Blowout Simulation

Overview

The blowout simulation is employed to estimate the potential blowout rates from reservoirs through designated wellbores to the surface. The surface may be the rig or a platform or it may be the seabed.

Model Overview and Application

Area of Application and Limitations

Oliasoft's blowout simulator offers decision support both for environmental risk management in well planning and as basis for worst case discharge for kill evaluations. The simulator takes into account most phases of conventional drilling and the most common flow paths. Oliasoft WellDesign™ offers a simulator that is user friendly, intuitive and fast allowing the user to perform multiple simulations and a range of sensitivities in order to optimize the well design both from a production and well control point of view. There are however some important limitations on the range of applicability which the user should be aware of. These are listed below.

• The flow simulator assumes steady-state, i.e. fluid properties do not change over time. Reservoir depletion is not taken into consideration.
• For two-phase reservoirs, a mixture of oil and gas is available. Combinations of oil or gas with water are currently not available, nor are three- phase reservoirs.
• Flow paths handled are release to Surface/Subsea for Drill string/Annulus/Open hole/Casing. Blowout through the external casing annulus is not handled.
• Cross flow between reservoirs is currently not handled.
• Pressure losses due to acceleration is neglected

Within these restrictions the tool enables analyzing well specific blowout consequences and providing detailed results for the different flow scenarios. The analysis results are given for both oil and gas, and the simulation engine handles multiple reservoirs and multiple fluids in comingled production.

Model Structure

The blowout engine contains two main models:

• Blowout flow rate model, which is based on the following three sub-models
• PVT model
  • Inflow model
  • Outflow model
• Blowout weighted rate model

The models take input from the user operating the tool.
The output from a blowout analysis includes:

• Blowout Flow Rates. Blowout flow rates are computed for all defined scenarios, for both oil and gas. As the model is steady-state, only impact of well kill mechanisms will influence flow rates over time.
• Weighted Blowout Rates. The weighted rates are computed based on probabilities assigned for a range of different scenarios including three flow paths, fully open and partly closed BOP and various reservoir penetration cases.

Model Overview and Application

Area of Application and Limitations

Oliasoft's blowout simulator offers decision support both for environmental risk management in well planning and as basis for worst case discharge for kill evaluations. The simulator takes into account most phases of conventional drilling and the most common flow paths. Oliasoft WellDesign offers a simulator that is user friendly, intuitive and fast allowing the user to perform multiple simulations and a range of sensitivities in order to optimize the well design both from a production and well control point of view. There are however some important limitations on the range of applicability which the user should be aware of. These are listed below.

• The flow simulator assumes steady-state, i.e. fluid properties do not change over time. Reservoir depletion is not taken into consideration.
• For two-phase reservoirs, a mixture of oil and gas is available. Combinations of oil or gas with water are currently not available, nor are three- phase reservoirs.
• Flow paths handled are release to Surface/Subsea for Drill string/Annulus/Open hole/Casing. Blowout through the external casing annulus is not handled.
• Cross flow between reservoirs is currently not handled.
• Pressure losses due to acceleration is neglected

Within these restrictions the tool enables analyzing well specific blowout consequences and providing detailed results for the different flow scenarios. The analysis results are given for both oil and gas, and the simulation engine handles multiple reservoirs and multiple fluids in comingled production.

Model Structure

The blowout engine contains two main models:

• Blowout flow rate model, which is based on the following three sub-models
  • PVT model
  • Inflow model
  • Outflow model
• Blowout weighted rate model

The models take input from the user operating the tool. The output from a blowout analysis includes:

• Blowout Flow Rates. Blowout flow rates are computed for all defined scenarios, for both oil and gas. As the model is steady-state, only impact of well kill mechanisms will influence flow rates over time.
• Weighted Blowout Rates. The weighted rates are computed based on probabilities assigned for a range of different scenarios including three flow paths, fully open and partly closed BOP and various reservoir penetration cases.

Modelling Principles and Input Assessment

The simulated blowout rates are used for two purposes. The weighted rates are used, predominantly in the Norwegian sector, as input for drift calculations for environmental studies, and are normally only run for oil fluids. The user can apply NORSOK recommended probabilities as default or alter the probabilities.

Single blowout rates are used for maximum discharge evaluations and as basis for dynamic kill simulations.

Uncertainties may be assigned to reservoir parameters and Monte Carlo simulations may be run to obtain a probability span of blowout rates.

Probability Assessment

Probability distributions are used as the measure of uncertainty related to reservoir characteristics and the consequences of a blowout. Probability assessment related to scenarios, kill mechanisms and well information is a main activity in the blowout analysis process. Hence, personnel involved in the analysis process or in decision making related to well planning and design must be familiar with how the probability figures shall be interpreted in this context. Due to field-to-field and well-to-well variations in factors like water depth, lithology, pressure regimes, equipment configurations and drilling procedures the drilling of each well can be considered as a unique process. Consequently, the amount of relevant experience data suitable for supporting probability assessments related to killing a well is scarce. However, by consulting system experts including geologists, mud engineers, drilling managers and other personnel with operational experience, uncertainty can be expressed at a level of detail where system information exists. In order to establish an optimal basis for decision making and planning, the aim is to reach a maximum share of the available information in the analysis, including both the available relevant historical data and expert judgments.

The Blowout Analysis Process

A blowout analysis process should be performed in accordance with the following steps:

1. Assessment of input data
2. Blowout analysis
3. Evaluation of results and decision making related to implementation of candidate measures for consequence reduction

The steps for analysis process are described below.

Input Data Assessment

Assessment of input data is a crucial and considerable part of the blowout analysis process. Some of the input required may be found from relevant documentation related to the drilling operation, e.g. the drilling program. Probabilities and probability distributions, are assigned on the basis of subjective considerations in combination with available experience data. Experience from pilot studies has shown that work meetings involving personnel from various disciplines help to stimulate constructive discussions and ensure that relevant conditions are included in the considerations in a consistent manner.

Blowout Analysis

After assessing the input parameters required for the blowout analysis, the overall analysis can be performed. The main results from the Blowout simulations include:

• Maximum discharge rate
• Weighted oil rate to surface/seabed

Evaluation of Results

The effect of risk-reducing measures, such as equipment modifications, change of operational routines or increased information of downhole parameters can be represented by proper adjustment of the model input. Re-analyses with adjusted input provide a basis for ranking and selection of candidate measures.

Model Input

General Input Types

The blowout simulation engine requires input on many parameters.

Input Categories

The different input parameters are divided into the following categories:

• Settings Reference information such as rig type, water depth, air gap
• Formation data Pore pressure, fracture gradient, temperature gradien
• Trajectory
• Well bore schematic Casing design, open hole size
• Drill pipe and bottom hole assembly
• Reservoir. Characteristics of the reservoir including:
  • Permeability
  • Pressure and temperature
  • Net pay
  • Permeability
  • Fluid
• Blowout settings Flow path, release point, source reservoir

Input

The reservoir module can be populated manually from scratch, as an individual module separate from the rest of the application, or it can be populated after the data for the formation module, trajectory module, wellbore schematics module and drill pipe module have been input. In that case, the reservoir module will pick correct data from these modules. This input data can always be manually overwritten.

Inflow Performance Relationship

Six different inflow models are available, three for oil and three for gas. These are Oil (combined Darcy-Vogel), Gas deliverability (Forchheimer). In addition, the model allows for user defined PI (productivity index) and user defined IPR curve for both oil and gas. (IPR) [1]. The IPR curve is the relation between the flowing bottom-hole pressure $P_{wf}$ and liquid production rate $q$. Undersaturated oil reservoirs exist as single-phase reservoirs where pressures are above the bubble point pressure. The linear IPR model is given as,

$$q = J (P_{res} - P_{wf})$$

where $J$ is the productivity index to describe the trends of the IPR curve and $P_{res}$ represents the reservoir pressure. The solution gas escapes from the oil and becomes free gas when the flowing bottom-hole pressure $P_{wf}$ is below the bubble point pressure $P_b$ [1]. Therefore, Vogel established an empirical equation for two-phase reservoirs in 1968 [2], and it is still widely used in the industry. If the reservoir pressure $P_{res} < P_b$ ,

$$q = q_{max} (1 - 0.2 \frac{P_{wf}}{P_{res}} - 0.8 \frac{P_{wf}}{P_{res}}^2)$$

and,

$$q_{max} = \frac{JP_{res}}{1.8}$$

Otherwise the following is being used,

$$q = J (P_{res} - P_b) + \frac{JP_{b}}{1.8} (1 - 0.2 \frac{P_{wf}}{P_{b}} - 0.8 \frac{P_{wf}}{P_{b}}^2)$$

As the productivity index $J$ is an unknown variable, it can be estimated according to different flow types.

Oil

Productivity index for vertical and deviated wells in rectangular drainage areas with constant pressure boundaries.

$$J = \frac{kh}{18.7 B_o \mu_o (0.5 \log \frac{2.2 x_e y_e}{c_a r_w^2} + S ) }$$

Gas Deliverability

Transient productivity index (i.e. productivity index of a well which has not yet seen any of the boundaries (radial flow) is used in this part. Most DST/WFT fall into this category) which can be used to determine the infinite-acting period.

$$J = \frac{kh}{21.5 B_g \mu_g (\log \frac{kt}{\textsf{poro} \mu_g c r_w^2} - 3.1 + 0.87S ) }$$

Forcheimer's Model

High velocity flow in porous media and fractures is modeled by the Forchheimer equation in gas reservoir when the reservoir pressure exceeds a cut-off value numerically.

$$P_{wf}= P_{res} - a \cdot q - b \cdot q^2$$

Forchheimer equation can be performed for the gas systems, where the nonlinear flow is much more significant, due to the lower gas viscosity which will give high numbers for the same velocity as in liquid systems.

Vertical Lift Performance Relationship

The Vertical lift performance (VLP), known as the outflow model, describes the relationship between the bottom-hole pressure and the flow rate.

The Drift-flux model

The classical drift-flux model for two-phase gas-liquid pipe flow describes the slip between the gas and liquid phases as the combined effect of non-uniform distribution of gas and liquid across the pipe cross section and the additional effect of gas buoyancy as well as local hydraulic gradients near the tip of a Taylor bubble.

The main assumption in the model is that these two effects are additive:

UG=C0UM+Ud

Where UG is the gas velocity and UM the mixture velocity given by the sum of the gas and liquid superficial velocities

UM=USG+USL

The distribution coefficient C0 and the drift velocity Ud are often taken to be flow regime dependent, since the physical phenomena they reflect vary widely with the spatial distribution of gas and liquid.

The classical-type drift-flux model of Bhagwat and Ghajar (2014) has been employed for two-phase gas-liquid flow, since this model has been developed to cover all pipe or well inclinations and thus seems to be the most general and up to data drift-flux model available. We have then added the three-phase corrections from Shi et al (2003) on top of the basic two-phase gas-liquid drift-flux model.

One challenge with the classical drift-flux model is that it is invalid as the gas volume fraction approaches unity. Our solution is to replace the classical drift-flux model by an alternative model. In the classical drift-flux model, the gas velocity is expressed as a linear function of the mixture velocity. In the alternative model, the gas velocity is expressed as a linear function of the liquid velocity, allowing for a greater flexibility of the gas and liquid velocities to adjust to each other under extreme conditions like for example very high gas volume fractions.

The classical drift-flux model is very well-established and tested for liquid dominated flows, i.e. slug and bubbly flow. The classical drift-flux model is kept more or less unchanged for liquid dominated flow. The new approach is to employ the new drift-flux model for gas dominated flow and use an interpolation between the two models for intermediate gas volume fractions. From experience we have defined gas dominated flow as multiphase flow with an input gas volume fraction of more than 90%. This may sound like a very strict definition, but dynamically it makes sense since a GVF of 90% corresponds to a gas mass fraction of 50% at a gas density of 90 kg/m3.

For the steady state case, we normally assume that the gas and liquid superficial velocities USG and USL are known and that the void fraction αG is unknown. In this case we can compute the unknown void fraction and phase velocities from the standard drift-flux model. For the dynamic or transient case, we normally assume that the mixture velocity UM and the void fraction αG are known.

For the three-phase case, we first use the two-phase model to compute the gas and liquid velocities assuming known gas and liquid volume fractions and mixture velocity (transient case). Since we only will be using the three-phase model for dynamic kill simulations, we only need to consider the transient case here. We can thus assume that the gas, oil and water holdups are known from the previous time step or iteration.

PVT Model

The pressure-volume-temperature (PVT) handling of fluids in many fluid flow simulations describes the phase behavior of gas, oil, and water at different conditions. A mixture with known composition consists of defined number of phases, phase amounts, phase compositions, phase properties (molecular weight, density, and viscosity), and the interfacial tension between phases. In addition, it is important to define the phase behavior of mixtures at a specific pressure and acquire the derivatives of all phase properties corresponding to pressure and composition.

In the reservoir module of Oliasoft WellDesign, the user has three options to describe the fluid to be analysed in the blowout simulations:

• PVT tab files uploaded from a PVT simulator
• PVT files custom made in the Oliasoft PVT engine
• Black oil

References

[1] Nilsen, T.: Retningslinjer for beregning av utblåsningsrater og -varighet til bruk ved analyse av miljørisiko, OLF/Statoil, Stavanger, Norway, 2004
[2] Andersen, L. B., Aven, T., Nilsen, T.: KickRisk - Stochastic Modelling for The Quantification of Kick and Blowout Risk in Exploration Drilling - Guidelines for assessing subjective probabilities, Report RT - 199/229. RF - Rogaland Research, Stavanger, Norway, 1998
[3] Berg, A., Fosse, F. and Nævdal, G.: Production Monitoring - Specification Document Report RF - 2001/323, RF - Rogaland Research, Stavanger/Bergen, Norway, 2002
[4] Lage, A. C. V. M.: Two-phase Flow Models and Experiments for Low-Head and Underbalanced Drilling, PhD thesis at University of Stavanger, Norway, 2000
[5] Rommetveit, R.: A numerical solution model for gas kicks in oil based drilling fluids, Dr. Scient thesis at University of Bergen, Norway, 1998
[6] Masella, J. M., Faille, I. and Gallouet, T.: On an approximate Godunov scheme, Int. J. Computational Fluid Dynamics, 1999
[7] Gavaga, S. B.: Analyse numérique des modèles hydrodynamiques d'écoulements diphasiques instationnaires dans les réseaux de production pétrolière. Thèse, ENS Lyon, France, 1991
[8] Inglis, T. A.: Petroleum engineer and development studies, Vol. 2: Directional drilling, Graham & Trotman Limited, UK, 1987
[9] ALAdwani, F. A.: Application of mechanistic models in predicting flow behavior in deviated wells under UBD conditions, M.Sc. Thesis at Louisiana State University and Agricultural and Mechanical College, USA, 2003.
[10] Colebrook, C. F.: Turbulent flow in pipes, with particular reference to the transition region between smooth and rough pipe laws, Journal of the Institution of Civil Engineers, London, UK. 1939.
[11] Lyons, W. C. and Plisga, G. J.: Standard handbook of Petroleum & Natural gas engineering (2nd Edition), Elsevier, UK, 2005
[12] J. V. Vogel. Inflow performance relationship for solution gas drive wells. Journal of Petroleum Technology, pages 83-93, 1968
[14] Boyun, G., Lyons, W. C. and Ghalambor, A.: Petroleum Production Engineering - A Computer-Assisted Approach, Elsevier Science & Technology Books, February 2007
[15] Statoil: Excel DST design_all in one V2_Rev4.xls, 08.08.2011
[16] Larsen, L.: Well Testing - Analysis of Pressure Transient Data, University of Stavanger, October 2010
[17] Larsen, L.: General Productivity Models for Wells in Homogeneous and Layered Reservoirs, SPE 71613,2001
[18] Beggs, H. D.: Production Optimization Using NodalTM Analysis, 2nd Edition, OGCI and Petroskills Publications, Tulsa, Oklahoma, USE, May 2003
[19] Vasquez, M. and Beggs, H. D.: Correlations for Fluid Physical Property Prediction, Journal of Petroleum Technology, June 1980, pp. 968-970
[20] Moradi, B. et.al.: Bubble Point Pressure Empirical Correlations, SPE 132759, June 2010
[21] Standing, M. B. Volumetric and Phase Behavior of Oil Field Hydrocarbon Systems, Society of Petroleum Engineers of AIME, Dallas, USA, 1977
[22] Sutton, R. P. and Farshad, F.: Evaluation of Empirically Derived PVT Properties for Gulf of Mexico Crude Oil, SPE Reservoir Engineering, February 1990, pp. 79-86
[23] De Ghetto, G., Paone, F. and Villa, M.: Reliability Analysis on PVT Correlations, SPE 28904, 1994
[24] De Ghetto, G., Paone, F. and Villa, M.: Pressure-Volume-Temperature Correlations for Heavy and Extra Heavy Oil, SPE 30316, 1995
[25] Beggs, H. D. and Robinson, J. R.: Estimating the Viscosity of Crude Oil Systems, Journal of Petroleum Technology, September 1975, pp. 1140-41
[26] Chew, J. and Connally, C. A. Jr.: A Viscosity Correlation for Gas-Saturated Crude Oils, AIME (1959) 2016, pp. 23-25
[27] Egbogah, E. O. and Ng, J. T.: An improved temperature-viscosity correlation for crude oil systems, Journal of Petroleum Science and Engineering, 5 (1990) 197-200
[28] Lake, L. W. et. al.: Petroleum Engineering Handbook, Vol. I, Society of Petroleum Engineers, 2006
[29] Muskat, M.: Physical Principles of Oil Production, Mc-Graw-Hill Book Co., Inc., New York, 1949.]
[30] Trube, A. S.: Compressibility of Undersaturated Hydrocarbon Reservoir Fluids, SPE 899-G, Vol. 210, 1957, pp. 341-344
[31] Standing, M. B. and Kratz, D. L.: Density of Natural Gases, Trans. American Institute of Mechanical Engineers, Vol. 146, p. 140-149, 1942
[32] Dranchuk, P. M., Purvis, R. A. and Robinson, D. B.: Computer Calculation of Natural Gas Compressibility Factors Using the Standing and Katz Correlation, Institute of Petroleum Technical Series, No. IP 74-008, 1974
[33] Ahmed, T.: Reservoir Engineering Handbook, Elsevier, 2010
[34] Shi, C.: Flow Behavior of Gas-Condensate Wells, Stanford University, March 2009
[35] Lee, A. L., Gonzalez, M. H. and Eakin, B. E.: The Viscosity of Natural Gases, Journal of Petroleum Technology, August 1966, pp. 997-1000
[36] Londono, F. E., Archer, R. A. and Blasingame, T. A.: Simplified Correlations for Hydrocarbon Gas Viscosity and Gas Density - Validation and Correlation of Behavior Using a Large-Scale Database, SPE 75721, 2002
[37] Hagedorn, A. R., and Brown, K. E.: Experimental Study of Pressure Gradients Occurring During Continuous Two-Phase Flow in Small Diameter Vertical Conduits, Journal of Petroleum Technology, April 1965
[38] Brown, K. E. and Beggs, H. D.: The Technology of Artificial Lift Methods, Vol. 1, Pennwell Books, 1977
[39] Griffith, P.: Two-Phase Flow in Pipes, Special Summer Program. Massachusetts Institute of Technology, Cambridge, Mass., 1962
[40] Maurer Engineering Inc.: Well Control Model (WELCON2) - Theory and User's Manual, Houston, Texas, February 1993
[41] Beggs, H. D. and Brill, J. P.: A Study of Two-Phase Flow in Inclined Pipes, Journal of Petroleum Technology, May 1973
[42] Beggs, H. D. and Brill, J. P.: Two-Phase Flow in Pipes, 6th Edition, Tulsa University Press, January 1991
[43] Orkiszewski, J.: Predicting Two-Phase Pressure Drops in Vertical Pipe, Journal of Petroleum Technology, June 1967
[44] Brill, J. P.: Discontinuities in the Orkiszewski Correlation for Predicting Pressure Gradients in Wells, Journal of Energy Resources Technology, Vol. 111/35, March 1989
[45] Chierici, G. L., Ciucci, G. M. and Sclocchi, G.: Two-Phase Vertical Flow in Oil Wells - Prediction of Pressure Drop, Journal of Petroleum Technology, August 1974, pp. 927-938
[46] Takács, G.: Gas Lift Manual, PennWell Books, July 2005
[47] Griffith, P. and Wallis, G. B.: Two-phase slug flow, Journal of Heat Transfer, Trans. ASME, August 1961, pp. 307-320
[48] Duns, H. Jr. and Ros, N. C. J.: Vertical Flow of Gas and Liquid Mixtures from Wells, Proc. Sixth World Petroleum Congress, Frankfurt, Section II, 22-PD 6, June 1963
[49] Gray, H. E.: Vertical Flow Correlation in Gas Wells, User Manual for API 14B, Subsurface Controlled Safety Valve Sizing Computer, 1978
[50] Scandpower. Blowout and well release frequencies - Based on SINTEF Offshore Blowout Database, 2008, 2009
[51] B. Guo, W. C. Lyons, and A. Ghalambor. Petroleum production engineering, a computer-assisted approach. Elseiver Science & Technology Books, 2007
[52] Shi, H., Holmes, J. A., Durlofsky, L. J., Aziz, K., Diaz, L. R., Alkaya, B. and Oddie, G. (2003): Drift-Flux Modeling of Multiphase Flow in Wellbores. SPE 84228
[53] Bhagwat, S. M. and Ghajar, A. J. (2014): A flow pattern independent drift flux model based on void fraction correlation for a wide range of gas-liquid two-phase flow. Int. J. of Multiphase Flow 59, pp. 186-205