eDrilling is a new and innovative system for real time drilling simulation, 3D visualization and control from a remote drilling expert centre. The concept uses all available real time drilling data (surface and downhole) in combination with real time modelling to monitor and optimize the drilling process. This information is used to visualize the wellbore in 3D in real time. eDrilling has been implemented in an Onshore Drilling Center in Norway. The system is composed of the following elements, some of which are unique and ground-breaking:An advanced and fast Integrated Drilling Simulator which is capable to model the different drilling sub-processes dynamically, and also the interaction between these sub-processes in real time. The Integrated Drilling Simulator is used for automatic forward-looking during drilling, and can be used for what-if evaluations as well.Automatic quality check and corrections of drilling data; making them suitable for processing by computer modelsReal time supervision methodology for the drilling process using time based drilling data as well as drilling models / the integrated drilling simulatorMethodology for diagnosis of the drilling state and conditions. This is obtained from comparing model predictions with measured data.Advisory technology for more optimal drilling.A Virtual Wellbore, with advanced visualization of the downhole process. A new generation visualization system designed to integrate all participants involved, will enable enhanced collaboration of all drilling and well activities in a global environment.Data flow and computer infrastructure eDrilling (Ref. 1)has been implemented in an Onshore Drilling Center on Ekofisk in Norway. The system has been used on several drilling operations. Experiences from its use will be summarized and presented; both related to technical and work process issues. The supervision and diagnosis functionalities have been useful in particular. The system has given very early warnings on ECD and friction related problems. This paper will present the eDrilling system used on a specific Ekofisk wells with focus on experiences from its use. Introduction The southwestern part of the Norwegian continental shelf, called the Ekofisk Area, contains eleven major chalk fields. The Ekofisk field is the first and main discovery, discovered in 1969 and put on production in 1972. The fractured chalk reservoir lies at a depth of 9500 - 10700 feet and is approximately 11.2 × 5.4 kilometers in area, with production coming from two zones Ekofisk and Tor. It is one of the North Sea Giants with a STOIIP of 7 mmbo! Currently there are 4 fields in production, 4 fields abandoned with current production around 325,000 bbls per day of oil and 350 scf of gas per day. Water injection is currently used to maintain reservoir pressure, and approximately 900,000 bbls of water are injected each day.

Inadequate hole cleaning during a drilling operation may result in immediate problems such as excessive torque or pack-off situations, or it can lead to delayed problems such as while running in hole with a casing/liner or a completion string in a leftover cuttings bed. It is therefore desirable to provide quantitative information about hole cleaning conditions: at the planning stage, while drilling or when investigating incidents. Because the ability to transport cuttings particles varies with their position in the borehole and the current drilling parameters, hole cleaning modelling is a history-dependent problem. The precise modelling of the movement of solid particles during a drilling operation gives the possibility to estimate whether they are in suspension in the drilling fluid, settling on the low side or being eroded from a cuttings bed. These estimations may be confirmed through the change of the active volume or by the increase of downhole pressure, when PWD (Pressure While Drilling) measurements are available. A transient cuttings transport model has been used for planning and monitoring an ERD (Extended Reach Drilling) well drilled in the North Sea. The model has been used to compare the expected performance of different mud systems on both hydraulic and mechanical limits that could be expected prior to drilling. For another challenging drilling operation, the model has been used to post analyze the sequence of actions that led to problems while running in hole with a liner. Here also, the observations tended to confirm the predictions made by the hole cleaning model. Furthermore, an active use of such an advanced hole cleaning model may help determine the time required for circulation procedures prior to pull out of hole. This can help reduce the flat time associated with circulation procedures and at the same time decrease the duration by which the hole stays open thus reducing the risk of hole instability.

Objectives/Scope The world's first deployment of an automated drilling control system on a Statoil rig in the North Sea helped the rig in saving up to 10% rig time per well through safeguarding and optimizing manual operations and through automating repetitive drilling activities such as tripping, pipe filling, connections and pump start up. Advanced modelling of well conditions, combined with closed loop control of the drilling control system provided safeguards for pressure, rotary and hoisting velocity. Methods, Procedures, Process The drilling instrumentation, surface- and downhole sensors are coupled with robust real-time and fully transient hydraulic, mechanical and thermodynamic models that continuously evaluate the current downhole conditions. These models determine all possible combinations of drillers' actions (string accelerations, velocities, rotation, pump start-ups and flow rates) that will cause the dynamic downhole pressure to reach or exceed upper and lower well stability- and geo-pressure prognosis. These results are actively used to safeguard both manual and automated sequences. For example should the driller attempt to pull the drill string at a velocity that would cause the downhole pressure to fall below the Pore Pressure or Collapse Pressure at any depth in the open hole section, the drilling control system will intervene and limit the upward velocity to a safe value based on the dynamic model. Results, Observations, Conclusions The models effectively calculated and communicated current limits to the drilling control system, allowing the control system to safeguard the well against human error during manual operations and to automate various repetitive operations. Statistics after 3 wells proved an overall time saving of 4% per well through automated repetitive sequences (such as pump start-ups and friction tests) while another 2–8% time savings per well were realized through optimized manual operations (active safeguards and safety triggers) and other improvement initiatives by the rig. Although the system was originally developed to eliminate human errors and avoid major incidents (including technical side-tracks), the daily efficiency gains indicate that the system also avoids minor issues that otherwise would have slowed down the operation without being seen as downtime or Invisible Lost Time. This indicates that the system works as intended and that the system should be able to avoid major incidents when the relevant conditions arise. Novel/Additive Information This paper demonstrates how automation reduces invisible lost time and allows drillers to focus on other activities while repetitive tasks are controlled by software. Furthermore, rig safety is significantly enhanced since the closed loop drilling control system prevents users from exceeding the dynamic limits calculated by the drilling control system.

The paper describes the underlying infrastructure of an advanced real time integrated drilling simulator that is under development. Focus will be on handling the complex data flow and meeting the requirements regarding simultaneity when designing such a compound system. A synthesis of multiple transient and steady state models for drilling sub-processes with links to 3D visualization software and measured data is being built on the technology described in this paper. Possible applications include: This paper describes several challenges when developing a compound system for data processing in networks. For example the system must: The paper discusses different approaches to the challenges presented and recommends solution strategies. The underlying infrastructure is a crucial part of an integrated drilling simulator. It is the core of a technology that allows better utilization of drilling data through easier data access, visualization and advanced modeling. Such a system is an important element in integrating operations and enabling better communication between distributed experts. Introduction Technology development has facilitated the availability of time-based drilling process data in the operator's main office or control center in real time. This has been realized by: This has also made it possible to utilize drilling data in a more efficient and intelligent way for supervision of the drilling conditions by means of real time simulations and automatic diagnosis and decision support. Other important factors are The non-productive time during drilling operations amounts to about 20–30% of the total time. Of this 50% is due to well problems including geological surprises, and 50% due to equipment failure. By using real time simulation combined with real time 3D visualization it is possible to reduce well problems (lost circulation, stuck pipe, well stability and well control problems etc.) significantly.

Hook load is a very important parameter used during drilling operations to control the weight on bit and to assess possible deteriorations of the downhole conditions such as poor hole cleaning or excessive tortuosity. However, the quality of the hook load measurement is often regarded as quite poor. This paper investigates the sources of uncertainties associated with the hook load measurement and proposes correction methods in order to obtain better data quality. In practice, the hook load is normally measured indirectly, either in the travelling equipment or as a tension in the dead-line. This apparent hook load is subject to load-generating forces between the measurement location and the top of the string, including the weight of the mud hose attached to the top drive, imperfect tension transmission across sheaves and gravitational and inertia forces associated with weight and rotation of the drill line, respectively. The load contribution from these forces can amount to several metric tons and this must be accounted for whenever the true hook load is to be derived. In this paper, a mathematical model describing the forces affecting the hook load measurement is developed. The model predicts true hook load as a function of block position, velocity and other conditions that can influence the measurement like the mud weight or whether the dolly is retracted or not. It is a generalization of the industry-standard hook load estimation technique and successfully replicates a number of trends observed in the field. Extensive model evaluation is performed by comparing model results to field measurements of simultaneous travelling equipment and dead-line tension derived hook loads. The model is also compared with data from a laboratory scale test rig equipped with distributed load cells connected to a data acquisition system. The hook load correction model works exclusively at the software level and represents a potentially cost-efficient alternative to a direct hook load sensor installed in the internal BOP of some top drives. Assessment of model uncertainty and its effect on hook load predictions is given particular attention as this ultimately determines whether the correction method can replace direct hook load measurements in practice.