Exebenus Current ML

Making informed decisions requires clear, moment-to-moment understanding of the situation. Exebenus Current ML provides real-time predictive situational awareness aimed at avoiding or reducing costly operational downtime and lost time.

During planning, our machine learning agents can be used for offset well analysis to optimize drilling parameters ranges for optimal drilling speed as well as evaluate risks and prepare ready-for-action contingency plans. This increases operational efficiency, reduces potential downtime and helps eliminate the hesitations that can add up to invisible lost time.

During execution, the Real-time ROP Optimization agent advices, identifies combination and changes to parameters that may not be obvious to a person based on traditional routines. Real-time advice that has proven in field operations to increase ROP and reduce drilling time significantly.

In operation, the Stuck pipe agents – Differential Sticking, Mechanical Sticking and Hole Cleaning – use real-time data to recognize deteriorating conditions that may lead to stuck pipe and other issues. This gives your crew a heads-up 15 minutes to four hours ahead of potential events, prompting them to implement mitigation steps and avoid downtime.

  • With Exebenus Current ML, insight propels productivity.
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Exceptional benefits
  • No need for training on offset data
  • Vendor-neutral plug & play solution 
  • Self-adaptive to any lithology, bit type, BHA or mud properties
  • Minimal configuration time to get started
  • Easily scalable to any # of wells

The rate of penetration (ROP) is a major contributor to drilling time and costs. Today, optimized ROP is achieved by adjusting the weight on bit (WOB), RPM and fluid flow, the allowed ranges typically obtained using time consuming and complex simulation models. In addition uncontrollable factors such as bit dulling, buckling, vibration and formation strength influence the ROP.

Exebenus Current ML™ Real-time ROP Optimization machine learning agent is unique in its ability to decipher the relationship between the controllable and uncontrollable drilling parameter relationships.

The agent provide reliable and consistent advice, identifies combination and changes to drilling parameters – weigh on bit (WOB), RPM and fluid flow – that may not be obvious to the naked eye based on traditional routines.

The agent’s advice is proven in field operations to increase ROP and reduce drilling time significantly.

 

Exceptional benefits

  • Multiparameter recommendations – RPM, WOB, fluid flow
  • Usable anywhere; no customization required
  • Consistent and reliable
  • Reduce risk of human error

Exebenus Current ML Stuck Pipe agents are designed to predict, in real time, high-risk conditions related to pressure differentials, hole cleaning conditions and wellbore geometry—conditions that, without intervention, typically result in costly stuck pipe situations. Warnings are provided 30 minutes to hours prior to potential events, giving rig crews sufficient time to take mitigating actions.

When used on historical real-time data as part of offset well analysis, the agents can identify unreported near misses and provide guidance for optimizing performance in the future.

 

Exceptional benefits

  • Predict hazardous events
  • Optimize offset well and root cause analysis
  • Provide real-time situational awareness

At Exebenus, we have chosen to develop targeted machine learning models rather than complex models. Why this approach?

Complex models consume vast amounts of data, and take longer to set up, train and run. In contrast, our targeted models solve well-defined problems and deliver more accurate predictions. They use data that’s always available in real time on the rig, which means our agents can be used anytime, anywhere, easily.

Our generalized “out of the box” models are adaptive enough to be used in any geographic area. They provide risk awareness and ROP optimization opportunities in various well operations.

Our robust stuck pipe agents return useful predictions based on a range of data quality. In fact, they do their best work when consuming raw, unfiltered data, and even handle data gaps.

Exebenus Current ML Stuck Pipe agents provide reliable risk predictions within a useable timeframe, requiring only a connection to the existing WITSML system.

Our plug & play agents required no configuration and are easy to adopt and use in any well operation. The Exebenus Current ML Real-Time ROP Optimization agent provides easy to interpret advice and recommendations on RPM, weight on bit and fluid flow to optimize ROP.

Exebenus Current ML agents consume real-time or historical WITSML data that is readily available in all well operations. Minimal human intervention and no data filtering or cleaning required.

In trials, our agents have performed “out of the box” using raw data that was not prepared in any way.

For ease and speed, the agents output WITSML data to integrate with your operation center’s workflows and familiar real-time WITSML viewers. Within only a few days, your teams can be monitoring and analyzing data, and advising rig crews.

Exebenus Current ML is cloud-based, and agents can be deployed stand alone or as a package.

The Exebenus services team works to ensure that your company’s workflows are properly integrated and digitalized when deploying and implementing the Exebenus Current ML solution. Exebenus Service

“We’re seeing machine learning coming into the real-time operations space primarily to do two things: to predict and to optimize. Often it’s about detecting anomalies early and avoiding hazards… uses where machine learning is improving safety and reducing operating costs.”

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