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Installing an electrical submersible pump can be a way to increase the production in a well. This report will explain some ways the electrical submersible pump can be installed and describe the different components in an electrical submersible pump. The report will also give an insight in how electrical submersible pumps have been used and improvements done to the electrical submersible pump on two different fields. The main part will describe a method to screen wells and find out if installing an electrical submersible pump will be profitable. The method is to estimate future oil production, based on real-time production, with and without an ESP installed, and calculate the net present value. The method takes into account costs of installing, running and removing an electrical submersible pump. The ESP screening tool is screening the wells automatically. In addition it does an ESP simulation to find applicable pumps. For the ten wells screened by the ESP screening tool, the net present values have been negative, due to weak water cut estimation.

Drilling in deep water is requiring more advanced technology as fields at greater depths are being discovered. Managed Pressure Drilling and Dual Gradient Drilling are both offering different techniques for navigating through the narrow pore pressure and fracture gradient window during an operation. Along with different drilling and development systems they are introduced as possible solutions to many of the challenges associated with deepwater drilling. The industry is looking into the possibility of doing highly deviated extended reach wells in deep water environments. Before doing so different simulations are done to investigate which factors will limit the maximum well trajectory and to figure out of far it is theoretically possible to drill in horizontal and vertical direction. With the help of the WELLPLANTM software a reservoir located in the Gulf of Mexico is chosen as a well candidate to run simulations on. Case study shows that for both directional extensions buckling of the drillpipe is what keeps us from drilling further. In terms of torque and pump capacity both rig candidates used for the study are well within their maximum capacities. Equivalent circulating density (ECD) would have been the main problem for the case study, but can easily be compensated for assuming we have the potential to control the pressure profile. With conventional drilling we would not be able to handle problems associated with ECD, meaning that DGD or other methods are required. From the sensitivity study we learn the importance of having access to accurate wellbore data, as a reduction in friction factor has the potential to extend the well trajectory even further and a potential dogleg severity would make us unable to reach target depth.

Every year kick incidents occur, maybe best remembered by the Macondo blowout in April 2010 resulting in devastating oil spills throughout the Gulf of Mexico. Well control is one of the most important factors in any drilling operation, preventing disastrous blowouts where people and the environment will be affected. The development of new technologies has increased significantly, lowering the risks of blowouts, mostly because of the reliability of blowout preventers. Better hardware systems have been developed and better materials has increased the performance during critical parts of an operation. There are several causes why we encounter kicks; not keeping the hole full, lost circulation, swabbing, underbalanced pressures, trapped fluids/pressures and mechanical failures. Before an actual kick, there are warning signs that might occur and knowing how to interpret positive indicators of kick is very important. Pit gain, increase in return flow rate and abnormalities in drillpipe pressure are all signs that formation fluid has entered the well. When experiencing a kick, procedures to reduce the danger and the non productive time have to be started. Firstly the well has to be shut in by either the hard shut-in method or the soft shut-in method. Then the influx has to be circulated out of the well by the use of either the Driller’s Method or the Wait and Weight Method. To better understand and visualize the behavior of formation fluid entering the well, the simulation program Drillbench Kick has been used. The soft shut-in has been compared against the hard shut-in and the Driller’s Method has been run against the Wait & Weight Method. The simulations have been performed with both oil based mud and water based mud.

The system studied in this thesis is called the Low Rise Return system and uses a partly filled marine drilling riser with a variable mud level which is used control the bottom holes pressure.Initially main components of the Low Riser Return System are listed and explained. Then the performance characteristics of the system are explored. Level movements in riser during level increase and decrease at constant mud pump rates are explained along with the effect of mud pump rate on maximum level increase and decrease rates.A simple simulator is then presented that calculates the bottom hole pressure when pump rates are changed. The simulator includes a function that enables it to simulate lost circulation scenarios.The simulator is used to simulate some preferred scenarios. First a pressure increase and decrease at constant mud pump rates are simulated. Then it is shown how a faster pressure decrease can be achieved by temporarily lowering the mud pump rate. Next simulations are shown where changes in mud level are used to compensate for changes in equivalent circulation density as mud pump rates are changed. Finally simulations are run that demonstrate how mud level can be reduced to cure lost circulation scenarios. Results and lessons learned are then discussed.

Pipelines with multiphase flow will exhibit large and highly nonlinear liquid rates during transients caused by changes in production rate. This requires either a large separator (slug catcher) downstream, capable of handling all disturbances, or, some sort of control of the rate change in order to ensure that the downstream processing equipment can cope with the disturbances. In this report, it is proposed to use smart liquid control to optimally control the production during rate changes. The term smart liquid control was coined to describe the use of nonlinear model predictive control (NMPC) and a nonlinear pipeline model to control liquid levels. The pipeline from the Snøhvit field to the Hammerfest LNG plant was used as a test case on which to implement smart liquid control. The technique was proved using a simplified OLGA pipeline model for both predictions and as process, i.e. perfect model. Good results were shown, especially when compared to manual control. Smart liquid control therefore presents a possibility to reduce separator size for new projects. Another highly interesting use of smart liquid control is to optimize the use of separator buffer capacity in order to maximize production. The NMPC method used in this report, a single shooting multistep quasi-Newton method, was elaborated on in a literature review chapter. Also an implementation of the method was made and described in the context of NMPC. The implementation of the algorithm was made in the in-house STATOIL MPC-tool, SEPTIC. In order to assess the performance of the algorithm, it was tested on a small continuously stirred tank reactor system and the results reported and discussed.

Recent years advances within well deployment and instrumentation technology offers huge potentials for increased oil recovery from reservoir production. Wells can now be equipped with controllable valves at reservoir depth, which may possibly alter the production profitability of the field completely, if the devices are used in an intelligent manner. This thesis investigates this potential by using model predictive control to maximize reservoir production performance and total oil production. The report describes an algorithm for nonlinear model predictive control, using a single shooting, multistep, quasi-Newton method, and implements it on an existing industrial MPC platform - Statoil's in-house MPC tool SEPTIC. The method is an iterative method, solving a series of quadratic problems analogous to sequential quadratic programming, to find the optimal control settings. An interface between SEPTIC and a commercial reservoir simulator, ECLIPSE, is developed for process modelling and predictions. ECLIPSE provides highly realistic and detailed reservoir behaviour and is used by SEPTIC to obtain numerical gradients for optimization. The method is applied to two reservoir examples, Case 1 and Case 2, and develops optimal control strategies for each of these. The two examples have conceptually different model structures. Case 1 is a simple introduction model. Case 2 is a benchmark model previously used in Yeten, Durlofsky and Aziz (2002) and models a North Sea type channelized reservoir. It is described by a set of different realizations, to capture a notion of model uncertainty. The report addresses each of the available realizations and shows how the value of an optimal production strategy can vary for equally probable realizations. Improvements in reservoir production performance using the model predictive control method are shown for all cases, compared to basic controlled references cases. For the benchmark example improvements range up to as much as 68% increase in one realization, and 30% on average for all realizations. This is an increase from the results previously published for the benchmark, with a 3% average. However, the level of improvement shows significant variation, and is only marginal for example Case 1. A thorough field analysis should therefore be performed before deciding to take the extra cost of well equipment and optimal control.

In reservoir management it is important with reservoir models that have good predictive abilities. Since the models initially are based on measurements with high uncertainties it is important to utilize new available data. Ensemble Kalman Filter (EnKF) is a new method for history matching that has received a lot of attention the last couple of years. This method is sequential and continuously update the reservoir model states (saturations, pressures etc.) and parameters (permeabilities, porosities etc) as data become available. The EnKF algorithm is derived and presented with a different notation, similar to that of the Kalman Filter (KF) used in control engineering. This algorithm is also verified on a simple linear example to illustrate that the covariance of the EnKF approaches that of the linear KF in case of an infinite ensemble size. In control theory this method falls under the category of parameter and state estimation of nonlinear large scale systems. Interesting aspects as observability and constraint handling arises, and these are linked to the EnKF and the reservoir case. To determine if the total problem is observable is a nearly impossible task, but one can learn a lot from introducing this concept. The EnKF algorithm was implemented on a simple shoe box reservoir model and four different problem initializations were tested. Although decent results were achieved from some of the simulations other failed completely. Some strange development in the ensemble when little information is available in the measurements was experienced and discussed. An outline was presented for a reservoir management scheme where EnKF is combined with Model Predictive Control (MPC). Some challenges was pointed out and these involve computation time, predictive ability, closed-loop behavior etc.