# Browsing by Subject "Oil and gas drilling"

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Item Development of real-time early gas kick detection model(2013-08) Ojinnaka, Marcellinus Azuka; Beaman, Joseph J.; Fish, ScottShow more Gas kicks occur during oil and gas drilling operations due to pressure imbalances between reservoir pressure and bottomhole pressure. Uncontrolled gas kick results in blowouts which has severe consequences including death of rig personnel. For deepwater, High Temperature High Pressure, and depleted wells, early gas kick detection may mean the difference between a successful drilling operation and a catastrophic drilling operation. Modeling the physics of gas kicks is therefore an important aspect of well control in order to detect kicks and raise appropriate alarms that demand remedial action from the rig team. Also important is the quantification of the amount of kick already in the annulus and an estimation of the kick front, all in real time. Various kick models have been developed over the years to model wellbore-reservoir interactions and aid early detection of gas kicks. Some of these models and simulators are numerical and analytical; others are based on extensive collection of well data of kick events to model drilling events signatures including kicks of various sizes. In general, for non-data driven models, the accuracy of models depends on the amount of simplification done and the validity of the assumptions made. Steady state, semi-steady state and transient models exist, but if accurate detection is to occur in real-time, it is crucial that transient models are used, that the assumptions are valid, and that oversimplification is avoided in order to reflect as closely as possible, the complex physics of wellbore-reservoir interactions. The important issues to consider include the type of fluid property model used, such as compositional or black oil models; the type of frictional model used, such as Power law or Bingham plastic model; the flow regime considered; slip velocity between the phases, and the extent to which first principles are applied to problem solving, as opposed to using correlations. Our study is on real-time estimation of gas kicks during drilling using a two-phase, fully implicit, transient flow model in a vertical wellbore. The wellbore and reservoir are coupled, and a pressure gradient is introduced at the bottomhole causing gas influx into the wellbore. The gas front is then monitored in real-time as it is transported in the circulating mud to the surface pits. The model equations are the mud and gas continuity equations, the momentum conservation equation as well as sub-models, consisting of state equations and two-phase flow correlations, where needed. Much of the complex physics of gas kick is modeled, and the outcome of this research provides a tool for gas kick prediction, detection and control, and also for the estimation of the volume of kick occurring at the bottomhole in real-time.Show more Item Direct spatiotemporal interpolation of reservoir flow responses(2006-05) Srinivasan, Shekhar; Srinivasan, SanjayShow more The traditional reservoir modeling workflow consists of first developing a reservoir model, performing flow simulation on that model, verifying the model by performing history matching and finally using the history matched model to make predictions of future performance. In contrast, this research focused on two approaches to directly analyze the spatio-temporal variations of dynamic responses such as pressure and well flowrates and perform interpolation. Both these techniques are anchored to the data at the wells. Therefore, the resultant spatio-temporal predictions of dynamic response are history matched by construction. Interpolation or extrapolation of dynamic response to locations away from wells is possible using both the approaches. Therefore, the proposed approaches can be used to quickly determine optimal location to drill additional wells and to gauge the influence of reservoir management decisions. In the first approach, dynamic responses such as pressure transients are treated as time series data. They are analyzed using wavelets that facilitate multiscale decomposition of pressure signals. Using a wavelet-lifting scheme, the transient signal is decomposed into averages and residuals. The corresponding filter coefficients defining the wavelets are treated as spatial random variables and estimated using geostatistics at locations away from wells. A pressure response is reconstructed at unsampled locations by employing the inverse wavelet transform. In an alternate approach, direct spatiotemporal extrapolation of pressure is performed. The transient pressure data at the wells are first analyzed using correlation measures such as semivariograms. For simplicity, time is taken as another spatial dimension and semivariogram values corresponding to the resultant lag-vectors are inferred and subsequently modeled. Spatiotemporal extrapolation is then performed to obtain the response at any location in space and at any instant in time. The robustness of both these approaches is verified on several case examples.Show more