International audienceWe propose a simple model of two-phase gas–liquid flow by imposing a quasi-equilibrium on the mix- ture momentum balance of the classical transient drift-flux model. This reduces the model to a single hyperbolic PDE, describing the void wave, coupled with two static relations giving the void wave velocity from the now static momentum balance. Exploiting this, the new model uses a single distributed state, the void fraction, and with a suggested approximation of the two remaining static relations, all closure re- lations are given explicitly in, or as quadrature of functions of, the void fraction and exogenous variables. This makes model implementation, simulation and analysis very fast, simple and robust. Consequently, the proposed model is well-suited for model-based control and estimation applications concerning two- phase gas–liquid flow
Real-time estimation of annular pressure profile and formation pressure is crucial for the execution and planning of a well control operation, especially when drilling formations with narrow pore and fracture pressure margins. A simple transient multi-phase simulator, capable of accurately representing gas and liquid dynamics while minimizing complexity and computational requirements, is highly desirable for real-time kick mitigation and control applications. Such a simulator is presented here in the form of a coupled ODE-PDE model composed of a first order ODE and a first order hyperbolic PDE. This model is shown to retain the dominating two-phase dynamics encountered during gas kick incidents. As a particular application, we demonstrate the use of the model in design of switched control algorithms for kick handling in a Managed Pressure Drilling setting. A Recursive Least Squares algorithm is employed for estimation of unknown model parameters.
The safe and efficient construction of a well requires close attention to how the well and the rig equipment behave as the well is drilled. This primarily involves monitoring and correctly interpreting all the data collected at the rig site. In the past, this was primarily accomplished with an experienced drilling crew. While we still continue to use mostly antiquated rig sensors that were introduced decades ago, there has also been an "explosion" in the number of new advanced sensors used on a rig. The argument put forward for the additional sensors is that they increase safety and help reduce non-productive time and invisible lost time. Many of these new sensors provide data at high(er) sampling rates. This has made human monitoring of the data very difficult, if not impossible, and has led to a variety of event detection algorithms. The primary function of these event detection algorithms is to bring the driller's attention to anomalies in the system, based on data collected from all the rig sensors. However, the frequent occurrences of false and missed alarms, when using automated event detection software, can lead to significant downtime, and also reduce safety at the rig. One reason for these false alarms is bad data due to either sensor failure or data communication failure. Having sophisticated sensors, but not knowing whether the data can be trusted, defeats the very purpose of having additional sensors. This paper explores a methodology that can greatly reduce these false and missed alarms by validating the data coming from the sensors. The data validation methodology described in this paper utilizes a Bayesian network model of the sensed parameters, to maximize the number of analytical redundancies among the parameters. Also, data is collected and aggregated from multiple sources within and outside the rig, such as the well plan, morning reports and real-time data, to obtain increased certainty in the prediction of sensor / process anomalies. The algorithm allows for the detection and isolation of sensor faults and process faults, and does not impose unrealistic assumptions of process invariance. For any type of drilling operation, the parameters in a drilling model are constantly changing. Here, we briefly discuss how the model can be updated in real-time, depending on whether the fault is in the sensor or in the process. The approach is very flexible and easily accommodates the widely varying number of sensors from rig to rig. The methodology can also be easily scaled to be applied on a small subsystem (top drive, hydraulic unit, etc.) or the entire rig (all surface and sub-surface sensors). The proposed methodology is applied on sample scenarios that are typical of the unconventional shale drilling operations conducted in North America.
Multiple literature studies have indicated that a significant amount of data collected during drilling operations is unreliable. To move towards better data quality, two critical hurdles need to be overcome. First, the case for the value of good data needs to be made, so that resources can be allocated towards improving data quality. Second, a process needs to be established within the operator company to measure and improve the quality of data. This paper is a case study in addressing these challenges. In this work, we focus on eight core surface sensor measurements essential to drilling operations (block position, hook load, rotary speed, rotary torque, pump strokes per minute, flow rate out, standpipe pressure and pit volume) and attempt to assess/improve their quality. The first step involves identifying how much of each measured data deviates from their accepted values. This is most economically accomplished using automated data validation software. Once the root cause is identified, steps can be taken to rectify the problem. Four rigs in North America were identified for this trial conducted over a six-month period. The goal is to establish a data quality improvement loop that continually accesses data, identifies issues, and implements corrective actions. This paper explains this process and how it has been applied to improve the quality of drilling data.
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