Numerical simulation of optimization of volume fracturing parameters for horizontal wells in tight oil

ABSTRACT

A comprehensive workflow for optimizing the fracturing parameters of horizontal wells in tight oil reservoirs is demonstrated. A numerical simulation model with geologic characterization data from Cangdong Sag Basin reservoirs was constructed. Geometrical parameters of the fractures were calculated through a volumetric model of the fracture network. The fracture lengths, heights, and network radius were optimized using dynamic local grid refinement. The equivalent stimulated reservoir volume considering fracture propagation was then simulated. Based on the simulation results of the mechanism model, after considering factors such as interfracture interference, fracturing operation duration, cost, and construction safety, the following optimal ranges of fracturing parameters are recommended: number of fracturing fractures = 8–11 levels, half-length of fracturing fractures = 130–160 m, and conductivity of fracturing fractures = 14–20 D∙cm. Based on the actual situation of the work area, it is recommended that the number of fracturing fractures be 9, the half-length of the fracture be 150 m, and the fracture conductivity be 18 D∙cm to achieve higher horizontal well production and provide a certain data reference for the efficient development of similar dense oil reservoirs.

KEYWORDS:

• Tight reservoir
• fracture propagation
• volume fracturing
• numerical simulation
• Optimization simulation

Highlight

• This method effectively reduces the uncertainty of the numerical simulation process, enhances the accuracy of the numerical simulation process, and can effectively optimize the reasonable fracturing stages.

• Geometrical parameters of the fractures were calculated through volumetric model of the fracture network.

• The dynamic Local Grid Refinement (LGR) is used to set the fracture length, fracture height and fracture mesh radius.

Symbolic notes

qD	=	flow rate of filling main fracture, m3/min;
t	=	construction time, min;
VD	=	volume of main fracture, m3;
n	=	flow regime index of power-rate fluid, dimensionless;
$\mathrm{\Phi }\left(n\right)$	=	shape factor, approximate value 3π/16, dimensionless;
hf	=	height of main fracture, m;
K	=	the consistency coefficient of power rate fluid, Pa·sn;
wm	=	width of main fracture, mm;
ν	=	poisson’s ratio of reservoir, dimensionless;
E	=	elastic modulus of reservoir, MPa;
pf	=	fluid pressure of main fracture, MPa;
σmin	=	the minimum horizontal principal stress, MPa;
KI	=	stress intensity factor, MPa·m1/2;
l	=	half-height of fracture, m;
KIC	=	fracture toughness of fractures, MPa·m1/2;
Nx、Ny	=	number of secondary fracture perpendicular to X and Y axes;
a	=	half-length of fracture, m;
b	=	elliptical short-axis half-length, m;
γ	=	elliptical aspect ratio, fraction;
Lxs、Lys	=	total length of secondary fracture perpendicular to X and Y axes, m;
Lxis	=	length of secondary fracture perpendicular to section i of axis X, m;
Lyjs	=	the length of secondary fracture perpendicular to section j of Y axis, m;
hsxi	=	height of secondary fracture perpendicular to section i of axis X, m;
hsyj	=	the height of secondary fracture perpendicular to section j of Y axis, m;
dij	=	distance between two secondary fracture in unit i and j, m;
$\bar{h}$	=	average height of fractures, m;
${w_m}^{\prime }$	=	main fracture width considering induced stress, mm;
$\mathrm{\Delta }{\sigma }^{\prime }$	=	inter-fracture interference stress, MPa;
λx、λy	=	ratio of secondary fracture width to main fracture width perpendicular to X and Y axes;
wxs、wys	=	secondary fracture width perpendicular to X and Y axes, mm;
As	=	wall area of secondary fracture, m2;
Vs	=	volume of secondary fracture, m3;
Vl	=	total filtration loss of fracture network fracturing fluid, m3;
A	=	total area of fracture network, sum of main fracture and secondary fracture area, m2;
c	=	fracturing fluid leak-off factor;
τ	=	the starting time of fracturing fluid filtration loss, min;
Vsp	=	fracturing fluid loss volume in main fractures, m3;
Sp	=	spurt loss coefficient, m3/m2;
q	=	displacement volume of fracturing construction, m3/min;

Acknowledgements

This work was supported by the China Postdoctoral Science Foundation (No. M2019650965), Major R & D Plan of Sichuan Province (No.2020YFQ0034).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation (No.M2019650965), Major R & D Plan of Sichuan Province (No.2020YFQ0034).

Notes on contributors

Ping Yue

Ping Yue received his Ph. D. degree in Oil and Gas Field Development Engineering from Southwest Petroleum University in 2013 and post-doctoral degree from China University of Petroleum (Beijing). He is mainly engaged in research and teaching of oil and gas reservoir engineering and numerical simulation. His research fields include seepage theory of complex structural Wells such as horizontal Wells and branch horizontal Wells in edge and bottom water reservoirs, three-dimensional fine geological modeling and integrated application of numerical simulation of conventional and unconventional oil and gas reservoirs.

Simin Qu

Simin Qu received her undergraduate degree in oil and gas Engineering from Southwest Petroleum University in 2021. She is currently studying for a master's degree. Her research interests include numerical simulation of reservoirs and complex flows in pores.

Wensheng Xu

Wensheng Xu received his PhD in petroleum Engineering in 2022. He is now the chief geological officer of Zepu oil and gas production Management Area, Tarim.

Mutong Wang

Mutong Wang received his undergraduate degree in 2020. He is now engaged in Zepu oil and gas production Management Area, Tarim.

Zhifan Yang

Zhifan Yang received an undergraduate degree in Oil and Gas Engineering in 2018 and a Master's degree in Oil and Gas Field development in 2021 at Southwest Petroleum University and currently works in reservoir management.

James J. Sheng

James J. Sheng received the B.Sc. degree in Petroleum Engineering from China University of Petroleum (East China) in 1983, respectively, M.Sc. and the Ph.D. degrees in the Petroleum Engineering, University of Alberta, Canada, in 1992 and 1996. Since 2011, he has been a professor at Texas Tech University. His research interests include Carbon Capture, Utilization and Storage, Enhanced oil recovery.

Xing Zhang

Xing Zhang received the B.Sc., M.Sc. and Ph.D. degrees in Petroleum engineering from China University of Petroleum (Beijing),in 2008, 2010 and 2014. Since 2016, he has been an associate Professor at the Karamay Campus of China University of Petroleum. His research interests include shale pore structure characteristics and shale frackability, CCUS.