Search

Drilling a well is a complex process which needs to be monitored continuously to ensure that the well will reach its goals. For this purpose mud logging is performed with sensors installed on the rig. However, the direct use of these data is not sufficient to know precisely the state of the drilling process. Processing and interpretation are needed. IFP and Geoservices have collaborated for many years in a research program dedicated to mud logging data interpretation. A system has been designed to inform the driller about the problems encountered while drilling. The focus of this paper is to present the system called GetSMART, which aims at the detection while drilling of the main abnormal vibrations and hydraulic malfunctions. The system is based on the diagnosis trees methodology, which allows one to take into account the empirical knowledge of the driller to analyze the signals coming from sensors or physical models and also to generate alarms. Some results obtained on field malfunctions real cases show the interest and the pertinence of the GetSMART system. In the future, others diagnosis trees (i.e. wellbore stability) will be introduced in this system

Improving the production forecast of a hydrocarbon resource using reservoir simulation models, allows us to have a better prediction of reserves. This is important in asset development due to uncertainties that can reduce the expectation ultimate recovery after development. The resulting optimized production forecast allows for a better reservoir management plan. This paper presents the methodology and reservoir tools to improve production forecast for fields under development. This methodology was applied in the case of HMS Field and the information obtained can be applied in optimizing the production forecast for other fields. In the case of HMS field, the estimation of the production forecast was initially given by a first reservoir model (RMV1) of the field. After the acquisition of new seismic data, the production forecast needed to be improved. The field studied is composed of 3 main reservoirs. Therefore, the improvement of the global production forecast has been made by generating up to date vertical lift performance (VLP) curves representative of the oil producers and water injectors based on the new data, and by connecting these VLPs on one hand to the 3 individual reservoir models, on the other hand to the new version of the global reservoir model (RMV2). Moreover, VLPs for the network (from the Well heads to the FPSO) have been built in order to test their impact on the merged model (RMV2). The results show that for the individual reservoirs and merged model, there is marginal improvement in the reserves. Sensitivity analysis and pressure gradient comparison for the wells were also investigated to manage uncertainties in the study. The results obtained were benchmarked against RMV1. The integrated approach used in the study across the Reservoir Engineering, Drilling Engineering, Completions and Geosciences disciplines allowed for seamless delivery.

Transient multiphase flow simulators are generally used to dimension the production scheme. One of the problems encountered is to predict accurately the pressure drop and the liquid holdup. This can be solved using an accurate numerical scheme and an appropriate thermodynamic behavior linked to an accurate and robust hydrodynamic model. In the Tacite Code, developed by IFP, a mechanistic hydrodynamic model has been developed. This model is able to predict the flow regime, the phase velocities and the local pressure drop for all slopes and all diameters. It contains closure laws based on flow regimes. This mechanistic model has been validated against various data banks. The two limitations of such an hydrodynamic model may be its mathematical disturbance (continuity, derivability are not always guaranteed) and the time consuming. This can be troublesome when using an accurate numerical scheme that requires derivative computation and for real time purposes. This paper presents a neural network based approach to efficiently replace the hydrodynamic module in the two phase model with the following two objectives: - to avoid discontinuity problems during hydrodynamic computations;- to reduce significantly computational time. This method was tested with experimental and simulated data. The results given in this paper prove the relevancy of this approach.