservoir depletion and well interference to improve the predictability of complex full-stack development.

OAG’s Well Planning software learns new insights from rock, completion, location, and production data to improve:


  • Well spacing and sequencing 

  • Stage and cluster spacing

  • Proppant and fluid loading

  • Offset production volumes

Planning Infill Drilling Units

Adding more wells to drilling units that already have wells, i.e. infill, adds additional complexity, as the impact that existing wells (parents) have on new wells (children) changes as a function of time and space between wells.

OAG’s proprietary well interference algorithms combine with machine learning to train models that improve the predictability of frac hits between different parent/child well pairs. OAG also provides web-based software for SMEs to update frac hit labels, accelerating learning by facilitating SME-computer collaboration. The OAG Frac Hit UI displays data like daily production, high frequency SCADA production, pressure, frac truck data, well locations, geo data, and type curve areas.

OAG's Infill Drilling Unit solutions improve the predictability of:​

  • Frac hits

  • Offset production impact of frac hits


Add OAG to Your Subsurface Workflows

Computational Geometry to Extract New Signals from Your Maps

  • Data

  • QC

  • Subsurface Algorithms

  • AutoML

  • Simulation

  • Visualization

  • Decision

Demystify the Subsurface

Geoscience is uniquely poised to benefit from machine learning. Much of the industry’s “big data” is captured in seismic and well logs, and some level of interpretation or qualitative evaluation is usually necessary. Machine learning evaluates large data sets and helps geoscientists pair their qualitative interpretations with automated and quantitative workflows to better assess risk.


Through automation and quantification, operators are improving consistency; reducing interpretation time; and providing higher fidelity geologic and geophysical inputs to critical cross-disciplinary operations such as well spacing, completions optimization, and landing zone optimization.

  • Improve existing maps

  • Automate basin-wide mapping

  • Predict core properties from logs

  • Log normalization

  • Identify facies from logs

  • Missing log prediction


It All Begins With the Data

Make Managing Data Easier

OAG Data Manager

During upstream operations, millions of data points are generated and stored in an array of database instances that are disconnected and difficult to access. Data Manager simplifies the process of preparing and managing your complex data portfolio and enables self-service access for your entire team.

  • Organize all data types
  • Combine public, subscription, and proprietary data
  • Normalize data stored in multiple formats
  • Create a data set in minutes using a point and click interface
  • Seamlessly integrate with data visualization tools like TIBCO Spotfire

80% of U.S. oil & gas companies companies reported using AI and machine learning to Hart Energy in 2019. Most of these companies are having trouble improving well spacing, particularly combining physics with machine learning to create models that understand reservoir depletion and well interference.

Ready to improve your field development plans?

  • LinkedIn - White Circle
  • Twitter - White Circle

(844) 624-9355

1330 Post Oak Blvd, Suite 1825

Houston TX 77056

© 2020 OAG Analytics All rights reserved