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CN-117145450-B - Physical-data collaborative driving unconventional gas well yield prediction method and system

CN117145450BCN 117145450 BCN117145450 BCN 117145450BCN-117145450-B

Abstract

The invention discloses a physical-data collaborative driving unconventional gas well yield prediction method and system, wherein the method comprises the following steps of S1, obtaining production data of a target unconventional gas well, and preprocessing the production data; the method comprises the steps of S2, establishing an objective function with the minimum accumulated yield error as a target based on a dynamic drainage area calculation model and a material balance equation of a coupling dynamic drainage area, S3, carrying out history fitting to obtain model parameters, S4, calculating average pressure of the dynamic drainage area at different moments, S5, calculating gas production indexes corresponding to gas wells at different moments, S6, drawing a gas production index-material balance pseudo-time curve and obtaining a characteristic straight line, S7, obtaining fitting parameters according to the slope and the inclined distance of the characteristic straight line, S8, calculating the gas production index corresponding to tj at any moment, and carrying out yield prediction based on a recurrence principle. The invention realizes the prediction of the yield of the unconventional gas well driven by physical-data cooperation and can provide technical support for the development of the unconventional gas well.

Inventors

  • REN WENXI
  • Duan Youjing
  • GUO JIANCHUN
  • ZENG XIAOJUN

Assignees

  • 西南石油大学

Dates

Publication Date
20260512
Application Date
20230831

Claims (6)

  1. 1. A method for predicting production of an unconventional gas well driven by physical-data cooperation, comprising the steps of: S1, acquiring production data of a target unconventional gas well, and preprocessing the production data to obtain preprocessed production data, wherein the production data comprises daily yield q g , accumulated yield G p and bottom hole flow pressure p wf ; S2, establishing an objective function with the minimum accumulated yield error as a target based on a dynamic drainage area calculation model and a material balance equation of a coupled dynamic drainage area according to the preprocessed production data; the dynamic drainage area calculation model is as follows: (1) wherein: For the pore volume of the dynamic drainage zone, m 3 /m 3 ;q g is daily yield, 10 4 m 3 /d; The method comprises the steps of simulating pressure corresponding to reservoir pressure p i before gas well production, and MPa; For the average pressure of the dynamic drainage area The corresponding pseudo-pressure, MPa, C ti as the comprehensive compression coefficient under the initial condition, 1/MPa, t a as the material balance pseudo-time, d; Wherein the pseudo pressure The calculation is performed by the following formula: (2) wherein: is pseudo pressure, MPa, mu i is methane viscosity under initial conditions, pa.s, Z i is methane compression factor under initial conditions, p i is reservoir pressure before gas well production, MPa, p is reservoir pressure, MPa; Is pressure integral variable, MPa, mu is methane viscosity, pa.s, Z is methane compression factor, and dimensionless; The material balance pseudo-time t a is calculated by the following formula: (3) wherein: C t is the comprehensive compression coefficient, MPa -1 ; D is a time integral variable; The integrated compression coefficient c t is calculated by the following formula: (4) (5) (6) (7) Wherein c g is methane compression coefficient of 1/MPa, c f is stratum compression coefficient of 1/MPa; is the porosity of the reservoir, dimensionless; ρ r is rock density, t/m 3 ;V L is Langmuir volume, m 3 /t;p L is Langmuir pressure, MPa, TOC is organic carbon content, and dimensionless; The material balance equation of the coupling dynamic drainage area is as follows: (16) (17) (18) wherein: for cumulative yield, 10 4 m 3 ;B g is the average pressure in the dynamic drain Corresponding gas volume coefficient, dimensionless; The pressure is the average pressure of the dynamic drainage area, MPa, T is the temperature, K, p sc is the pressure corresponding to the ground standard state, MPa, T sc is the temperature corresponding to the ground standard state, K; the average pressure of the dynamic drainage area The calculation is performed by the following formula: (19) Wherein a 1 is a fitting parameter; s3, performing history fitting according to the objective function to obtain model parameters, wherein the model parameters comprise reservoir pressure p i , stratum compression coefficient c f and fitting parameter a 1 before gas well production; s4, according to the model parameters, based on the average pressure of the dynamic drainage area Calculating the average pressure of the dynamic drainage area at different moments according to the calculation formula of (1); S5, calculating gas production indexes J corresponding to gas wells at different moments according to the average pressure of the dynamic drainage areas at different moments and combining the bottom hole flow pressure p wf , wherein the gas production indexes J are calculated according to the following formula: (21) S6, drawing a gas production index-material balance pseudo-time curve in a double-logarithmic coordinate system according to gas production indexes J corresponding to gas wells at different moments, and obtaining a characteristic straight line of the gas production index-material balance pseudo-time curve; s7, obtaining a fitting parameter a 2 and a fitting parameter b 2 according to the slope and the slope distance of the characteristic straight line; S8, calculating a gas production index corresponding to any time t j according to the fitting parameter a 2 and the fitting parameter b 2 And carrying out yield prediction based on a recurrence principle; gas production index corresponding to any time t j The calculation is performed by the following formula: (22) wherein: And d, simulating time for the material balance corresponding to any time t j .
  2. 2. The method for predicting production of a physical-data co-driven unconventional gas well of claim 1 wherein the methane compression factor Z is calculated by: (8) (9) (10) Wherein T r is apparent contrast temperature, dimensionless, p r is apparent contrast pressure, p c is methane critical pressure, MPa, T is temperature, K, and T c is methane critical temperature, K.
  3. 3. The method for predicting production of a physical-data co-driven unconventional gas well of claim 1, wherein the methane viscosity μ is calculated by: (11) (12) (13) (14) (15) wherein: 、 、 Are all intermediate parameters, ρ is methane density, kg/M 3 , M is methane molar mass, mol/kg, T is temperature, K, R is ideal gas constant, J/mol/K.
  4. 4. The method for predicting production of a physical-data co-driven unconventional gas well of claim 1, wherein in step S2, the objective function is: (20) Where obj is the objective function, n is the sample size of the cumulative yield data, G p (t j ) is the calculated cumulative yield at time t j , 10 4 m 3 ;G pr (t j ) is the actual cumulative yield at time t j , 10 4 m 3 .
  5. 5. The method for predicting production of a physical-data co-driven unconventional gas well according to claim 1, wherein step S8 specifically comprises the sub-steps of: S81, calculating a gas production index corresponding to the current moment according to the fitting parameter a 2 and the fitting parameter b 2 ; s82, adopting explicit time dispersion, and predicting the gas production rate at the next time t j+1 by using the gas production index corresponding to the current time: (23) s83, calculating the accumulated output corresponding to the next time t j+1 according to the gas production amount of the next time t j+1 : (24) s84, calculating the average pressure of the dynamic drain region corresponding to the next time t j+1 according to the accumulated output corresponding to the next time t j+1 : (25) S85, calculating a material balance simulation time corresponding to the next time t j+1 according to the average pressure of the dynamic drain region corresponding to the next time t j+1 : S86, calculating a gas production index corresponding to the next time t j+1 according to the material balance simulation time corresponding to the next time t j+1 : (26) And S87, repeating the steps S81-S86 to obtain the yield prediction result of the target unconventional gas well.
  6. 6. A physical-data co-driven unconventional gas well production prediction system, comprising: The system comprises an acquisition module, a preprocessing module and a processing module, wherein the acquisition module is used for acquiring production data of a target unconventional gas well and preprocessing the production data, wherein the production data comprises daily yield q g , accumulated yield G p and bottom hole flow pressure p wf ; A fitting parameter obtaining module, configured to process the preprocessed production data, and obtain a fitting parameter a 2 and a fitting parameter b 2 by adopting steps S2-S7 in the physical-data collaborative driving unconventional gas well yield prediction method according to any one of claims 1-5; The yield prediction module is used for calculating a gas production index corresponding to any time t j according to the fitting parameter a 2 and the fitting parameter b 2 And carrying out yield prediction by combining a recurrence principle; gas production index corresponding to any time t j The calculation is performed by the following formula: (22) wherein: And d, simulating time for the material balance corresponding to any time t j .

Description

Physical-data collaborative driving unconventional gas well yield prediction method and system Technical Field The invention relates to the technical field of oil and gas reservoir exploitation, in particular to a physical-data collaborative driving unconventional gas well yield prediction method and system. Background Accurate prediction of production of unconventional gas wells is the basis for developing and adjusting completion strategies and development schemes. The unconventional gas reservoir generally develops micro-nano pores, has the characteristic of ultralow permeability, and can realize scale benefit development only by adopting a multi-stage fracturing and horizontal well technology. In addition to free gas, adsorbed gas may also be present in the unconventional gas reservoir nanopores. In addition, the gas seepage mechanism in the micro-nano pore is complex, besides the conventional viscous flow, the gas seepage mechanism also comprises multiple flow modes such as Knudsen diffusion and surface diffusion, and the like, so that a great challenge is provided for the conventional seepage theory, and the difficulty in accurately predicting the yield of an unconventional gas well is great. In addition, multi-section fracturing not only can generate artificial cracks, but also can activate natural cracks, and two types of cracks can interweave into a complex crack network, so that the difficulty in accurately predicting the yield of an unconventional gas well is further increased. Currently, unconventional gas well production prediction methods can be roughly divided into four types, namely an experience decremental model, a transient flow analysis, a numerical simulation and an artificial intelligence model, and the advantages and disadvantages of the various methods are as follows: (1) Experience-decreasing model the experience-decreasing model is a model based on experience/semi-experience equations, and has no definite physical meaning per se. It generally determines model parameters by fitting historical yield data and predicts future yields by extrapolation. The experience decremental model only needs historical output data, does not need additional data, has low cost and convenient calculation, and can realize quick prediction of future output. But the empirical decay model relies solely on production data for analysis and generally assumes that the well production regime is unchanged. The production gas well is limited or frequently shut in due to the influence of factors such as adjacent well fracturing, ground pipeline construction and the like, and the production system is complex. Thus, the empirical reduction model is difficult to adapt to unconventional gas wells with complex production regimes. (2) Transient flow analysis the analytical model and the semi-analytical model form the basis for transient flow analysis (Rate-TRANSIENT ANALYSIS). Transient flow analysis is a practical reservoir parameter inversion and production prediction method, similar to well test analysis, both of which are established based on seepage theory. However, well testing analysis investigated the response of gas well pressure, while transient flow analysis investigated the change in gas well production. The biggest characteristics of transient flow analysis are high calculation efficiency, but the method has the defects that 1) part of models do not consider non-reconstruction areas between adjacent hydraulic cracks, 2) the model has strong polynomiality, 3) the cracks are too simplified, such as flat-plate cracks, and 4) the shapes of drainage areas are required to be manually assumed. (3) Numerical simulation the numerical simulation method is the only method for quantitatively describing the natural gas flow law in the heterogeneous reservoir. The numerical simulation method can comprehensively utilize data information in various fields such as geology, oil and gas reservoirs, indoor experiments, well logging and the like. The method is characterized in that the method is carried out on grids obtained through geological modeling, a seepage equation and a material balance equation are further solved, and finally, the seepage rule of underground natural gas and the yield change of a well are obtained. The factors considered by the numerical simulation are comprehensive, but the modeling and calculation are time-consuming. In addition, the numerical simulation requires more input data, the data quality requirement is high, and errors of the input data can cause serious errors. For unconventional gas well numerical modeling, accurate production prediction results are obtained, and it is also desirable to 1) accurately characterize the location and distribution of the subsurface fracture network, and 2) accurately simulate the gas seepage process within and between the matrix, the fracture. However, no mature method for accurately identifying and characterizing the underground fracture network exists at presen