A pragmatic approach for optimizing gas lift operations
Tóm tắt
The oil flow rate in a single vertical well undergoing gas lift operations is complicated by three factors: (1) The flow is driven by gas injection, in addition to the fluid flow potential gradient applied along the well, (2) the well is interfaced with a porous and permeable reservoir contributing with a fluid feed, and (3) the wellbore geometry may consist of concentric pipes of varying diameters and lengths, rather than a single-diameter pipe. Dimensional analysis is applied to this complex, highly nonlinear production problem, in order to develop empirical models for predicting the optimal gas injection rate and the maximum oil production rate that may be produced from continuous gas lift operations. Two pairs of coupled dimensionless groups are revealed. The first pair consists of a dimensionless pressure drop (π1) adjusted to the complex wellbore geometry, and a dimensionless ratio of kinetic to viscous forces (π2) which accounts for the porous medium feed. A constructed database for 388 vertical wells producing by continuous gas lift operations has been used to validate the dimensionless groups. A power-law relation is revealed between the dimensionless groups π1 and π2, allowing to construct an analytical model for predicting the maximum oil production rate that corresponds to the optimal gas injection rate. The second pair consists of two groups denoted χ1 and χ2. The group χ1 is a dimensionless pressure drop with adjustment being augmented to account for the temperature effects on gas flow. Similar to π2, the dimensionless group χ2 is a ratio of kinetic to viscous forces, adjusted to account for the porous medium feed. However, χ2 is a function of the injection rate, instead of the oil production rate. Likewise, a power-law relation is revealed between χ1 and χ2, allowing to construct an analytical model for predicting the optimal gas injection rate. All power-law relations yield high correlation coefficients when the validation data are segregated according to a discrete productivity index. The analytical models developed by applying dimensional analysis appear to capture the physical controls of gas lift operations. Intuitively, the optimal gas injection rate depends on the pressure gradient along the pipe, the wellbore geometry, the temperature conditions at the bottom of the well and in the stock-tank, the oil density, and on the productivity index. Similarly, the maximum oil production rate, corresponding to the optimal gas injection rate, depends on the pressure gradient along the pipe, the wellbore geometry, the oil density, the productivity index which is implicitly affected by the oil permeability, and viscosity. Unlike multivariate nonlinear regression analysis, the application of dimensional analysis for deriving the analytical models, presented in this study, does not require a presumed functional relationship. In retrospect, dimensional analysis evades the guessing process associated with nonlinear regression analysis.
Tài liệu tham khảo
Al-Dousari MM, Garrouch AA (2013) An artificial neural network model for predicting the recovery performance of surfactant polymer floods. J Petrol Sci Eng 109:51–62
Al-Dousari MM, Garrouch AA, Al-Omair O (2016) Investigating the dependence of shear wave velocity on petrophysical parameters. J Petrol Sci Eng 146:286–296
Al-Omair O, Garrouch AA (2015) A general regression model offers reliable prediction of CO2 minimum miscibility. J Petrol Explor Prod Technol 6(3):351–365
Alsarraf Z (2019) Optimizing gas lift operations using dimensional analysis and general regression neural network. M.S. Thesis, Kuwait University, Kuwait
Antanasijevic D, Pocajt V, Ristic M, Peric-Grujic A (2015) Modeling of energy consumption and related GHC (Greenhouse Gas) intensity and emissions in Europe using general regression neural networks. Energy 84:816–824
Bedrikovetsky PG, Gladstone PM, Lopes RP Jr, Rosario FF, Silva MF, Bezerra MC, Lima EA (2003) Oilfield scaling—Part II: productivity index theory. Paper No. SPE 81128-MS. In: SPE Latin American and Caribbean petroleum engineering conference, Port-of-Spain, Trinidad, West Indies
Beggs HD (2003) Production optimization using nodal analysis. OGCI and Petroskills Publication, Tulsa, OK
Behjoomanesh M, Keyhani M, Ganzi-azad E, Izadmehr M, Riahi S (2015) Assessment of total oil production in gas-lift process of wells using Box-Behnken design of experiments in comparison with traditional approach. J Nat Gas Sci Eng 27:1455–1461
BenAmara A (2016) Gas lift—past & future. Paper No. SPE 184221-MS. In: SPE Middle East Artificial lift conference and exhibition, Manama, Bahrain
Camponogara E (2005) Solving a gas-lift optimization problem by dynamic programming. Eur J Operat Res 174:1220–1246
Camponogara E, Plucenio A, Teixeira AF, Campos SRV (2010) An automation system for gas-lifted oil wells model identification, control, and optimization. J Petrol Sci Eng 70:157–167
Chia YC, Hussain S (1999) Gas lift optimization efforts and challenges. Paper No. SPE 57313-M. In: SPE Asia Pacific improved oil recovery conference, Kuala Lumpur, Malaysia
Dai J, Liu X, Zhang S, Zhang H, Xu Q, Chen W, Zheng X (2010) Continuous neural decoding method based on general regression neural network. Int J Digit Content Technol Appl 4:1–6
de Souza JNM, de Medeiros JL, Costa ALH, Nunes GC (2010) Modeling, simulation and optimization of continuous gas lift systems for deepwater offshore petroleum production. J Petrol Sci Eng 72:277–289
Djikpesse HA, Couet B (2010) Gas lift optimization under facilities constraints. Paper No. SPE 136977-MS. In: 34th annual SPE international conference and exhibition, Tinapa-Calabar, Nigeria
Fang WY, Lo KKA (1996) Generalized well-management scheme for reservoir simulation. SPE Reserv Eng 11:116–120
Garrouch AA (2018) Predicting the cation exchange capacity of reservoir rocks from complex dielectric permittivity measurements. Geophysics 83(1):1–14
Garrouch AA, Al-Sultan AA (2019) Exploring the link between the flow zone indicator and key open-hole log measurements: an application of dimensional analysis. Petrol Geosci 25:1–16
Garrouch AA, Smaoui N (1996) Application of artificial neural network for estimating tight gas sand intrinsic permeability. Energy Fuel 10(5):1053–1059
Gutierrez F, Hallquist A, Shippen M, Rashid K (2007) A new approach to gas lift optimization using an integrated asset model. Paper No. IPTC-11594-MS. In: International petroleum technology conference, Dubai, UAE
Huang Z, Williamson MA (1994) Geological pattern recognition and modelling with a general regression neural network. Can J Explor Geophys 30:60–68
Khabibullin R, Burtzev Y (2015) New approach for gas lift optimization calculations. Paper No. SPE 176668-MS. In: SPE Russian petroleum technology conference, Moscow, Russia
Khamehchi E, Rashidi F, Rasouli H (2009a) Prediction of gas lift parameters using artificial neural network. Enhanc Oil Recover Iran Chem Eng J 8(43):179–186
Khamehchi E, Rashidi F, Omranpour H, Ghidary SS, Ebrahimian A, Rasouli H (2009b) Intelligent system for continuous gas lift operation and design with unlimited gas supply. J Appl Sci 9:1889–1897
Lu Q, Fleming GC (2011) Gas-lift optimization using proxy functions in reservoir simulation. Paper No. SPE 140935-MS. In: SPE reservoir simulation symposium, Woodland, Texas
Mahdiani MR, Khamehchi E (2015) Stabilizing gas lift optimization with different amounts of available lift gas. J Nat Gas Sci Eng 26:18–27
Miresmaeili SOH, Pourafshary P, Farahani FJ (2015) A novel multi-objective estimation of distribution algorithm for solving gas lift allocation problem. J Nat Gas Sci Eng 23:272–280
Munson BR, Young DF, Okiishi TH, Huebsch WW (2010) Fundamentals of fluid mechanics, 6th edn. Wiley, London
Nishikiori N, Redner RA, Doty DR, Schmidt Z (1998) An improved method for gas Lift allocation optimization. Paper No. SPE 19711-MS. In: SPE 64th annual technical conference and exhibition, San Antonio, TX
Oglesby KD, Mehdizadeh P, Rodger GJ (2006) Portable multiphase production tester for high-water-cut wells. Paper No. SPE 103087-MS. In: SPE annual technical conference and exhibition. San Antonio, Texas
Ojukwu KI, Edwards J (2008) Reliability of multiphase flowmeters and test separators at high water cut. Paper No. SPE 114127-MS. In: SPE Western Regional and Pacific Section AAPG joint meeting, Bakersfield, California
Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076
Ranjan A, Verma S, Singh Y (2015) Gas lift optimization using artificial neural network. Paper No. SPE 172610-MS. In: SPE middle east oil & gas show and conference, Manama, Bahrain
Ray T, Sarkar R (2007) Genetic algorithm for solving a gas lift optimization problem. J Petrol Sci Eng 59:84–96
Redden JD, Sherman TAG, Blann JR (1974) Optimizing gas lift systems. Paper No. SPE 5150-MS. In: Annual fall meeting of the society of petroleum engineers of AIME, Houston, Texas
Sarabia IG, Fairuzov YV (2013) Linear and non-linear analysis of flow instability in gas-lift wells. J Petrol Sci Eng 108:162–171
Shao W, Boiko I, Al-Durra A (2016) Control-oriented modeling of gas-lift system and analysis of casing-heading instability. J Nat Gas Sci Eng 29:365–381
Smaoui N, Garrouch AA (1997) A new approach combining Karhunen-Loeve decomposition and artificial neural network for estimating tight gas sand permeability. J Petrol Sci Eng 18(1/2):101–112
Specht D (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576
Sutton RP (2008) An accurate method for determining oil PVT properties using the Standing-Katz gas z-factor chart. SPE Reserv Eval Eng 11(2):246–266
Zendehboudi S, Chatzis I, Mohsenipour AS, Elkamel A (2011) Dimensional analysis and scale-up of immiscible two-phase flow displacement in fractured porous media under controlled gravity drainage. Energy Fuels 25(4):1731–1750