Automatic diagnosis method for wellhead pressure curve of hydraulic fracturing in shale gas horizontal well

Patent metadata

Field	Value
Publication number	US-10689972-B1
Application number	US-202016752651-A
Country	US
Kind code	B1
Filing date	Jan 26, 2020
Priority date	May 31, 2019
Publication date	Jun 23, 2020
Grant date	Jun 23, 2020

Abstract

Dynamic segmentation model to divide bottom hole net pressure curve into different stages based on slope of curve includes: step S1, establish calculation model of bottom hole net pressure: according to wellhead pressure during hydraulic fracturing in shale gas reservoir, calculate bottom hole net pressure based on fluid dynamics theory; step S2, establishing dynamic segmentation model to divide bottom hole net pressure curve into different stages based on slope of curve by numerical analysis theory; step S3, establish recognition model to recognize extension behavior of underground fracture network based on rock mechanics and fracture mechanics; and step S4, combine step S1, S2, and S3 to realize automatic diagnosis for wellhead pressure curve of hydraulic fracturing in shale gas horizontal well. A diagnosis and analysis method of the wellhead pressure curve of hydraulic fracturing in shale gas horizontal well is described.

First claim

What is claimed is:

1. An automatic diagnosis method for wellhead pressure curve of hydraulic fracturing in shale gas horizontal well, includes steps of:
step S 1 , establishing a conversion model for bottom hole net pressure of shale gas fracture network using fluid dynamics theory, comprising steps of:
sub-step A: collecting data, comprising: wellbore structure parameters, wellbore parameters, perforation parameters, fracturing construction parameters, fracturing fluid parameters, proppant parameters, in-situ stress conditions, natural fracture parameters, and bedding fracture parameters;
sub-step B: using the data in step A to calculate a fluid flow stress drop, a perforation stress drop, a static stress of the sand-carrying liquid column and a bottom hole net pressure, and drawing a bottom hole net pressure curve;

step S 2 , establishing a dynamic fitting model of bottom hole net pressure using numerical analysis theory, comprising steps of:
sub-step a: establishing a bottom hole net pressure data sequence p 1 , p 2 , p 3 3, . . . , p N and a corresponding time data sequence t 1 , t 2 , t 3 3, . . . , t N , and extracting the bottom hole net pressure data sequence and the corresponding time data sequence from 1 to N in turn for calculation;
sub-step b: setting the current data in the bottom hole net pressure data sequence and the time data sequence as reference pressure data and reference time data, respectively, for assigning a reference number r to the current number;
sub-step c: taking a next bottom hole net pressure data and a next time data as the current data, and setting the current number to i;
sub-step d: calculating a current bottom hole net pressure index fit value and a slope fit value, a bottom hole current net pressure index average and a slope average, and a bottom hole current net pressure index fit relative error value;
sub-step e: if the current bottom hole net pressure index fit relative error value >10%, returning to sub-step b to set the current bottom hole net pressure and the current time to the reference bottom hole net pressure and the reference time for reassigning the reference number r to the current number i; if the current bottom hole net pressure index fit relative error value ≤10%, returning to sub-step c to continue the calculation of next data;
sub-step f: when all the data has been calculated, and i=N, drawing a bottom hole net pressure index average curve;

step S 3 , establishing a fracture extension mode recognition model using fracture extension theory, wherein the fracture extension modes comprises fracture network extension mode, extension resistance mode, normal extension mode, bedding network extension mode, Fracture height extension mode, and rapid filtration mode, and each mode corresponds to different pressure curve feature recognition equations, the step of establishing a fracture extension mode recognition model comprising steps of:
sub-step I: using the bottom hole net pressure and the net pressure index average data sequence to determine whether the fracture extension modes are in turn the fracture network extension mode, the extension resistance mode, the normal extension mode, the bedding network extension mode, the Fracture height extension mode, and the rapid filtration mode, and determining the next data point if the data satisfies one of the modes;
sub-step II: when all the data points have been treated, stopping the calculation, and outputting a fracture extension mode recognition curve; and

step S 4 , combining steps S 1 , S 2 , and S 3 to realize automatic diagnosis for construction stress curve of shale gas fracture network through numerical calculation processes to recognize hydraulic fracture propagation behavior and guiding changes in on-site operation adjustment by assessing occurrence of propagation restraining, proppant blockage and fracturing leakoff risk, wherein the numerical calculation process of the step S 4 comprising steps of:
sub-step S 901 , inputting parameters in the step S 1 : the wellbore structure parameters, the wellbore parameters, the perforation parameters, the fracturing construction parameters, the fracturing fluid parameters, the proppant parameters, the in-situ stress conditions, the natural fracture parameters, and the bedding fracture parameters;
sub-step S 902 , calculating the fluid flow stress drop, the perforation stress drop, and the static stress of the sand-carrying liquid column in the step S 1 ; and converting a fracturing construction wellhead pressure into the bottom hole net pressure;
sub-step S 903 , establishing the bottom hole net pressure data sequence and the time data sequence in the step S 2 ; performing dynamic fitting of the bottom hole net pressure and calculating the bottom hole net pressure index average;
sub-step S 904 , automatically recognizing the fracture extension mode corresponding to each stage based on the bottom hole net pressure and the bottom hole net pressure index average in the step S 3 ;
sub-step S 905 , outputting data: the bottom hole net pressure, the bottom hole net pressure index average, and the fracture extension mode; and
sub-step S 906 , drawing an image: the bottom hole net pressure curve, the bottom hole net pressure index average curve, the fracture extension mode recognition curve, and a diagnosis diagram of construction pressure curve.

2. The automatic diagnosis method for wellhead pressure curve of hydraulic fracturing in shale gas horizontal well according to claim 1 , wherein the wellbore parameters include wellbore length vertical depth; wellbore diameter, and absolute roughness of wellbore wall; the perforation parameters include perforation number, perforation diameter, and perforation flow coefficient; the fracturing parameters include wellhead pressure, pump rate, and proppant ratio; the fracturing fluid parameters include viscosity and density; the proppant parameter is a proppant density; the geo-stress conditions include minimum horizontal stress, maximum horizontal stress, vertical stress, and stress difference between reservoir layer and adjacent layers; the natural fracture parameters include approach angle, dip angle, cohesion, friction coefficient, and tensile strength; the bedding fracture parameter is tensile strength.

3. The automatic diagnosis method for wellhead pressure curve of hydraulic fracturing in shale gas horizontal well according to claim 1 , wherein the fluid flow stress drop in wellbore is calculated using the following formula:

Δ
⁢

p

w
⁢
f

=

λ
⁢

L
D

⁢

v
2

⁢

ρ
1

2

;

(
1
)

Assignees

• Univ Southwest Petroleum

Inventors

• Zhao Jinzhou
• Ren Lan
• Lin Ran
• Li Kuidong
• Li Yuanzhao

Classifications

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• E21B43/26 Primary

  by forming crevices or fractures · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• E21B49/00

  Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells · CPC title

• E21B47/06 Primary

  Measuring temperature or pressure · CPC title