The University of Texas at Austin has been working on advanced sensor technologies over the last 6 years in a bid to improve the safety and efficiency of drilling operations, and to reduce uncertainty with regards to data and situational awareness.

This online training course will take an introductory look at the opportunities, challenges and specific requirements for petroleum data analytics for the energy industry.

​Real-time data now represents a growing stream of scores of channels of digital data being fed concurrently to numerous receiving entities in the operator's remote monitoring amenities, at contractor centers and other facilities. Analytics are applied to data channels to signal or predict deviations from expected readings that require attention. For such systems to work effectively and reliably the input data must be trustworthy.

A ROP optimization methodology is presented, using offset drilled wells data. Data is used to train a neural network model for different rock properties which is presented in the vertical section of the reservoir. By changing the drilling parameters like Weight on Bit, Rate of Penetration (ROP) for the target well is optimized.

This presentation describes the development of a well-site automation system consisting of an open data aggregator, with networked surface and downhole sensors and real-time applications for process monitoring, advice and control.

Analytics is not new to the oil & gas industry. From the early days of seismic acquisition and processing, well log interpretation and reservoir simulation, industry experts have used various techniques up to the limit of existing computer power, to analyze and model data recorded in the field to make better decisions.

Data volume is exploding — more than 90% of today’s data was created in the last few years, and there is an exponential increase in new types of data, with mobile, social media, video, and the Industrial Internet of Things adding to the growth of seismic, reservoir, drilling, production, and engineering data.

Presented by Dr. Luigi Saputelli

Presented by Jorge Landa

This presentation will strive to give an overview of the key components to build a fully automated system to identify rig states and provide rig operations performance key performance indicators (KPIs). Starting with the data requirement, feature engineering, machine learning, and how the sensor’s noise can be reduced in real-time. It will also provide information on drilling operation’s specific challenges (such as KPI validation) and how it can be addressed. Finally, we will discuss lessons learned of running such a system, and how such an effort can establish the foundation of advanced data science driven solutions.

This webinar explains how deviated wellbores are drilled automatically with a rotary steerable system. The automation system used is capable of predicting the wellbore trajectory, deriving steering proposals to follow the planned trajectory closely or steer back to plan, and submitting those steering downlinks automatically to the downhole RSS with the directional driller observing the system. During the webinar, we explain the underlying control methodology and the automation system architecture. Furthermore, operational considerations as well as sensor and actuator influencing factors are discussed. The concept of embedding the driller and directional driller in the decision-making workflow is outlined. Finally, the performance of the automated trajectory drilling system is assessed and case studies are presented, where up to 98 % of a single wellbore has been drilled automatically.

Automated Well Control is a new technology set to revolutionize secondary well control procedures. This webinar introduces the technology, details the reasons why the technology is required, describes how it works, and demonstrates the methodology used.

With the implementation of new technology on deep-water units, there have been huge strides made towards a safer and more efficient way of drilling wells. However, with the introduction of these advancements different challenges also arise. In fact, as new technologies are introduced, the more important team skills, or Crew Resource Management becomes.

Bridging The Gap Between Material Balance And Reservoir Simulation For History Matching And Probabilistic Forecasting Using Machine Learning

The purpose of this seminar is to illustrate the effectiveness of Computational Fluid Dynamics (CFD) in guiding re-design of a scrubber inlet within the constraints of the project team.