We offer several AI applications for upstream activities in the oil & gas industry.

Upstream Strategy

Visualizing and optimizing exploration and production strategies from discovery to production, we can build integrated analytics systems that will take account of all parameters and advise on optimum design and operational characteristics.

Drilling & Optimization

Recognizing an opportunity to reduce drilling time in the presence of large volumes of historical streaming data is difficult. Integrating machine learning algorithms and big data visualization tools enables the engineers to determine the optimal landing zone, rpm and Weight On Bit (WOB) thus achieving a higher Rate Of Penetration (ROP) while maintaining steerability.

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Completions Optimization

The value of an unconventional asset is determined by the number of drilling locations, spacing, stacking and their individual performances. Machine learning and modern optimization algorithms will maximize ROI by recommending well completions designs and spacing. After identifying production drivers, our intelligent design of experimentation process recommends how to proceed and learn through fewer experiments. Learning faster, smarter, we improve the economics of a larger set of wells.

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Automatic Field Ticket Processing

Drilling and well completions costs vary over time. To accelerate the capture of current cost data a machine learning assisted mobile app can be developed to process field tickets and update current spend closer to real-time. This new cost data is then injected into forecasting tools to guide expected spend through to year-end.

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Cost Tracking and Forecasting

Operators face increasing pressure to maintain capital spend within guidance. Machine learning systems can interrogate historic spend, scheduling and operations data to forecast expected expenditures. By providing accurate visualizations of expected and actual costs, planning teams have the flexibility to scale operations up or down in response to available capital, thereby avoiding shutting down operations too soon or not investing all available capital.

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Monitoring for Anomaly detection

Our engineers perform anomaly detection using in-house trained predictive models to provide early warning alerts and diagnostic guidance to our customers. In a complex industry setting such as power plants, aviation etc., anomaly detection is critical to raise alarms beforehand to prevent significant damages and mishaps. Here, we propose a novel approach of using a multimodal neural network-based autoencoder. These detected anomalies generally tend to be more accurate and robust than anomalies detected by a single data source as latent representations capture inter-dependencies between different parts of the system through those multiple data sources.

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Data & Compute

BKO defines and designs enterprise cloud based solutions for advanced analytics applications. Real-time and financial data stored in your database can support operational and investment decision making at all levels.

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Case Study

Hydrocarbon Prospecting with Machine Learning


An oil & gas exploration and production (E&P) company, wanted to maximize profit from investments in unconventional onshore basins.

They were primarily concerned with optimal utilization of unconventional reservoirs, those where the oil & gas is tightly bound to the rock fabric through strong capillary forces.


We developed and deployed a machine learning model that predicts the extractable hydrocarbon content at a reservoir. The model relied on subsurface, engineering, and well performance data. This enabled the client to reliably iterate over well economics and optimize well completions, therefore improving development and aiding commercial valuation.Aliquam vestibulum morbi blandit cursus risus atomol ultrices proin gravida.


This machine learning model serves three principal purposes:

  • Quantification of any interaction between the reservoir and well completions
  • Guidance to optimize new well completions, probabilistically
  • Estimation of productivity away from known production, with optimal well completions design showing P10, P50 and P90 scenarios.

Our client is now able to ask relevant questions which our model can immediately answer:

  • If well completions were held constant, what would my expected production be?
  • What can I expect if I pumped a 10% larger job?
  • What uplift should I expect?
  • Is the uplift the same everywhere?