Today, the effectiveness of real-time adjustments to drilling parameters may be hindered by uncertain surface and downhole measurements and the inability of humans to aggregate multiple streams of data in real-time. A recently introduced drilling optimization system addresses these issues by incorporating a Bayesian network into operators’ drilling rig data aggregation systems. This Bayesian network-enhanced…

To optimize the value of surface and downhole drilling data, an operator, a software company, and a service company have combined forces to develop a system to seamlessly integrate all forms of data, file types, and communication protocols. To ensure that personnel on the rig and in the office are able and willing to act…

The human component figures prominently in Apache Corporation’s well-documented drive toward developing an automatic rig-based drilling advisory system. The foundation has been laid with the installation of data aggregation boxes on 21 Apache rigs, which integrate seamlessly with the rig’s electronic drilling recorder to run physics-based models built around a basic Bayesian probabilistic network. The…

Today, drilling data are marred by a lack of quality assurance, which can result in costly consequences from poor analytics and real-time decisions made using faulty data. A new system boosts drilling efficiency through the validation of real-time or historical data, which enables operators to significantly reduce both non-productive time and invisible lost time.